@article {pmid40506484, year = {2025}, author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D}, title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {992}, pmid = {40506484}, issn = {2052-4463}, mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; }, abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.}, }
@article {pmid40505916, year = {2025}, author = {Bikiaris, RE and Matschek, NI and Koumentakou, I and Niti, A and Kyzas, GZ}, title = {Synergistic effects of arginine and tannic acid on chitosan matrices: An approach for hemostatic sponge development.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {145105}, doi = {10.1016/j.ijbiomac.2025.145105}, pmid = {40505916}, issn = {1879-0003}, abstract = {This study presents the development of a novel multifunctional hydrogel biocomposite sponge designed to address the complexities of wound healing, including rapid hemostasis, infection prevention, and tissue regeneration. Recognizing the limitations of conventional wound dressings that lack multifunctionality, this study introduces a 3D chitosan/tannic acid (CS/TA) hydrogel. After testing three chitosan/tannic acid (CS/TA) ratios, CS/TA-1 (1:0.16), CS/TA-2 (1:0.25), and CS/TA-3 (1:0.34), the most effective formulation, CS/TA-2, was enhanced with sodium alginate (SA) and arginine (Arg) for optimal performance. Arginine, with its guanidinium functional group, served as a green crosslinker through physical interactions, enhancing the sponge's mechanical strength while also improving its hemostatic performance and biocompatibility, promoting cellular interactions. Its inclusion significantly amplified antioxidant activity (>90 %), mitigating oxidative stress and contributing to enhanced therapeutic outcomes. Ionic crosslinking and freeze-drying created a porous, absorbent sponge with high water retention and compression resilience. SEM confirmed the sponge's interconnected porosity, enabling cell infiltration and nutrient exchange. Blood Clotting Index (BCI) assessments demonstrated the hemostatic effectiveness of CS/TA/SA/Arg-3, with 25 % BCI at 5 min and 20 % at 15 min, along with excellent hemocompatibility, achieving a 2.08 % hemolysis rate. These results suggest the hydrogel sponge's potential for effective wound management in emergencies and clinical applications.}, }
@article {pmid40503091, year = {2025}, author = {Li, Q and Pan, Y}, title = {Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf013}, pmid = {40503091}, issn = {2634-4416}, abstract = {Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards "naturalistic" approaches, with keywords such as "face-to-face" and "real-world" gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.}, }
@article {pmid40502712, year = {2025}, author = {Chen, H and Zhang, M and Ye, T and Wolpert, MA and Ding, N}, title = {Low-frequency cortical activity reflects context-dependent parsing of word sequences.}, journal = {iScience}, volume = {28}, number = {6}, pages = {112650}, pmid = {40502712}, issn = {2589-0042}, abstract = {During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context-the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.}, }
@article {pmid40501187, year = {2025}, author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y}, title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.}, journal = {Social neuroscience}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/17470919.2025.2517068}, pmid = {40501187}, issn = {1747-0927}, abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.}, }
@article {pmid40499369, year = {2025}, author = {Banovoth, RS and K V, K}, title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.}, journal = {Computers in biology and medicine}, volume = {194}, number = {}, pages = {110397}, doi = {10.1016/j.compbiomed.2025.110397}, pmid = {40499369}, issn = {1879-0534}, abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.}, }
@article {pmid40499342, year = {2025}, author = {Silveira, I and Varandas, R and Gamboa, H}, title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.}, journal = {Computer methods and programs in biomedicine}, volume = {269}, number = {}, pages = {108863}, doi = {10.1016/j.cmpb.2025.108863}, pmid = {40499342}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.<br><br>METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.<br><br>RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.<br><br>CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.}, }
@article {pmid40498623, year = {2025}, author = {Lian, Q and Wang, Y and Qi, Y}, title = {Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3578627}, pmid = {40498623}, issn = {2168-2208}, abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.}, }
@article {pmid40496741, year = {2025}, author = {Tiwari, N and Anwar, S and Bhattacharjee, V}, title = {EEG dataset for natural image recognition through visual stimuli.}, journal = {Data in brief}, volume = {60}, number = {}, pages = {111639}, pmid = {40496741}, issn = {2352-3409}, abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.}, }
@article {pmid40496017, year = {2025}, author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ}, title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.}, journal = {Frontiers in cellular and infection microbiology}, volume = {15}, number = {}, pages = {1560700}, pmid = {40496017}, issn = {2235-2988}, mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; }, abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.<br><br>METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.<br><br>RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.<br><br>CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.}, }
@article {pmid40495523, year = {2025}, author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X}, title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.}, journal = {The American journal of psychiatry}, volume = {}, number = {}, pages = {appiajp20240475}, doi = {10.1176/appi.ajp.20240475}, pmid = {40495523}, issn = {1535-7228}, abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.<br><br>METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.<br><br>RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.<br><br>CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.}, }
@article {pmid40495436, year = {2025}, author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA}, title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.}, journal = {Brain and behavior}, volume = {15}, number = {6}, pages = {e70589}, doi = {10.1002/brb3.70589}, pmid = {40495436}, issn = {2162-3279}, support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; }, abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.<br><br>METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.<br><br>RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.<br><br>CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.}, }
@article {pmid40494420, year = {2025}, author = {Spinelli, R and Sanchís, I and Siano, A}, title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.}, journal = {Bioorganic &amp; medicinal chemistry letters}, volume = {}, number = {}, pages = {130305}, doi = {10.1016/j.bmcl.2025.130305}, pmid = {40494420}, issn = {1464-3405}, abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.}, }
@article {pmid40494387, year = {2025}, author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ}, title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ade344}, pmid = {40494387}, issn = {1741-2552}, abstract = {OBJECTIVE: Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.<br><br>APPROACH: The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.<br><br>RESULTS: The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.<br><br>SIGNIFICANCE: These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.}, }
@article {pmid40494367, year = {2025}, author = {Thielen, J}, title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/ade316}, pmid = {40494367}, issn = {2057-1976}, abstract = {This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency. Approach. In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied. Main Results. Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort. Significance. This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.}, }
@article {pmid40493465, year = {2025}, author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R}, title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3578235}, pmid = {40493465}, issn = {1558-2531}, abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.}, }
@article {pmid40493186, year = {2025}, author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J}, title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {24}, pages = {e2418920122}, doi = {10.1073/pnas.2418920122}, pmid = {40493186}, issn = {1091-6490}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; }, abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.}, }
@article {pmid40490658, year = {2025}, author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR}, title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.}, journal = {Behavior research methods}, volume = {57}, number = {7}, pages = {196}, pmid = {40490658}, issn = {1554-3528}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.}, }
@article {pmid40490007, year = {2025}, author = {Chen, Y and Peng, Y and Tang, J and Camilleri, TA and Camilleri, KP and Kong, W and Cichocki, A}, title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ade290}, pmid = {40490007}, issn = {1741-2552}, abstract = {As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e., depression, anxiety) are detected, which are considered as the two basic functions of aBCI system. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems. Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders. Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information','outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI. Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI system in practical deployment.}, }
@article {pmid40490003, year = {2025}, author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I}, title = {Speech imagery brain-computer interfaces: a systematic literature review.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ade28e}, pmid = {40490003}, issn = {1741-2552}, abstract = {Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines. \textit{Approach}. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode Speech Imagery from neural activity. \textit{Main results}. Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6\% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions. \textit{Significance} Speech Imagery is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.}, }
@article {pmid40489280, year = {2025}, author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z}, title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3577813}, pmid = {40489280}, issn = {2168-2208}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.}, }
@article {pmid40485770, year = {2025}, author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A}, title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1491494}, pmid = {40485770}, issn = {2296-9144}, abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.}, }
@article {pmid40398443, year = {2025}, author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T}, title = {CNNs improve decoding of selective attention to speech in cochlear implant users.}, journal = {Journal of neural engineering}, volume = {22}, number = {3}, pages = {}, doi = {10.1088/1741-2552/addb7b}, pmid = {40398443}, issn = {1741-2552}, mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; }, abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.}, }
@article {pmid40484831, year = {2025}, author = {Zhao, JZ}, title = {[A historical review and future outlook of neurosurgery in China].}, journal = {Zhonghua yi xue za zhi}, volume = {105}, number = {21}, pages = {1679-1685}, doi = {10.3760/cma.j.cn112137-20250325-00727}, pmid = {40484831}, issn = {0376-2491}, mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; }, abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.}, }
@article {pmid40483841, year = {2025}, author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A}, title = {Supervised factor selection in tensor decomposition of EEG signal.}, journal = {Computer methods and programs in biomedicine}, volume = {269}, number = {}, pages = {108866}, doi = {10.1016/j.cmpb.2025.108866}, pmid = {40483841}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.<br><br>METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.<br><br>RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.<br><br>CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.}, }
@article {pmid40483616, year = {2025}, author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X}, title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-16}, doi = {10.1080/10255842.2025.2514132}, pmid = {40483616}, issn = {1476-8259}, abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.}, }
@article {pmid40482972, year = {2025}, author = {M V, H and K, K and B, SB}, title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {115652}, doi = {10.1016/j.bbr.2025.115652}, pmid = {40482972}, issn = {1872-7549}, abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.<br><br>NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.<br><br>RESULTS: The proposed model achieved average classification accuracies of 83.83% &#xb1; 5.94 and 64.58% &#xb1; 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.<br><br>CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.}, }
@article {pmid40481499, year = {2025}, author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G}, title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.}, journal = {Journal of nanobiotechnology}, volume = {23}, number = {1}, pages = {422}, pmid = {40481499}, issn = {1477-3155}, support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.<br><br>RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.<br><br>CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.<br><br>GRAPHICAL ABSTRACT: [Image: see text]<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.}, }
@article {pmid40481295, year = {2025}, author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H}, title = {Expectation violation enhances short-term source memory.}, journal = {Psychonomic bulletin &amp; review}, volume = {}, number = {}, pages = {}, pmid = {40481295}, issn = {1531-5320}, abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.}, }
@article {pmid40481078, year = {2025}, author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X}, title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {19938}, pmid = {40481078}, issn = {2045-2322}, support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; }, mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; }, abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.}, }
@article {pmid40481044, year = {2025}, author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J}, title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {953}, pmid = {40481044}, issn = {2052-4463}, support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; }, abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.}, }
@article {pmid40480870, year = {2025}, author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V}, title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110502}, doi = {10.1016/j.jneumeth.2025.110502}, pmid = {40480870}, issn = {1872-678X}, }
@article {pmid40480308, year = {2025}, author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y}, title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.}, journal = {Experimental neurology}, volume = {}, number = {}, pages = {115330}, doi = {10.1016/j.expneurol.2025.115330}, pmid = {40480308}, issn = {1090-2430}, abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.<br><br>OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.<br><br>METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.<br><br>RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.<br><br>CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.}, }
@article {pmid40457516, year = {2025}, author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D}, title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {}, number = {}, pages = {e202505038}, doi = {10.1002/anie.202505038}, pmid = {40457516}, issn = {1521-3773}, support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; }, abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.}, }
@article {pmid40480249, year = {2025}, author = {Pritchard, M and Campelo, F and Goldingay, H}, title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ade1f9}, pmid = {40480249}, issn = {1741-2552}, abstract = {Objective: Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG &amp; EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings. Approach: We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection &amp; Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature. Main results: EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems. Significance: To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies, enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.}, }
@article {pmid40479831, year = {2025}, author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J}, title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-9}, doi = {10.3171/2025.2.JNS243025}, pmid = {40479831}, issn = {1933-0693}, abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.<br><br>METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.<br><br>RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.<br><br>CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.}, }
@article {pmid40478867, year = {2025}, author = {Jiang, M and Luo, Q and Wang, X and Tan, Y}, title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.}, journal = {PloS one}, volume = {20}, number = {6}, pages = {e0324848}, doi = {10.1371/journal.pone.0324848}, pmid = {40478867}, issn = {1932-6203}, mesh = {*Phonetics ; Humans ; Male ; *Language ; Semantics ; Female ; China ; Adult ; Animals ; Young Adult ; East Asian People ; }, abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.}, }
@article {pmid40478707, year = {2025}, author = {Li, H and Zhang, H and Chen, Y}, title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3577611}, pmid = {40478707}, issn = {2168-2208}, abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.}, }
@article {pmid40476694, year = {2025}, author = {Nazareth, G}, title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {}, number = {}, pages = {1-2}, doi = {10.1080/07434618.2025.2508491}, pmid = {40476694}, issn = {1477-3848}, abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 24. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.}, }
@article {pmid40475558, year = {2025}, author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD}, title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.05.15.653674}, pmid = {40475558}, issn = {2692-8205}, abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models- available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r <.35) and within-person emotional states (r =.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.}, }
@article {pmid40477046, year = {2020}, author = {Amoo-Adare, EA}, title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.}, journal = {Postdigital science and education}, volume = {2}, number = {3}, pages = {606-613}, pmid = {40477046}, issn = {2524-4868}, }
@article {pmid40472937, year = {2025}, author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ}, title = {Clonorchiasis in China: geospatial modelling of the population infected or at risk, based on national surveillance.}, journal = {The Journal of infection}, volume = {}, number = {}, pages = {106528}, doi = {10.1016/j.jinf.2025.106528}, pmid = {40472937}, issn = {1532-2742}, abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and chemotherapy need hinders the control.<br><br>METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 &#xd7; 5km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.<br><br>RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.<br><br>CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.<br><br>Environmental and socioeconomic data are open access (Table S1 in Supplementary Information). Epidemiological and behavioral data are not publicly available but are available on reasonable request after reviewed by the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research).}, }
@article {pmid40472336, year = {2025}, author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J}, title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.}, journal = {PloS one}, volume = {20}, number = {6}, pages = {e0319031}, pmid = {40472336}, issn = {1932-6203}, mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; }, abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.}, }
@article {pmid40471721, year = {2025}, author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G}, title = {Applying SSVEP BCI on Dynamic Background.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3576984}, pmid = {40471721}, issn = {1558-0210}, abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.}, }
@article {pmid40471491, year = {2025}, author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y}, title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40471491}, issn = {1741-0444}, support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; }, abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.}, }
@article {pmid40470749, year = {2025}, author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K}, title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e17408}, doi = {10.1002/advs.202417408}, pmid = {40470749}, issn = {2198-3844}, support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; }, abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.}, }
@article {pmid40469097, year = {2025}, author = {Wang, Z and Wang, Y}, title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1599960}, pmid = {40469097}, issn = {1662-5161}, abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.}, }
@article {pmid40469096, year = {2025}, author = {Li, P and Yu, D and Cheng, L and Wang, K}, title = {Influence of attentional state on EEG-based motor imagery of lower limb.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1545492}, pmid = {40469096}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).<br><br>METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.<br><br>RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r&#x202f;=&#x202f;0.9641, p&#x202f;<&#x202f;0.01). Cluster-based permutation testing confirmed that this &#x3b1;-band ERD difference was significant (corrected p&#x202f;<&#x202f;0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13)&#x202f;=&#x202f;5.12, p&#x202f;=&#x202f;2.4&#x202f;&#xd7;&#x202f;10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p&#x202f;<&#x202f;0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.<br><br>CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.}, }
@article {pmid40468342, year = {2025}, author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S}, title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.}, journal = {BMC medicine}, volume = {23}, number = {1}, pages = {334}, pmid = {40468342}, issn = {1741-7015}, support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; }, abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.<br><br>METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal&#xa0;anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3&#xa0;weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).<br><br>RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.<br><br>CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.}, }
@article {pmid40467567, year = {2025}, author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H}, title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {188}, pmid = {40467567}, issn = {2158-3188}, support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; }, abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.}, }
@article {pmid40465456, year = {2025}, author = {Li, C and Hasegawa, I and Tanigawa, H}, title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.}, journal = {STAR protocols}, volume = {6}, number = {2}, pages = {103870}, doi = {10.1016/j.xpro.2025.103870}, pmid = {40465456}, issn = {2666-1667}, abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].}, }
@article {pmid40463690, year = {2025}, author = {Rabiee, A and Ghafoori, S and Cetera, A and Abiri, R}, title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {40463690}, issn = {2331-8422}, abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.}, }
@article {pmid40462746, year = {2025}, author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J}, title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2025.2512877}, pmid = {40462746}, issn = {1476-8259}, abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.}, }
@article {pmid40031188, year = {2025}, author = {Yu, H and Zeng, F and Liu, D and Wang, J and Liu, J}, title = {Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {6}, pages = {4147-4160}, doi = {10.1109/JBHI.2025.3530922}, pmid = {40031188}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Acupuncture Therapy/methods ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; *Signal Processing, Computer-Assisted ; *Deep Learning ; Young Adult ; *Brain/physiology ; Neural Networks, Computer ; }, abstract = {Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92.42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.}, }
@article {pmid40461535, year = {2025}, author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H}, title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5132}, pmid = {40461535}, issn = {2041-1723}, support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; }, abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.}, }
@article {pmid40460359, year = {2025}, author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H}, title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.}, journal = {ACS nano}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsnano.5c03865}, pmid = {40460359}, issn = {1936-086X}, abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.}, }
@article {pmid40459463, year = {2025}, author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S}, title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.}, journal = {Annals of plastic surgery}, volume = {94}, number = {6S Suppl 4}, pages = {S572-S576}, doi = {10.1097/SAP.0000000000004273}, pmid = {40459463}, issn = {1536-3708}, mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention &amp; control ; *Amputation, Surgical/rehabilitation ; }, abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.}, }
@article {pmid40459258, year = {2025}, author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR}, title = {Air-liquid interface model for influenza aerosol exposure in vitro.}, journal = {Journal of virology}, volume = {}, number = {}, pages = {e0061925}, doi = {10.1128/jvi.00619-25}, pmid = {40459258}, issn = {1098-5514}, abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.<br><br>IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.}, }
@article {pmid40459142, year = {2025}, author = {Gao, J and Jiang, D and Wang, H and Wang, X}, title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.}, journal = {Journal of medicinal chemistry}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.jmedchem.5c00136}, pmid = {40459142}, issn = {1520-4804}, abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.}, }
@article {pmid40458259, year = {2025}, author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y}, title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {13}, number = {}, pages = {1591316}, pmid = {40458259}, issn = {2296-4185}, abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.<br><br>METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.<br><br>RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.<br><br>DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.}, }
@article {pmid40457127, year = {2025}, author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y}, title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40457127}, issn = {1741-0444}, support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; }, abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.}, }
@article {pmid40456926, year = {2025}, author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA}, title = {A brain-computer interface working definition.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {40456926}, issn = {2157-846X}, }
@article {pmid40456256, year = {2025}, author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J}, title = {Learning from Small Datasets - Review of Workshop 6 of the 10th International BCI Meeting 2023.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addf80}, pmid = {40456256}, issn = {1741-2552}, abstract = {In brain-computer interfacing (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models. Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions. At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets. We explored methodologies from both traditional machine learning as well as deep learning. In addition to talks and discussions, we introduced Python toolboxes for all presented methods and for the benchmarking of classification models. This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.}, }
@article {pmid40456243, year = {2025}, author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A}, title = {P300-based Brain-Computer Interface for communication in Assistive Technology centres: influence of users' profile on BCI access.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addf7f}, pmid = {40456243}, issn = {1741-2552}, abstract = {Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A Brain-Computer Interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-center to investigate their eligibility for BCI access and the factors influencing the BCI control. Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance. Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6±8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR. Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centers, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.}, }
@article {pmid40456242, year = {2025}, author = {Schmid, PR and Sweeney-Reed, CM and Dürschmid, S and Reichert, C}, title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addf81}, pmid = {40456242}, issn = {1741-2552}, abstract = {Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI. Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance. Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention. Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication. .}, }
@article {pmid40456241, year = {2025}, author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X}, title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addf82}, pmid = {40456241}, issn = {1741-2552}, abstract = {OBJECTIVE: Existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.<br><br>APPROACH: The system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis (TRCA) is employed for SSVEP signal identification.<br><br>MAIN RESULTS: The feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 &#xb1; 9.95% and an information transfer rate (ITR) of 66.74 &#xb1; 15.44 bits/min. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 &#xb1; 3.02% and an ITR of 51.88 &#xb1; 3.74 bits/min.<br><br>SIGNIFICANCE: The performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.}, }
@article {pmid40456131, year = {2025}, author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C}, title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.}, journal = {Journal of medical Internet research}, volume = {27}, number = {}, pages = {e78147}, doi = {10.2196/78147}, pmid = {40456131}, issn = {1438-8871}, abstract = {[This corrects the article DOI: 10.2196/71741.].}, }
@article {pmid40456094, year = {2025}, author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A}, title = {Acquisition delay of wireless EEG instruments in time-sensitive applications.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3575695}, pmid = {40456094}, issn = {1558-0210}, abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.}, }
@article {pmid40456080, year = {2025}, author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH}, title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3576088}, pmid = {40456080}, issn = {2168-2208}, abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results of 90.17% on SEED, 88.38% on PhysioNet, and 77.02% on the OpenBMI dataset in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.}, }
@article {pmid40455568, year = {2025}, author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R}, title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.5c03798}, pmid = {40455568}, issn = {1944-8252}, abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.}, }
@article {pmid40454682, year = {2025}, author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J}, title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {}, number = {}, pages = {1-9}, doi = {10.1080/07434618.2025.2495897}, pmid = {40454682}, issn = {1477-3848}, abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.}, }
@article {pmid40450930, year = {2025}, author = {Fan, C and Song, Y and Mao, X}, title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {190}, number = {}, pages = {107684}, doi = {10.1016/j.neunet.2025.107684}, pmid = {40450930}, issn = {1879-2782}, abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.}, }
@article {pmid40450863, year = {2025}, author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G}, title = {Extraction and analysis of abnormal EEG features in children with amblyopia.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {175}, number = {}, pages = {2110765}, doi = {10.1016/j.clinph.2025.2110765}, pmid = {40450863}, issn = {1872-8952}, abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.<br><br>METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.<br><br>RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.<br><br>CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.<br><br>SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.}, }
@article {pmid40450806, year = {2025}, author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA}, title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.}, journal = {Marine pollution bulletin}, volume = {218}, number = {}, pages = {118233}, doi = {10.1016/j.marpolbul.2025.118233}, pmid = {40450806}, issn = {1879-3363}, abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.}, }
@article {pmid40450046, year = {2025}, author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S}, title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {921}, pmid = {40450046}, issn = {2052-4463}, mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; }, abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.}, }
@article {pmid40448829, year = {2025}, author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L}, title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.}, journal = {Experimental brain research}, volume = {243}, number = {7}, pages = {160}, pmid = {40448829}, issn = {1432-1106}, support = {#21/05897-5//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #21/12790-2//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #20/08512-4//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; }, mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; }, abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.}, }
@article {pmid40448287, year = {2025}, author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G}, title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.}, journal = {Annals of the New York Academy of Sciences}, volume = {}, number = {}, pages = {}, doi = {10.1111/nyas.15380}, pmid = {40448287}, issn = {1749-6632}, support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; }, abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.}, }
@article {pmid40446349, year = {2025}, author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y}, title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-12}, doi = {10.3171/2025.2.JNS242655}, pmid = {40446349}, issn = {1933-0693}, abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.<br><br>METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.<br><br>RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).<br><br>CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.}, }
@article {pmid40446280, year = {2025}, author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z}, title = {Mechanics of Soft-Body Rolling Motion without External Torque.}, journal = {Physical review letters}, volume = {134}, number = {19}, pages = {198401}, doi = {10.1103/PhysRevLett.134.198401}, pmid = {40446280}, issn = {1079-7114}, mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; }, abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.}, }
@article {pmid40443843, year = {2025}, author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y}, title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1554266}, pmid = {40443843}, issn = {1662-5161}, abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.}, }
@article {pmid40442937, year = {2025}, author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X}, title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2416698}, doi = {10.1002/adma.202416698}, pmid = {40442937}, issn = {1521-4095}, support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&amp;D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; }, abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.}, }
@article {pmid40442546, year = {2025}, author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C}, title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.}, journal = {Cognitive, affective &amp; behavioral neuroscience}, volume = {}, number = {}, pages = {}, pmid = {40442546}, issn = {1531-135X}, support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; }, abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.}, }
@article {pmid40442206, year = {2025}, author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N}, title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18869}, pmid = {40442206}, issn = {2045-2322}, mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; }, abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.}, }
@article {pmid40442062, year = {2025}, author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM}, title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {5008}, pmid = {40442062}, issn = {2041-1723}, support = {Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; }, mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; }, abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.}, }
@article {pmid40441574, year = {2025}, author = {Rahman, MH and Mondal, MIH}, title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {144712}, doi = {10.1016/j.ijbiomac.2025.144712}, pmid = {40441574}, issn = {1879-0003}, abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.}, }
@article {pmid40440260, year = {2025}, author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X}, title = {A deep learning-based algorithm for the detection of personal protective equipment.}, journal = {PloS one}, volume = {20}, number = {5}, pages = {e0322115}, pmid = {40440260}, issn = {1932-6203}, mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; }, abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.}, }
@article {pmid40438784, year = {2025}, author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G}, title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.}, journal = {Frontiers in pediatrics}, volume = {13}, number = {}, pages = {1546001}, pmid = {40438784}, issn = {2296-2360}, abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).<br><br>OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.<br><br>METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40&#x2005;min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2&#x2005;h of one-on-one sessions and 0.5&#x2005;h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).<br><br>RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.<br><br>CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.}, }
@article {pmid40438090, year = {2025}, author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X}, title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {81}, pmid = {40438090}, issn = {1871-4080}, abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.}, }
@article {pmid40437332, year = {2025}, author = {Cherukuri, SB and Ramakrishnan, S}, title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.}, journal = {Physical and engineering sciences in medicine}, volume = {}, number = {}, pages = {}, pmid = {40437332}, issn = {2662-4737}, abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.}, }
@article {pmid40436265, year = {2025}, author = {Chopra, M and Kumar, H}, title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.}, journal = {Drug discovery today}, volume = {}, number = {}, pages = {104387}, doi = {10.1016/j.drudis.2025.104387}, pmid = {40436265}, issn = {1878-5832}, abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.}, }
@article {pmid40434889, year = {2025}, author = {Ruszala, BM and Schieber, MH}, title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.}, journal = {Cell reports}, volume = {44}, number = {5}, pages = {115664}, doi = {10.1016/j.celrep.2025.115664}, pmid = {40434889}, issn = {2211-1247}, abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.}, }
@article {pmid40434816, year = {2025}, author = {Liu, L}, title = {Did you see it?.}, journal = {eLife}, volume = {14}, number = {}, pages = {}, pmid = {40434816}, issn = {2050-084X}, mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; }, abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.}, }
@article {pmid40434551, year = {2025}, author = {Sokhadze, E}, title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {40434551}, issn = {1573-3270}, abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.}, }
@article {pmid40433677, year = {2025}, author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H}, title = {Graphene quantum dots induced performance enhancement in memristors.}, journal = {Nanoscale}, volume = {}, number = {}, pages = {}, doi = {10.1039/d5nr00597c}, pmid = {40433677}, issn = {2040-3372}, abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.}, }
@article {pmid40431969, year = {2025}, author = {You, Z and Guo, Y and Zhang, X and Zhao, Y}, title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, doi = {10.3390/s25103178}, pmid = {40431969}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.}, }
@article {pmid40431893, year = {2025}, author = {Sasatake, Y and Matsushita, K}, title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, doi = {10.3390/s25103102}, pmid = {40431893}, issn = {1424-8220}, support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; }, mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; }, abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.}, }
@article {pmid40431780, year = {2025}, author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM}, title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {10}, pages = {}, doi = {10.3390/s25102987}, pmid = {40431780}, issn = {1424-8220}, support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigaci&#xf3;n del Envejecimiento de la Fundaci&#xf3;n de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie/ ; }, mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; }, abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.}, }
@article {pmid40428890, year = {2025}, author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I}, title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.}, journal = {Medicina (Kaunas, Lithuania)}, volume = {61}, number = {5}, pages = {}, doi = {10.3390/medicina61050932}, pmid = {40428890}, issn = {1648-9144}, mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; }, abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.}, }
@article {pmid40428702, year = {2025}, author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H}, title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.}, journal = {Micromachines}, volume = {16}, number = {5}, pages = {}, doi = {10.3390/mi16050576}, pmid = {40428702}, issn = {2072-666X}, abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.}, }
@article {pmid40428683, year = {2025}, author = {Hong, S}, title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.}, journal = {Micromachines}, volume = {16}, number = {5}, pages = {}, doi = {10.3390/mi16050557}, pmid = {40428683}, issn = {2072-666X}, support = {N/A//Hongik University/ ; }, abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.}, }
@article {pmid40428114, year = {2025}, author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S}, title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {5}, pages = {}, doi = {10.3390/bioengineering12050495}, pmid = {40428114}, issn = {2306-5354}, abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.}, }
@article {pmid40426690, year = {2025}, author = {Taha, BN and Baykara, M and Alakuş, TB}, title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.}, journal = {Brain sciences}, volume = {15}, number = {5}, pages = {}, doi = {10.3390/brainsci15050519}, pmid = {40426690}, issn = {2076-3425}, abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.}, }
@article {pmid40426631, year = {2025}, author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J}, title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.}, journal = {Brain sciences}, volume = {15}, number = {5}, pages = {}, doi = {10.3390/brainsci15050460}, pmid = {40426631}, issn = {2076-3425}, support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; }, abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.}, }
@article {pmid40426214, year = {2025}, author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z}, title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {118}, pmid = {40426214}, issn = {1743-0003}, support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&amp;D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; }, mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; }, abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.}, }
@article {pmid40425805, year = {2025}, author = {Liu, CW and Wang, YM and Chen, SY and Lu, LY and Liang, TY and Fang, KC and Chen, P and Lee, IC and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK}, title = {The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {40425805}, issn = {2157-846X}, support = {NTUMC 110C101-011//NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University)/ ; NSC-145-11//National Taiwan University Hospital (NTUH)/ ; 113-UN0013//National Taiwan University Hospital (NTUH)/ ; 108-039//National Taiwan University Hospital (NTUH)/ ; 112-UN0024//National Taiwan University Hospital (NTUH)/ ; 113-E0001//National Taiwan University Hospital (NTUH)/ ; AS-TM-112-01-02//Academia Sinica/ ; NHRI-EX113-11303NI//National Health Research Institutes (NHRI)/ ; 109-2326-B-002-013-MY4//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 107-2321-B-002-020//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-002-059-MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 110-2321-B-002-012//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 111-2628-B-002-036//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 112-2628-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 113-2628-B-002-002//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; R01NS118179//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS104423//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS124854//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; }, abstract = {Understanding brain behaviour encoding or designing neuroprosthetics requires identifying precise, consistent neural algorithms across individuals. However, cerebral microstructures and activities are individually variable, posing challenges for identifying precise codes. Here, despite cerebral variability, we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in vivo electrophysiology and optogenetics in mice, we confirm that deep cerebellar neurons encode frequencies using populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism is consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validate the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for a brain-computer interface for motor control.}, }
@article {pmid40425792, year = {2025}, author = {Chen, ZP and Zhao, X and Wang, S and Cai, R and Liu, Q and Ye, H and Wang, MJ and Peng, SY and Xue, WX and Zhang, YX and Li, W and Tang, H and Huang, T and Zhang, Q and Li, L and Gao, L and Zhou, H and Hang, C and Zhu, JN and Li, X and Liu, X and Cong, Q and Yan, C}, title = {GABA-dependent microglial elimination of inhibitory synapses underlies neuronal hyperexcitability in epilepsy.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {40425792}, issn = {1546-1726}, support = {82373856//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900824//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071097//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471481//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200778//National Natural Science Foundation of China (National Science Foundation of China)/ ; 020813005031//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2019M651779//Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)/ ; }, abstract = {Neuronal hyperexcitability is a common pathophysiological feature of many neurological diseases. Neuron-glia interactions underlie this process but the detailed mechanisms remain unclear. Here, we reveal a critical role of microglia-mediated selective elimination of inhibitory synapses in driving neuronal hyperexcitability. In epileptic mice of both sexes, hyperactive inhibitory neurons directly activate surveilling microglia via GABAergic signaling. In response, these activated microglia preferentially phagocytose inhibitory synapses, disrupting the balance between excitatory and inhibitory synaptic transmission and amplifying network excitability. This feedback mechanism depends on both GABA-GABAB receptor-mediated microglial activation and complement C3-C3aR-mediated microglial engulfment of inhibitory synapses, as pharmacological or genetic blockage of both pathways effectively prevents inhibitory synapse loss and ameliorates seizure symptoms in mice. Additionally, putative cell-cell interaction analyses of brain tissues from males and females with temporal lobe epilepsy reveal that inhibitory neurons induce microglial phagocytic states and inhibitory synapse loss. Our findings demonstrate that inhibitory neurons can directly instruct microglial states to control inhibitory synaptic transmission through a feedback mechanism, leading to the development of neuronal hyperexcitability in temporal lobe epilepsy.}, }
@article {pmid40425030, year = {2025}, author = {Dehgan, A and Abdelhedi, H and Hadid, V and Rish, I and Jerbi, K}, title = {Artificial neural networks for magnetoencephalography: A review of an emerging field.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addd4a}, pmid = {40425030}, issn = {1741-2552}, abstract = {Objective: Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in Artificial Intelligence (AI) has led to the growing use of Machine Learning (ML) methods for MEG data classification. An emerging trend in this field is the use of Artificial Neural Networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach: This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: Classification, Modeling, and Other. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main Results:The review identified 119 relevant studies, with 69 focused on Classification, 16 on Modeling, and 34 in the Other category. Classification studies addressed tasks such as brain decoding, clinical diagnostics, and BCI implementations, often achieving high predictive accuracy. Modeling studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The Other category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance: By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.}, }
@article {pmid40425024, year = {2025}, author = {Wolpaw, JR}, title = {Making brain-computer interfaces as reliable as muscles.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addd47}, pmid = {40425024}, issn = {1741-2552}, abstract = {While BCIs can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e., muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable. A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills. A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the name heksor, from the ancient Greek word hexis. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in a negotiated equilibrium that enables each to produce its skill satisfactorily. A BCI-based skill is produced by a synthetic heksor, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features. A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its CNS and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.}, }
@article {pmid40425023, year = {2025}, author = {Wu, D}, title = {Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addd49}, pmid = {40425023}, issn = {1741-2552}, abstract = {Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.}, }
@article {pmid40424668, year = {2025}, author = {Sawyer, A and Brannigan, J and Spielman, L and ,  and Putrino, D and Fry, A}, title = {Development of a novel clinical outcome assessment: digital instrumental activities of daily living.}, journal = {EBioMedicine}, volume = {116}, number = {}, pages = {105732}, doi = {10.1016/j.ebiom.2025.105732}, pmid = {40424668}, issn = {2352-3964}, abstract = {BACKGROUND: Digital technology is integral to activities of daily living, particularly instrumental activities of daily living (IADLs). However, tools that accommodate digital performance of IADLs are lacking. The aim of this study was to develop a novel Digital IADL Scale.<br><br>METHODS: The multi-stage methodology included: (i) deductive item generation via a systematic review and assignment to domains using a Delphi process, (ii) inductive item generation via a survey of individuals with lived experience (IWLE) of severe paralysis, (iii) item refinement via item rating surveys of content experts and IWLE, and (iv) focus group discussions with key opinion leaders.<br><br>FINDINGS: The systematic review identified 1250 IADL items from validated IADL measures, of which 353 met criteria. Deduplication reduced the deductive item set to 77, of which 42 remained following the Delphi process. IWLE generated 152 items, of which 132 met criteria. Deduplication reduced the inductive item set to 41. The combined item pool was reduced to 69 following the item rating surveys. Following focus group feedback, a list of nine domains, containing 37 items, and suggested response scale options are presented.<br><br>INTERPRETATION: We describe the initial development of a scale to assess functional independence within IADLs that may be completed digitally, which will be submitted to further validation.<br><br>FUNDING: Support for this project was provided in kind by the Abilities Research Center. No formal funding was received.}, }
@article {pmid40423756, year = {2025}, author = {Moeller, A and Andres Porras, JM}, title = {Human enhancement, past and present.}, journal = {Monash bioethics review}, volume = {}, number = {}, pages = {}, pmid = {40423756}, issn = {1836-6716}, abstract = {One important role the medical humanities might and should play relates to public education. In this instance, we mean helping persons to think about their own aims or purposes as potential receivers of enhancement interventions, and similarly helping to inform the developers of said interventions. This article argues that, in the light of real and speculative applications of emerging biotechnologies and artificial intelligence aimed at human enhancement-including germline genetic engineering, the linking of the human brain with an artificial general intelligence by way of a brain-computer interface, and various interventions directed toward life extension-historians would do well to consider the following three practices as they participate in the medical humanities and the shared task of public education: (1) Taking under scrutiny a broad swath of topics and timeframes as it relates to past efforts aimed at human enhancement; (2) Focusing on past engagement with enhancement efforts and their perceived relation to the pursuit of living well; and (3) Entering into debates on enhancement as equal participants. In support of these assertions, this article takes efforts directed towards the prolongation of life in medieval Europe as an illustrative example. It also highlights continuities and discontinuities between past and present justifications for human enhancement, and addresses how similarities and differences can shape and challenge contemporary bioethical arguments.}, }
@article {pmid40423554, year = {2025}, author = {Brackman, KN and Taychert, MT and Serrell, EC and Gralnek, D and Manakas, C and Knoedler, M and Antar, A and Allen, GO and Grimes, MD}, title = {Clinical Outcomes of HoLEP in Patients with Diminished Bladder Contractility.}, journal = {Urology practice}, volume = {}, number = {}, pages = {101097UPJ0000000000000840}, doi = {10.1097/UPJ.0000000000000840}, pmid = {40423554}, issn = {2352-0787}, abstract = {INTRODUCTION: Bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) is common in aging men and can be treated with holmium laser enucleation of the prostate (HoLEP). However, diminished bladder contractility (DC) is also highly prevalent (9-48%) and can be clinically indistinguishable from BOO without urodynamics. While HoLEP effectively treats BPH/BOO, clinical outcomes data for DC patients are limited and mixed. We aim to compare the prevalence and risk factors for catheter dependence among patients with and without DC post-HoLEP.<br><br>METHODS: A retrospective cohort study was conducted on 179 patients with preoperative urodynamics who underwent HoLEP between June 2018 and December 2023. Diminished contractility was defined as Bladder Contractility Index (BCI) < 100. Statistical analyses included univariate and multivariate logistic regression.<br><br>RESULTS: Among 179 patients 103 (57.5%) had DC (BCI <100). Post HoLEP all normal contractility (NC) patients were voiding while 7.8% of DC patients were catheter dependent (p = 0.01) at mean follow up of 28 months. Preoperative BCI was associated with post HoLEP catheter dependence (OR = 0.97, 95% CI 0.95-1.00, p = 0.046). Postoperative international prostate symptom scores were significantly higher in DC compared to NC groups despite similar preoperative scores.<br><br>CONCLUSIONS: HoLEP rendered 95.5% (171/179) of patients catheter free. However, DC patients were more likely to require catheterization postoperatively and reported worse urinary symptoms compared to NC patients. Our results support obtaining urodynamics when there is clinical concern for DC, as this may guide shared decision-making prior to pursuing HoLEP.}, }
@article {pmid40422053, year = {2025}, author = {Avital, N and Shulkin, N and Malka, D}, title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.}, journal = {Biosensors}, volume = {15}, number = {5}, pages = {}, doi = {10.3390/bios15050314}, pmid = {40422053}, issn = {2079-6374}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; }, abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.}, }
@article {pmid40421845, year = {2025}, author = {Pizzolante, S and Covelli, E and Filippi, C and Barbara, M}, title = {Percutaneous Bone Implant Surgery: A MIPS Modified Technique.}, journal = {The Laryngoscope}, volume = {}, number = {}, pages = {}, doi = {10.1002/lary.32192}, pmid = {40421845}, issn = {1531-4995}, abstract = {Since their introduction, passive percutaneous hearing aids have undergone substantial evolution, including changes in implant production, improvements in the sound processor, and simplification of surgical implantation techniques. The latest innovation comes from the minimally invasive technique proposed for the PONTO system (MIPS), which does not involve the creation of a mucoperiosteal flap in order to leave the surrounding soft tissue and vascular microcirculation intact. This study proposes a modified surgical technique compared to the one proposed for the PONTO system in order to overcome some steps of the traditional surgical technique for the placement of the Baha Connect prosthesis. Our technique does not involve any incision but the exposure of the periosteum using a skin punch and subsequent drilling without the use of any protective cannula. The described procedure allows one to overcome some steps of the traditional surgical technique and, consequently, also some post-operative complications. Moreover, a minimally invasive procedure can help reduce surgical time and the invasiveness of the application.}, }
@article {pmid40420994, year = {2025}, author = {Esteves, D and Valente, M and Bendor, SE and Andrade, A and Vourvopoulos, A}, title = {Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1572851}, pmid = {40420994}, issn = {2673-6195}, abstract = {The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.}, }
@article {pmid40420178, year = {2025}, author = {Jiang, M and Luo, Q and Wang, X and Qu, D}, title = {Semantic radicals' semantic attachment to their composed phonograms.}, journal = {BMC psychology}, volume = {13}, number = {1}, pages = {559}, pmid = {40420178}, issn = {2050-7283}, support = {20BYY095//National Social Science Fund of China/ ; 2019YBYY131//Chongqing Social Science Planning Fund/ ; 22SKGH236//Humanities and Social Sciences Research Project Fund of Chongqing Municipal Education Commission/ ; }, mesh = {Humans ; *Semantics ; Female ; Male ; Young Adult ; Reaction Time ; Adult ; Decision Making ; *Reading ; }, abstract = {In Chinese character processing studies, it is widely accepted that semantic radicals, whether character or non-character ones, can undergo semantic activation. However, there is a notable absence of studies dedicated to understanding the nature and operation of the semantic radicals' semantic information. To address this gap, the present study employed a masked semantic priming paradigm combined with a part-of-speech decision task and a lexical decision task across three experiments. Experiment 1 was designed to examine the semantic autonomy and the semantic attachment of semantic radicals in transparent phonograms. Experiment 2 sought to further investigate the degree of semantic autonomy of semantic radicals in opaque phonograms. Experiment 3 was crafted to further probe into the presence of semantic attachment of semantic radicals in pseudo-characters. Results showed significant priming effects in both transparent and opaque phonogram conditions, with faster reaction times and higher accuracy for semantically related prime-target pairs. However, no such priming effect was observed in the pseudo-character condition, indicating that semantic radicals are not activated in non-lexical contexts. These findings suggest that semantic radicals were semantically activated when embedded in both transparent and opaque phonograms, but not when planted in pseudo-characters. The plausible account put forward is that semantic radicals stand on pars with their composed phonograms in possessing their own semantic information, but the former is semantically strongly attached to the latter, such that it cannot live without the latter's semantic company.}, }
@article {pmid40419791, year = {2025}, author = {Chen, Y and Ding, K and Zheng, S and Gao, S and Xu, X and Wu, H and Zhou, F and Wang, Y and Xu, J and Wang, C and Ling, C and Xu, J and Wang, L and Wu, Q and Giamas, G and Chen, G and Zhang, J and Yi, C and Ji, J}, title = {Post-translational modifications in DNA damage repair: mechanisms underlying temozolomide resistance in glioblastoma.}, journal = {Oncogene}, volume = {}, number = {}, pages = {}, pmid = {40419791}, issn = {1476-5594}, support = {82203035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82403931//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Temozolomide (TMZ) resistance is one of the critical factors contributing to the poor prognosis of glioblastoma (GBM). As a first-line chemotherapeutic agent for GBM, TMZ exerts its cytotoxic effects through DNA alkylation. However, its therapeutic efficacy is significantly compromised by enhanced DNA damage repair (DDR) mechanisms in GBM cells. Although several DDR-targeting drugs have been developed, their clinical outcomes remain suboptimal. Post-translational modifications (PTMs) in GBM cells play a pivotal role in maintaining the genomic stability of DDR mechanisms, including methylguanine-DNA methyltransferase-mediated repair, DNA mismatch repair dysfunction, base excision repair, and double-strand break repair. This review focuses on elucidating the regulatory roles of PTMs in the intrinsic mechanisms underlying TMZ resistance in GBM. Furthermore, we explore the feasibility of enhancing TMZ-induced cytotoxicity by targeting PTM-related enzymatic to disrupt key steps in PTM-mediated DDR pathways. By integrating current preclinical insights and clinical challenges, this work highlights the potential of modulating PTM-driven networks as a novel therapeutic strategy to overcome TMZ resistance and improve treatment outcomes for GBM patients.}, }
@article {pmid40419502, year = {2025}, author = {Rajabi, N and Zanettin, I and Ribeiro, AH and Vasco, M and Björkman, M and Lundström, JN and Kragic, D}, title = {Exploring the feasibility of olfactory brain-computer interfaces.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18404}, pmid = {40419502}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Odorants/analysis ; Male ; Adult ; Female ; *Smell/physiology ; Feasibility Studies ; Neural Networks, Computer ; Young Adult ; *Olfactory Perception/physiology ; *Brain/physiology ; }, abstract = {In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.}, }
@article {pmid40419488, year = {2025}, author = {Wang, D and Xue, H and Xia, L and Li, Z and Zhao, Y and Fan, X and Sun, K and Wang, H and Hamalainen, T and Zhang, C and Cong, F and Li, Y and Song, F and Lin, J}, title = {A tough semi-dry hydrogel electrode with anti-bacterial properties for long-term repeatable non-invasive EEG acquisition.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {105}, pmid = {40419488}, issn = {2055-7434}, support = {2022 ZD0210700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, abstract = {Non-invasive brain-computer interfaces (NI-BCIs) have garnered significant attention due to their safety and wide range of applications. However, developing non-invasive electroencephalogram (EEG) electrodes that are highly sensitive, comfortable to wear, and reusable has been challenging due to the limitations of conventional electrodes. Here, we introduce a simple method for fabricating semi-dry hydrogel EEG electrodes with antibacterial properties, enabling long-term, repeatable acquisition of EEG. By utilizing N-acryloyl glycinamide and hydroxypropyltrimethyl ammonium chloride chitosan, we have prepared electrodes that not only possess good mechanical properties (compression modulus 65 kPa) and anti-fatigue properties but also exhibit superior antibacterial properties. These electrodes effectively inhibit the growth of both Gram-negative (E. coli) and Gram-positive (S. epidermidis) bacteria. Furthermore, the hydrogel maintains stable water retention properties, resulting in an average contact impedance of <400 Ω measured over 12 h, and an ionic conductivity of 0.39 mS cm[-1]. Cytotoxicity and skin irritation tests have confirmed the high biocompatibility of the hydrogel electrodes. In an N170 event-related potential (ERP) test on human volunteers, we successfully captured the expected ERP signal waveform and a high signal-to-noise ratio (20.02 dB), comparable to that of conventional wet electrodes. Moreover, contact impedance on the scalps remained below 100 kΩ for 12 h, while wet electrodes became unable to detect signals after 7-8 h due to dehydration. In summary, our hydrogel electrodes are capable of detecting ERPs over extended periods in an easy-to-use manner with antibacterial properties. This reduces the risk of bacterial infection associated with prolonged reuse and expands the potential of NI-BCIs in daily life.}, }
@article {pmid40419083, year = {2025}, author = {Chen, L and Zhang, L and Wang, Z and Li, Q and Gu, B and Ming, D}, title = {Task-related reconfiguration patterns of frontoparietal network during motor imagery.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2025.05.035}, pmid = {40419083}, issn = {1873-7544}, abstract = {Motor imagery (MI) is closely associated with the frontoparietal network that includes prefrontal and posterior parietal regions. Studying task-related network reconfiguration after brain shifts from the resting state to the MI task is an important way to understand the brain's response process. However, how the brain modulates functional connectivity of the frontoparietal network when it shifts to MI has not been thoroughly studied. In this study, we attempted to characterize the frontoparietal network reconfiguration patterns as the brain transitioned to motor imagery tasks. We performed the analysis using EEG signals from 52 healthy subjects during left- and right-hand MI tasks. The results indicated distinct reconfiguration patterns in the frontoparietal network across four typical brain wave rhythms (theta (4 ∼ 7 Hz), alpha (8 ∼ 13 Hz), beta (14 ∼ 30 Hz), and gamma (31 ∼ 45 Hz)). Meanwhile, there was a significant positive correlation between the frontoparietal network reconfiguration and the event-related desynchronization of alpha and beta rhythms in the sensorimotor cortex. We further found that subjects with better MI-BCI performance exhibited greater reconfiguration of the frontoparietal network in alpha and beta rhythms. These findings implied that MI was accompanied by a shift in information interaction between brain regions, which might contribute to understanding the neural mechanisms of MI.}, }
@article {pmid40418615, year = {2025}, author = {Song, Y and Wang, Y and He, H and Gao, X}, title = {Recognizing Natural Images From EEG With Language-Guided Contrastive Learning.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3562743}, pmid = {40418615}, issn = {2162-2388}, abstract = {Electroencephalography (EEG), known for its convenient noninvasive acquisition but moderate signal-to-noise ratio, has recently gained much attention due to the potential to decode image information. However, previous works have not delivered sufficient evidence of this task, primarily limited by performance and biological plausibility. In this work, we first introduce a self-supervised framework to demonstrate the feasibility of recognizing images from EEG signals. Contrastive learning is leveraged to align the representations of EEG responses with image stimuli. Then, language descriptions of the stimuli generated by large language models (LLMs) help guide learning core semantic information. With the framework, we attain significantly above-chance results on the THINGS-EEG2 dataset, achieving a top-1 accuracy of 19.7% and a top-5 accuracy of 51.5% in challenging 200-way zero-shot tasks. Furthermore, we conduct thorough experiments to resolve the human visual responses with EEG from temporal, spatial, spectral, and semantic perspectives. These results provide evidence of feasibility and plausibility regarding EEG-based image recognition, substantiated by comparative studies with the THINGS-Magnetoencephalography (MEG) dataset. The findings offer valuable insights for neural decoding and real-world applications of brain-computer interfaces (BCIs), such as health care and robot control. The code is available at https://github.com/eeyhsong/NICE-LLM.}, }
@article {pmid40416647, year = {2025}, author = {Teng, Y and Song, L and Shi, J and Lv, Q and Hou, S and Ramakrishna, S}, title = {Advancing electrospinning towards the future of biomaterials in biomedical engineering.}, journal = {Regenerative biomaterials}, volume = {12}, number = {}, pages = {rbaf034}, pmid = {40416647}, issn = {2056-3418}, abstract = {Biomaterial is a material designed to take a form that can direct, through interactions with living systems, the course of any therapeutic or diagnostic procedure. Growing demand for improved and affordable healthcare treatments and unmet clinical needs seek further advancement of biomaterials. Over the past 25 years, the electrospinning method has been innovated to enhance biomaterials at nanometer and micrometer length scales for diverse healthcare applications. Recent developments include intelligent (smart) biomaterials and sustainable biomaterials. Intelligent materials can sense, adapt to and respond to external stimuli, autonomously adjusting to enhance functionality and performance. Sustainable biomaterials possess several key characteristics, including renewability, a low carbon footprint, circularity, durability, biocompatibility, biodegradability and others. Herein, advances in electrospun biomaterials, encompassing process innovations, working principles and the effects of process variables, are presented succinctly. The potential of electrospun intelligent biomaterials and sustainable biomaterials in specific biomedical applications, including tissue engineering, regenerative medicine, drug delivery systems, brain-computer interfaces, biosensors, personal protective equipment and wearable devices, is explored. More effective healthcare demands further advancements in electrospun biomaterials. In the future, the distinctive characteristics of intelligent biomaterials and sustainable biomaterials, integrated with various emerging technologies (such as AI and data transmission), will enable physicians to conduct remote diagnosis and treatment. This advancement significantly enhances telemedicine capabilities for more accurate disease prediction and management.}, }
@article {pmid40416500, year = {2025}, author = {Mokienko, OA}, title = {The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).}, journal = {Sovremennye tekhnologii v meditsine}, volume = {17}, number = {2}, pages = {73-83}, pmid = {40416500}, issn = {2309-995X}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy/diagnosis/diagnostic imaging ; }, abstract = {The advancement of novel technologies for the rehabilitation of post-stroke patients represents a significant challenge for a range of interdisciplinary fields. Near-infrared spectroscopy (NIRS) is an optical neuroimaging technique based on recording local hemodynamic changes at the cerebral cortex level. The technology is typically employed in post-stroke patients for diagnostic purposes, including the assessment of neuroplastic processes accompanying therapy, the study of hemispheric asymmetry, and the examination of functional brain networks. However, functional NIRS can also be used for therapeutic purposes, including the provision of biofeedback during rehabilitation tasks, as well as the navigation method during transcranial stimulation. The effectiveness of therapeutic NIRS application in stroke patients remains insufficiently studied, despite existing scientific evidence confirming its promising potential as a treatment method. The review examines the published literature on the therapeutic applications of NIRS after stroke, evaluating its potential role in the rehabilitation process. The paper describes NIRS features, advantages, and disadvantages, determining its position among other neuroimaging technologies; analyzes the findings of neurophysiological studies, which justified the clinical trials of NIRS technology; and evaluates the results of the studies on the therapeutic use of NIRS in post-stroke patients. Two potential applications of NIRS for therapeutic purposes following a stroke were suggested: the first was to provide real-time feedback during movement training (motor or ideomotor ones, including that in brain-computer interface circuits), and the second was to facilitate navigation during transcranial stimulation. Based on a comprehensive literature review, there were proposed and justified further research lines and development in this field.}, }
@article {pmid40414967, year = {2025}, author = {Qian, L and Jia, C and Wang, J and Shi, L and Wang, Z and Wang, S}, title = {The dynamics of stimulus selection in the nucleus isthmi pars magnocellularis of avian midbrain network.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {18260}, pmid = {40414967}, issn = {2045-2322}, support = {2024M752934//China Postdoctoral Science Foundation/ ; }, mesh = {Animals ; *Mesencephalon/physiology ; Neurons/physiology ; Photic Stimulation ; *Nerve Net/physiology ; Birds/physiology ; }, abstract = {The nucleus isthmi pars magnocellularis (Imc) serves as a critical node in the avian midbrain network for encoding stimulus salience and selection. While reciprocal inhibitory projections among Imc neurons (inhibitory loop) are known to govern stimulus selection, existing studies have predominantly focused on stimulus selection under stimuli of constant relative intensity. However, animals typically encounter complex and changeable visual scenes. Thus, how Imc neurons represent stimulus selection under varying relative stimulus intensities remains unclear. Here, we examined the dynamics of stimulus selection by in vivo recording of Imc neurons' responses to spatiotemporally successive visual stimuli divided into two segments: the previous stimulus and the post stimulus. Our data demonstrate that Imc neurons can encode sensory memory of the previous stimulus, which modulates competition and salience representation in the post stimulus. This history-dependent modulation is also manifested in persistent neural activity after stimulus cessation. We identified, through neural tracing, focal inactivation, and computational modeling experiments, projections from the nucleus isthmi pars parvocellularis (Ipc) to "shepherd's crook" (Shc) neurons, which could be either direct or indirect. These projections enhance Imc neurons' responses and persistent neural activity after stimulus cessation. This connectivity supports a Shc-Ipc-Shc excitatory loop in the midbrain network. The coexistence of excitatory and inhibitory loops provides a neural substrate for continuous attractor network models, a proposed framework for neural information representation. This study also offers a potential explanation for how animals maintain short-term attention to targets in complex and changeable environments.}, }
@article {pmid40414233, year = {2025}, author = {Paton, NI and Cousins, C and Sari, IP and Burhan, E and Ng, NK and Dalay, VB and Suresh, C and Kusmiati, T and Chew, KL and Balanag, VM and Lu, Q and Ruslami, R and Djaharuddin, I and Sugiri, JJR and Veto, RS and Sekaggya-Wiltshire, C and Avihingsanon, A and Saini, JK and Papineni, P and Nunn, AJ and Crook, AM and , }, title = {Efficacy and safety of 8-week regimens for the treatment of rifampicin-susceptible pulmonary tuberculosis (TRUNCATE-TB): a prespecified exploratory analysis of a multi-arm, multi-stage, open-label, randomised controlled trial.}, journal = {The Lancet. Infectious diseases}, volume = {}, number = {}, pages = {}, doi = {10.1016/S1473-3099(25)00151-3}, pmid = {40414233}, issn = {1474-4457}, abstract = {BACKGROUND: WHO recommends a 2-month optimal duration for new drug regimens for rifampicin-susceptible tuberculosis. We aimed to investigate the efficacy and safety of the 8-week regimens that were assessed as part of the TRUNCATE management strategy of the TRUNCATE-TB trial.<br><br>METHODS: TRUNCATE-TB was a multi-arm, multi-stage, open-label, randomised controlled trial in which participants aged 18-65 years with rifampicin-susceptible pulmonary tuberculosis were randomly assigned via a web-based system, using permuted blocks, to 24-week standard treatment (rifampicin, isoniazid, pyrazinamide, and ethambutol) or the TRUNCATE management strategy comprising initial 8-week treatment, then post-treatment monitoring and re-treatment where needed. The four 8-week regimens comprised five drugs, modified from standard treatment: high-dose rifampicin and linezolid, or high-dose rifampicin and clofazimine, or bedaquiline and linezolid, all given with isoniazid, pyrazinamide, and ethambutol; and rifapentine, linezolid, and levofloxacin, given with isoniazid and pyrazinamide. Here, we report the efficacy (proportion with unfavourable outcome; and difference from standard treatment, assessed via Bayesian methods) and safety of the 8-week regimens, assessed in the intention-to-treat population. This prespecified exploratory analysis is distinct from the previously reported 96-week outcome of the strategy in which the regimens were deployed. This trial is registered with ClinicalTrials.gov (NCT03474198).<br><br>FINDINGS: Between March 21, 2018, and March 26, 2020, 675 participants (674 in the intention-to-treat population) were enrolled and randomly assigned to the standard treatment group or one of the four 8-week regimen groups. Two 8-week regimens progressed to full enrolment. An unfavourable outcome (mainly relapse) occurred in seven (4%) of 181 participants on standard treatment; 46 (25%) of 184 on the high-dose rifampicin and linezolid-containing regimen (adjusted difference 21&#xb7;0%, 95% Bayesian credible interval [BCI] 14&#xb7;3-28&#xb7;1); and 26 (14%) of 189 on the bedaquiline and linezolid-containing regimen (adjusted difference 9&#xb7;3% [4&#xb7;3-14&#xb7;9]). Grade 3-4 adverse events occurred in 24 (14%) of 181 participants on standard treatment, 20 (11%) of 184 on the rifampicin-linezolid regimen, and 22 (12%) of 189 on the bedaquiline-linezolid regimen.<br><br>INTERPRETATION: Efficacy was worse with 8-week regimens, although the difference from standard treatment varied between regimens. Even the best 8-week regimen (bedaquiline-linezolid) should only be used as part of a management strategy involving post-treatment monitoring and re-treatment if necessary.<br><br>FUNDING: Singapore National Medical Research Council; UK Department of Health and Social Care; UK Foreign, Commonwealth, and Development Office; UK Medical Research Council; Wellcome Trust; and UK Research and Innovation Medical Research Council.}, }
@article {pmid40411529, year = {2025}, author = {Sun, Y and Guan, M and Chen, X and Feng, F and He, R and Huang, L and Tong, X and Zhou, H and Liu, X and Ming, D}, title = {Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.}, journal = {Epilepsia}, volume = {}, number = {}, pages = {}, doi = {10.1111/epi.18463}, pmid = {40411529}, issn = {1528-1167}, support = {020/0903065111//Tianjin University Innovation Fund/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; c02022088//National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; }, abstract = {OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.<br><br>METHODS: We proposed a novel deep learning framework, U-IEDNet, for detecting IEDs in multichannel EEG. The U-IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single-channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U-shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation.<br><br>RESULTS: The results showed prominent advantage compared with other methods. U-IEDNet achieved a recall of .916, precision of .911, F1-score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1-score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1-score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model.<br><br>SIGNIFICANCE: In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.}, }
@article {pmid40409524, year = {2025}, author = {Li, Y and Pan, Y and Zhao, D}, title = {Understanding the neurobiology and computational mechanisms of social conformity: implications for psychiatric disorders.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2025.05.011}, pmid = {40409524}, issn = {1873-2402}, abstract = {Social conformity and psychiatric disorders share overlapping brain regions and neural pathways, arousing our interest in uncovering their potentially shared underlying neural and computational mechanisms. Critically, the dynamics of group behavior may either mitigate or exacerbate mental health conditions, highlighting the need to bridge social neuroscience and psychiatry. Our work examines how aberrant neurobiological circuits and computations influence social conformity. We propose a hierarchical computational framework, based on dynamical systems and active inference, to facilitate the interpretation of the multi-layered interplay among processes that drive social conformity. We underscore the significant implications of this hierarchical computational framework for guiding future research on psychiatry, particularly with respect to the clinical translation of interventions such as targeted pharmacotherapy and neurostimulation techniques. The interdisciplinary efforts hold the potential to propel the fields of social and clinical neuroscience forward, fostering the emergence of more efficacious and individualized therapeutic approaches tailored to psychiatric disorders characterized by aberrant social behaviors.}, }
@article {pmid40408764, year = {2025}, author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C}, title = {Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.}, journal = {Journal of medical Internet research}, volume = {27}, number = {}, pages = {e71741}, doi = {10.2196/71741}, pmid = {40408764}, issn = {1438-8871}, mesh = {Humans ; *Students, Nursing/psychology ; *Mindfulness/methods ; Female ; Male ; *Mental Health ; China ; *Neurofeedback/methods ; Adult ; Young Adult ; Anxiety/therapy ; *Internet ; Depression/therapy ; *Internet-Based Intervention ; East Asian People ; }, abstract = {BACKGROUND: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue.<br><br>OBJECTIVE: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students.<br><br>METHODS: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time.<br><br>RESULTS: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups.<br><br>CONCLUSIONS: This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.}, }
@article {pmid40408491, year = {2025}, author = {Zhou, H and Wu, J and Li, J and Pan, Z and Lu, J and Shen, M and Wang, T and Hu, Y and Gao, Z}, title = {Event cache: An independent component in working memory.}, journal = {Science advances}, volume = {11}, number = {21}, pages = {eadt3063}, pmid = {40408491}, issn = {2375-2548}, mesh = {*Memory, Short-Term/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; Brain Mapping ; *Cerebellum/physiology ; Brain/physiology ; }, abstract = {Working memory (WM) has been a major focus of cognitive science and neuroscience for the past 50 years. While most WM research has centered on the mechanisms of objects, there has been a lack of investigation into the cognitive and neural mechanisms of events, which are the building blocks of our experience. Using confirmatory factor analysis, psychophysical experiments, and resting-state and task functional magnetic resonance imaging methods, our study demonstrated that events have an independent storage space within WM, named as event cache, with distinct neural correlates compared to object storage in WM. We found the cerebellar network to be the most essential network for event cache, with the left cerebellum Crus I being particularly involved in encoding and maintaining events. Our findings shed critical light on the neuropsychological mechanism of WM by revealing event cache as an independent component of WM and encourage the reconsideration of theoretical models for WM.}, }
@article {pmid40408214, year = {2025}, author = {Chen, W and Li, Y and Zheng, N and Shi, W}, title = {DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3573042}, pmid = {40408214}, issn = {2168-2208}, abstract = {Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG), which degrade signal quality and affect subsequent analysis. Effective EEG denoising is critical for enhancing the performance of EEG-based applications, including disease diagnosis and brain-computer interfaces (BCIs). While recent deep learning (DL) approaches have shown promise in this area, they often struggle to efficiently model the temporal dependencies inherent in EEG signals, as well as to capture local contextual information simultaneously. In this work, we introduce DenoiseMamba, a novel deep learning-based EEG denoising model. The model incorporates the ConvSSD module, which integrates convolutional neural networks (CNNs) with structured state-space duality (SSD) mechanisms. This allows DenoiseMamba to capture both local and global spatiotemporal features, resulting in more effective artifact suppression. Extensive experiments on three semi-simulated datasets demonstrate that DenoiseMamba outperforms existing methods in EEG reconstruction accuracy, effectively eliminating myoelectric, electrooculographic, and electrocardiographic artifacts while preserving critical EEG signal details.}, }
@article {pmid40408213, year = {2025}, author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z}, title = {MTSNet: Convolution-based Transformer Network with Multi-scale Temporal-Spectral Feature Fusion for SSVEP Signal Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3573410}, pmid = {40408213}, issn = {2168-2208}, abstract = {Improving the decoding performance of steady-state visual evoked (SSVEP) signals is crucial for the practical application of SSVEP-based brain-computer interface (BCI) systems. Although numerous methods have achieved impressive results in decoding SSVEP signals, most of them focus only on the temporal or spectral domain information or concatenate them directly, which may ignore the complementary relationship between different features. To address this issue, we propose a dual-branch convolution-based Transformer network with multi-scale temporal-spectral feature fusion, termed MTSNet, to improve the decoding performance of SSVEP signals. Specifically, the temporal branch extracts temporal features from the SSVEP signals using the multi-level convolution-based Transformer (Convformer) that can adapt to the dynamic fluctuations of SSVEP signals. In parallel, the spectral branch takes the complex spectrum converted from temporal signals by the zero-padding fast Fourier transform as input and uses the Convformer to extract spectral features. These extracted temporal and spectral features are then integrated by the multi-scale feature fusion module to obtain comprehensive features with different scale information, thereby enhancing the interactions between the features and improving the effectiveness and robustness. Extensive experimental results on two widely used public SSVEP datasets, Benchmark and BETA, show that the proposed MTSNet significantly outperforms the state-of-the-art calibration-free methods in terms of accuracy and ITR. The superior performance demonstrates the effectiveness of our method in decoding SSVEP signals, which may facilitate the practical application of SSVEP-based BCI systems.}, }
@article {pmid40408200, year = {2025}, author = {Ingolfsson, TM and Kartsch, V and Benini, L and Cossettini, A}, title = {A Wearable Ultra-Low-Power System for EEG-based Speech-Imagery Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2025.3573027}, pmid = {40408200}, issn = {1940-9990}, abstract = {Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VOWELNET, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of- the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system's performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.}, }
@article {pmid40407663, year = {2025}, author = {Astefanei, O and Martu, C and Cozma, S and Radulescu, L}, title = {Cochlear and Bone Conduction Implants in Asymmetric Hearing Loss and Single-Sided Deafness: Effects on Localization, Speech in Noise, and Quality of Life.}, journal = {Audiology research}, volume = {15}, number = {3}, pages = {}, doi = {10.3390/audiolres15030049}, pmid = {40407663}, issn = {2039-4330}, abstract = {BACKGROUND: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life.<br><br>OBJECTIVE: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit.<br><br>METHODS: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05).<br><br>RESULTS: In adult CI users, localization error significantly decreased from 81.9&#xb0; &#xb1; 15.8&#xb0; to 43.7&#xb0; &#xb1; 13.5&#xb0; (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3&#xb0; to 44.8&#xb0;, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6&#xb0; to 69.2&#xb0;, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 &#xb1; 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 &#xb1; 1.2) and in speech understanding for BCI users (1.6 &#xb1; 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = -0.48).<br><br>CONCLUSIONS: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input.}, }
@article {pmid40406801, year = {2025}, author = {Moumdjian, RA}, title = {Bioethics of neurotechnologies: a field in effervescence.}, journal = {Neurological research}, volume = {}, number = {}, pages = {1-4}, doi = {10.1080/01616412.2025.2499896}, pmid = {40406801}, issn = {1743-1328}, abstract = {Brain-Computer Interface (BCI) comprises a device that detects brain signals conveying specific intentions and translates them into executable outputs by a machine. It enables neurologically impaired patients to regain some control over their environment, thereby aiding in their rehabilitation. Some authors argue that 'the use of BCI is the greatest ethical challenge that neuroscience faces today. Ethical issues highlighted in the literature include safety, justice, privacy, security, and the balance of risks and benefits.}, }
@article {pmid40403087, year = {2025}, author = {Essam, AA and Ibrahim, A and Seif Al-Nasr, A and El-Saqa, M and Mohamed, S and Anwar, A and Eldeib, A and Akcakaya, M and Khalaf, A}, title = {Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.}, journal = {PloS one}, volume = {20}, number = {5}, pages = {e0311075}, pmid = {40403087}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; Adult ; Female ; Young Adult ; Bayes Theorem ; *Brain/physiology ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) provide alternative means of communication and control for individuals with severe motor or speech impairments. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to advance the state of the art in multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) by introducing advanced analysis approaches that enhance system performance. Our EEG-fTCD BCIs employ two distinct paradigms to infer user intent: motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. In the MI paradigm, we introduce the use of Filter Bank Common Spatial Pattern (FBCSP) for the first time in an EEG-fTCD BCI, while in the flickering MR/WG paradigm, we extend FBCSP application to non-motor imagery tasks. Additionally, we extract previously unexplored time-series features from the envelope of fTCD signals, leveraging richer information from cerebral blood flow dynamics. Furthermore, we employ a Bayesian fusion framework that allows EEG and fTCD to contribute unequally to decision-making. The multimodal EEG-fTCD system achieved high classification accuracies across tasks in both paradigms. In the MI paradigm, accuracies of 94.53%, 94.9%, and 96.29% were achieved for left arm MI vs. baseline, right arm MI vs. baseline, and right arm MI vs. left arm MI, respectively - outperforming EEG-only accuracy by 3.87%, 3.80%, and 5.81%, respectively. In the MR/WG paradigm, the system achieved 95.27%, 85.93%, and 96.97% for MR vs. baseline, WG vs. baseline, and MR vs. WG, respectively, showing accuracy improvements of 2.28%, 4.95%, and 1.56%, respectively compared to EEG-only results. Overall, the proposed analysis approach improved classification accuracy for 5 out of 6 binary classification problems within the MI and MR/WG paradigms, with gains ranging from 0.64% to 9% compared to our previous EEG-fTCD studies. Additionally, our results demonstrate that EEG-fTCD BCIs with the proposed analysis techniques outperform multimodal EEG-fNIRS BCIs in both accuracy and speed, improving classification performance by 2.7% to 24.7% and reducing trial durations by 2-38 seconds. These findings highlight the potential of the proposed approach to advance assistive technologies and improve patient quality of life.}, }
@article {pmid40402697, year = {2025}, author = {Chaisaen, R and Autthasan, P and Ditthapron, A and Wilaiprasitporn, T}, title = {AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3572197}, pmid = {40402697}, issn = {2168-2208}, abstract = {In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) classification. AlphaGrad is the first method to automatically adjust multi-loss functions with differing metric scales, including mean square error, cross-entropy, and deep metric learning, within the context of MI-EEG. We evaluate AlphaGrad using two state-of-the-art MTL-based neural networks, MIN2Net and FBMSNet, across four benchmark datasets. Experimental results show that AlphaGrad consistently outperforms existing strategies such as AdaMT, GradApprox, and fixed-weight baselines in classification accuracy and training stability. Compared to baseline static weighting, AlphaGrad achieves over 10% accuracy improvement on subject-independent MI tasks when evaluated on the largest benchmark dataset. Furthermore, AlphaGrad demonstrates robust adaptability across various EEG paradigms-including steady-state visually evoked potential (SSVEP) and event-related potential (ERP), making it broadly applicable to brain-computer interface (BCI) systems. We also provide gradient trajectory visualizations highlighting AlphaGrad's ability to maintain training stability and avoid local minima. These findings underscore AlphaGrad's promise as a general-purpose solution for adaptive multi-loss optimization in biomedical time-series learning.}, }
@article {pmid40401160, year = {2025}, author = {Zhao, L}, title = {Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf007}, pmid = {40401160}, issn = {2634-4416}, abstract = {Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.}, }
@article {pmid40401149, year = {2025}, author = {Leong, F and Micera, S and Shokur, S}, title = {Optimization frameworks for bespoke sensory encoding in neuroprosthetics.}, journal = {APL bioengineering}, volume = {9}, number = {2}, pages = {020901}, pmid = {40401149}, issn = {2473-2877}, abstract = {Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.}, }
@article {pmid40399603, year = {2025}, author = {Wang, Y and Fukuma, R and Seymour, B and Yang, H and Kishima, H and Yanagisawa, T}, title = {Neurofeedback modulation of insula activity via MEG-based brain-machine interface: a double-blind randomized controlled crossover trial.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {770}, pmid = {40399603}, issn = {2399-3642}, support = {JP19dm0307008//Japan Agency for Medical Research and Development (AMED)/ ; 19dm0207070h//Japan Agency for Medical Research and Development (AMED)/ ; JP24wm0625517//Japan Agency for Medical Research and Development (AMED)/ ; JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; JP20H05705//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 22H04998//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 214251/Z/18/Z//Wellcome Trust (Wellcome)/ ; EP/W03509X/1//DH | National Institute for Health Research (NIHR)/ ; 203316//DH | National Institute for Health Research (NIHR)/ ; }, mesh = {Humans ; *Neurofeedback/methods ; *Magnetoencephalography/methods ; Double-Blind Method ; Male ; Cross-Over Studies ; *Brain-Computer Interfaces ; Female ; Adult ; Young Adult ; *Insular Cortex/physiology ; Pain Threshold/physiology ; *Cerebral Cortex/physiology ; }, abstract = {Insula activity has often been linked to pain perception, making it a potential target for therapeutic neuromodulation strategies such as neurofeedback. However, it is not known whether insula activity is under cognitive control and, if so, whether this activity is consequently causally related to pain. Here, we conducted a double-blind randomized controlled crossover trial to test the modulation of insula activity and pain thresholds using neurofeedback training. Nineteen healthy subjects underwent neurofeedback training for upmodulation and downmodulation of right insula activity using our magnetoencephalography (MEG)-based brain-machine interface. We observed significant differences in insula activity between the upmodulation and downmodulation training sessions. Furthermore, resting-state insula activity significantly decreased following downmodulation training compared to following upmodulation training. Compared with upmodulation training, downmodulation training was also associated with increased pain thresholds, albeit with no significant interaction effect. These findings show that humans can cognitively modulate insula activity as a potential route to develop therapeutic MEG neurofeedback systems for clinical testing. However, the present findings do not provide direct evidence of a causal link between modulation of insula activity and changes in pain thresholds.}, }
@article {pmid39818881, year = {2025}, author = {Valle, G and Alamri, AH and Downey, JE and Lienkämper, R and Jordan, PM and Sobinov, AR and Endsley, LJ and Prasad, D and Boninger, ML and Collinger, JL and Warnke, PC and Hatsopoulos, NG and Miller, LE and Gaunt, RA and Greenspon, CM and Bensmaia, SJ}, title = {Tactile edges and motion via patterned microstimulation of the human somatosensory cortex.}, journal = {Science (New York, N.Y.)}, volume = {387}, number = {6731}, pages = {315-322}, pmid = {39818881}, issn = {1095-9203}, support = {R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Bionics ; Cervical Cord/injuries ; *Deep Brain Stimulation/methods ; Hand/physiology ; *Somatosensory Cortex/physiology ; *Touch/physiology ; *Touch Perception/physiology ; Trauma, Nervous System/physiopathology/therapy ; *Brain-Computer Interfaces ; }, abstract = {Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin. We simultaneously delivered ICMS through electrodes with spatially patterned projected fields (PFs), evoking sensations of edges. We then created complex PFs that encode arbitrary tactile shapes and skin indentation patterns. By delivering spatiotemporally patterned ICMS, we evoked sensation of motion across the skin, the speed and direction of which could be controlled. Thus, we improved individuals' tactile experience and use of brain-controlled bionic hands.}, }
@article {pmid40398442, year = {2025}, author = {Peterson, V and Spagnolo, V and Galván, CM and Nieto, N and Spies, R and Milone, DH}, title = {Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addb7a}, pmid = {40398442}, issn = {1741-2552}, abstract = {OBJECTIVE: Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.<br><br>APPROACH: Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.<br><br>MAIN RESULTS: Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.<br><br>SIGNIFICANCE: This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.}, }
@article {pmid40398440, year = {2025}, author = {Van der Eerden, JHM and Liu, PC and Villalobos, J and Yanagisawa, T and Grayden, DB and John, SE}, title = {Decoding cortical responses from visual input using an endovascular brain-computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/addb7c}, pmid = {40398440}, issn = {1741-2552}, abstract = {Implantable neural interfaces enable recording of high-quality brain signals that can improve our understanding of brain function. This work examined the feasibility of using a minimally invasive endovascular neural interface (ENI) to record interpretable cortical activity from the visual cortex. Approach: A sheep model (n = 5) was used to record and decode visually evoked potentials from the cortex both with an ENI and a subdural electrode grid. Sets of distinct experimental visual stimuli were presented to attempt decoding from the recorded cortical potentials, using perceptual categories of colour, contrast, movement direction orientation, spatial frequency and temporal frequency. Decoding performances are presented as accuracy scores from K-fold cross-validation of a stratified random forest classification model. The study compared the signal quality and decoding performance between the ENI and electrocorticography electrodes. Main results: Recordings from the ENI array resulted in lower decoding performances than the electrocorticography array, but the classification scores were significantly above chance in the stimuli categories of colour, contrast, direction and temporal frequency. This study is the first report of visually evoked neural activity using a minimally-invasive ENI. Significance: Overall, the results show that implantable macro-electrodes yield sufficient neural signal definition to discern primary visual percepts, using both endo-vascular and intracranial surgical placements.}, }
@article {pmid40398391, year = {2025}, author = {Huang, W and Shu, N}, title = {AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.}, journal = {Cell reports. Medicine}, volume = {6}, number = {5}, pages = {102132}, doi = {10.1016/j.xcrm.2025.102132}, pmid = {40398391}, issn = {2666-3791}, mesh = {Humans ; *Precision Medicine/methods ; *Multimodal Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Artificial Intelligence ; *Neuroimaging/methods ; }, abstract = {Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.}, }
@article {pmid40398228, year = {2025}, author = {Xiao, S and Huang, X and He, X and Chen, Z and Li, X and Wei, X and Liu, Q and Dong, H and Zeng, X and Bai, W}, title = {Interactions between curcumin and fish-/bovine-derived (type I and II) collagens: Preparation of nanoparticle and their application in Pickering emulsions.}, journal = {Food chemistry}, volume = {487}, number = {}, pages = {144781}, doi = {10.1016/j.foodchem.2025.144781}, pmid = {40398228}, issn = {1873-7072}, abstract = {This study aims to elucidate the interaction mechanisms between curcumin (Cur) and four collagen subtypes (fish type I [FCI], bovine type I [BCI], fish type II [FCII], bovine type II [BCII]), with parallel characterization of the structural and functional attributes of their derived nanoparticles. Type I Collagen/Cur nanoparticles exhibited superior solution stability compared to type II. Cur binding significantly enhanced the surface hydrophobicity, absolute ζ potential, and surface tension of collagen, while reduced dynamic interfacial tension. The binding type of Cur to collagen was static, and binding process was enthalpy-driven exothermic reaction. Molecular dynamics simulations revealed that hydrophobic interactions, hydrogen bonds, and electrostatic forces dominated the binding process. The binding affinity followed the order: FCI/Cur > BCI/Cur > FCII/Cur > BCII/Cur. The binding sites of Cur to type I collagen and type II collagen were around Ser129-Glu135 and Asn179-Ser183 residues. Collagen/Cur nanoparticle stabilized emulsions and improved oxidative stability and storage modulus.}, }
@article {pmid40395924, year = {2025}, author = {Russell, M and Hincks, S and Wang, L and Babar, A and Chen, Z and White, Z and Jacob, RJK}, title = {Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1550629}, pmid = {40395924}, issn = {2673-6195}, abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.}, }
@article {pmid40395688, year = {2025}, author = {Tibermacine, IE and Russo, S and Citeroni, F and Mancini, G and Rabehi, A and Alharbi, AH and El-Kenawy, EM and Napoli, C}, title = {Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1583342}, pmid = {40395688}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying artifacts. In response, the present study explores a Generative Adversarial Network (GAN) framework to enhance EEG signal quality, focusing on two variants: a conventional GAN model and a Wasserstein GAN with Gradient Penalty (WGAN-GP).<br><br>METHODS: Data were obtained from two distinct EEG datasets: a "healthy" set of 64-channel recordings collected during various motor/imagery tasks, and an "unhealthy" set of 18-channel recordings from individuals with orthopedic impairments. Both datasets underwent comprehensive preprocessing, including band-pass filtering (8-30 Hz), channel standardization, and artifact trimming. The training stage involved adversarial learning, in which a generator sought to reconstruct clean EEG signals while a discriminator (or critic in the case of WGAN-GP) attempted to distinguish between real and generated signals. The model evaluation was conducted using quantitative metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), correlation coefficient, mutual information, and dynamic time warping (DTW) distance.<br><br>RESULTS: Experimental findings indicate that adversarial learning substantially improves EEG signal fidelity across multiple quantitative metrics. Specifically, WGAN-GP achieved an SNR of up to 14.47 dB (compared to 12.37 dB for the standard GAN) and exhibited greater training stability, as evidenced by consistently lower relative root mean squared error (RRMSE) values. In contrast, the conventional GAN model excelled in preserving finer signal details, reflected in a PSNR of 19.28 dB and a correlation coefficient exceeding 0.90 in several recordings. Both adversarial frameworks outperformed classical wavelet-based thresholding and linear filtering methods, demonstrating superior adaptability to nonlinear distortions and dynamic interference patterns in EEG time-series data.<br><br>DISCUSSION: By systematically comparing standard GAN and WGAN-GP architectures, this study highlights a practical trade-off between aggressive noise suppression and high-fidelity signal reconstruction. The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain-computer interfaces (BCIs) in clinical or consumer-grade settings. The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.}, }
@article {pmid40395354, year = {2025}, author = {Zargarian, SS and Rinoldi, C and Ziai, Y and Zakrzewska, A and Fiorelli, R and Gazińska, M and Marinelli, M and Majkowska, M and Hottowy, P and Mindur, B and Czajkowski, R and Kublik, E and Nakielski, P and Lanzi, M and Kaczmarek, L and Pierini, F}, title = {Chronic Probing of Deep Brain Neuronal Activity Using Nanofibrous Smart Conducting Hydrogel-Based Brain-Machine Interface Probes.}, journal = {Small science}, volume = {5}, number = {5}, pages = {2400463}, pmid = {40395354}, issn = {2688-4046}, abstract = {The mechanical mismatch between microelectrode of brain-machine interfaces (BMIs) and soft brain tissue during electrophysiological investigations leads to inflammation, glial scarring, and compromising performance. Herein, a nanostructured, stimuli-responsive, conductive, and semi-interpenetrating polymer network hydrogel-based coated BMIs probe is introduced. The system interface is composed of a cross-linkable poly(N-isopropylacrylamide)-based copolymer and regioregular poly[3-(6-methoxyhexyl)thiophene] fabricated via electrospinning and integrated into a neural probe. The coating's nanofibrous architecture offers a rapid swelling response and faster shape recovery compared to bulk hydrogels. Moreover, the smart coating becomes more conductive at physiological temperatures, which improves signal transmission efficiency and enhances its stability during chronic use. Indeed, detecting acute neuronal deep brain signals in a mouse model demonstrates that the developed probe can record high-quality signals and action potentials, favorably modulating impedance and capacitance. Evaluation of in vivo neuronal activity and biocompatibility in chronic configuration shows the successful recording of deep brain signals and a lack of substantial inflammatory response in the long-term. The development of conducting fibrous hydrogel bio-interface demonstrates its potential to overcome the limitations of current neural probes, highlighting its promising properties as a candidate for long-term, high-quality detection of neuronal activities for deep brain applications such as BMIs.}, }
@article {pmid40395337, year = {2025}, author = {Yao, J and Zhou, Z and Tong, Q and Li, L and Wei, J and Lu, J and Hu, S and Bao, A and He, H}, title = {Magnetic resonance imaging of postmortem human brain specimens: methodological considerations and prospects in psychoradiology.}, journal = {Psychoradiology}, volume = {5}, number = {}, pages = {kkaf012}, pmid = {40395337}, issn = {2634-4416}, abstract = {Ex vivo magnetic resonance imaging (MRI) has revolutionized psychoradiological research by enabling detailed structural and pathological assessments of the brain in conditions ranging from psychiatric disorders to neurodegenerative diseases. By providing high-resolution images of postmortem brain tissue, ex vivo MRI overcomes several limitations inherent in in vivo imaging, offering unparalleled insights into the underlying pathophysiology of mental disorders. This review critically summarizes the state-of-the-art ex vivo MRI methodologies for neuroanatomical mapping and pathological characterization in psychoradiology, while also establishing standardized specimen processing protocols. Furthermore, we explore the prospects of application in ex vivo MRI in schizophrenia, major depressive disorder and bipolar disorder, highlighting its role in understanding neuroanatomical alterations, disease progression, and the validation of in vivo neuroimaging biomarkers.}, }
@article {pmid40395088, year = {2025}, author = {Zhang, S and Gu, J and Yang, Y and Li, J and Ni, L}, title = {Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.}, journal = {Brain and behavior}, volume = {15}, number = {5}, pages = {e70451}, doi = {10.1002/brb3.70451}, pmid = {40395088}, issn = {2162-3279}, support = {2020Z388//Jiangsu Postdoctoral Research Foundation/ ; //Top Talent Support Program for young and middle-aged people of the Wuxi Health Committee/ ; M202033//Wuxi Health Commission Scientific Research Project/ ; 24CC00903//Beijing Academy of Science and Technology Think Tank Research Project/ ; ZYYB05//Wuxi Administration of Traditional Chinese Medicine/ ; }, mesh = {*Bibliometrics ; Humans ; *Biomedical Research/trends ; *Neurosciences/trends ; *Brain/physiology ; United States ; China ; }, abstract = {BACKGROUND: Brain science research is considered the crown jewel of 21st-century scientific research; the United States, the United Kingdom, and Japan have elevated brain science research to a national strategic level. This study employs bibliometric analysis and knowledge graph visualization to map global trends, research hotspots, and collaborative networks in brain science, providing insights into the field's evolving landscape and future directions.<br><br>METHODS: We analyzed 13,590 articles (1990-2023) from the Web of Science Core Collection using CiteSpace and VOSviewer. Metrics included publication volume, co-authorship networks, citation patterns, keyword co-occurrence, and burst detection. Analytical tools such as VOSviewer, CiteSpace, and online bibliometric platforms were employed to facilitate this investigation.<br><br>RESULTS: The United States, China, and Germany dominated research output, with China's publications rising from sixth to second globally post-2016, driven by national initiatives like the China Brain Project. However, China exhibited limited international collaboration compared to the United States and European Union. Key journals included Human Brain Mapping and Journal of Neural Engineering, while emergent themes centered on "task analysis," "deep learning," and "brain-computer interfaces" (BCIs). Research clusters revealed three focal areas: (1) Brain Exploration (e.g., fMRI, diffusion tensor imaging), (2) Brain Protection (e.g., stroke rehabilitation, amyotrophic lateral sclerosis therapies), and (3) Brain Creation (e.g., neuromorphic computing, BCIs integrated with AR/VR). Despite China's high output, its influence lagged in highly cited scholars, reflecting a "quantity-over-quality" challenge.<br><br>CONCLUSION: Brain science research is in a golden period of development. This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in brain science. It reveals current research frontiers and crucial directions, offering a strategic roadmap for researchers and policymakers to navigate countries when planning research layouts.}, }
@article {pmid40395013, year = {2025}, author = {Pyo, YW and Kim, H and Park, HG}, title = {Graphene-Integrated Ultrathin Neural Probe for Multiregional Cortical Recordings.}, journal = {ACS nano}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsnano.5c03145}, pmid = {40395013}, issn = {1936-086X}, abstract = {Electrophysiological measurement techniques are essential for understanding the functions of the central and peripheral nervous systems. Specifically, noninvasive neural probes, such as surface electrode arrays, provide stable electrophysiological recordings without eliciting an immunological response. However, the ability to capture complex interactions across multiple brain regions is limited by their localized recording site. Here, we present the "large-area NeuroWeb (LNW)", an ultrathin, minimally invasive neural probe designed for extensive cortical recording and stimulation. LNW consists of four recording areas, each containing 16-channel platinum electrodes interconnected by graphene networks. In vivo experiments of the mouse brain exhibit stable, high-quality single-unit spike recordings for up to 7 days post-surgery. Simultaneous high-resolution neural activity recordings are performed across left/right somatosensory cortex and cerebellum, simplifying the experimental procedure by eliminating the necessity for multiple synchronized probes, thus reducing tissue displacement and inflammation. Furthermore, whisker and electrical stimulations demonstrate that the LNW has precise and bidirectional connections with neurons for reliable, region-specific signal acquisition and activation. These findings highlight the capability of LNW to facilitate comprehensive and accurate mapping of neuronal dynamics, thereby advancing brain-machine interfaces and neural prostheses.}, }
@article {pmid40393988, year = {2025}, author = {Garro, F and Fenoglio, E and Ceroni, I and Forsiuk, I and Canepa, M and Mozzon, M and Bruschi, A and Zippo, F and Laffranchi, M and De Michieli, L and Buccelli, S and Chiappalone, M and Semprini, M}, title = {An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {831}, pmid = {40393988}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiology ; *Electromyography ; Biomarkers ; Adult ; Movement ; }, abstract = {This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a reaching task with and without the assistance of an upper-limb exoskeleton. It was designed to explore electrophysiological biomarkers for assessing assistive technologies. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks. A custom-designed touch panel was built to standardize and simulate natural upper-limb movements relevant to daily activities. The dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. To provide an overview of data quality, we present subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency- domain decomposition for EMG. Beyond providing a methodology for evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.}, }
@article {pmid40393212, year = {2025}, author = {Lu, Q and Yi, M and Jiang, J}, title = {Bioelectronic nose for ultratrace odor detection via brain-computer interface with olfactory bulb electrode arrays.}, journal = {Biosensors &amp; bioelectronics}, volume = {285}, number = {}, pages = {117585}, doi = {10.1016/j.bios.2025.117585}, pmid = {40393212}, issn = {1873-4235}, abstract = {Rapid and accurate detection of hazardous volatile compounds is crucial for public health and environmental safety. While conventional methods, including electronic noses, typically exhibit detection thresholds in the parts-per-million (ppm) range, many harmful substances pose risks at parts-per-billion (ppb) concentrations or lower. To address this challenge, we leverage the exceptional sensitivity of the mammalian olfactory system, specifically that of Rattus norvegicus (lab rat), which has evolved to detect and discriminate a vast array of odors at extremely low concentrations. In this study, we developed a novel bio-hybrid system that integrates behavioral training with in vivo electrophysiological recordings from the olfactory bulb (OB). Rats were operantly conditioned to recognize target odors, namely TNT (2,4,6-trinitrotoluene), TNP (2,4,6-trinitrophenol), and chlorine gas (Cl2), at ppb levels. Concurrent with behavioral testing, we recorded neural activity from both the dorsal and ventral OB using a customdesigned, multi-channel electrode array optimized for the rat OB's cytoarchitecture. Electrophysiological data were decoded using a Support Vector Machine algorithm, achieving a mean accuracy of over 90 % in classifying odor identity at ppb concentrations based on OB activity patterns. These results demonstrate the feasibility of utilizing a brain-computer interface with the olfactory system to achieve ultratrace detection of hazardous substances. This bio-hybrid approach offers significantly enhanced sensitivity compared to existing electronic nose technologies, paving the way for highly effective environmental and biomedical sensing applications.}, }
@article {pmid40390719, year = {2025}, author = {Leung, ES and Mofatteh, M}, title = {Investigating the Feasibility and Safety of Osseointegration With Neural Interfaces for Advanced Prosthetic Control.}, journal = {Cureus}, volume = {17}, number = {4}, pages = {e82567}, pmid = {40390719}, issn = {2168-8184}, abstract = {Osseointegrated neural interfaces (ONI), particularly in conjunction with peripheral nerve interfaces (PNIs), have emerged as a promising advancement for intuitive neuroprosthetics. PNIs can decode neural signals and allow precise prosthetic movement control and bidirectional communication for haptic feedback, while osseointegration can address limitations of traditional socket-based prosthetics, such as poor stability, limited dexterity, and lack of sensory feedback. This review explores advancements in ONIs, including screw-fit and press-fit systems and their integration with PNIs for bidirectional communication. ONIs integrated with PNIs (OIPNIs) have shown improvements in signal fidelity, motor control, and sensory feedback compared to popular surface electromyography (sEMG) systems. Additionally, emerging technologies such as hybrid electrode designs (e.g., cuff and sieve electrode (CASE)) and regenerative peripheral nerve interfaces (RPNIs) show potential for enhancing selectivity and reducing complications such as micromotion and scarring. Despite these advances, challenges remain, including infection risk, electrode degradation, and variability in long-term signal stability. Osseointegration combined with advanced neural interfaces represents a transformative approach to prosthetic control, offering more natural and intuitive movement with sensory feedback. Further research is needed to address long-term biocompatibility, reduce surgical invasiveness, and explore emerging technologies such as machine learning for personalized ONI designs. The findings of this review underscore the potential of ONIs to enhance embodiment and quality of life for amputees and highlight current pitfalls and possible areas of improvement and future research.}, }
@article {pmid40389429, year = {2025}, author = {Karpowicz, BM and Ali, YH and Wimalasena, LN and Sedler, AR and Keshtkaran, MR and Bodkin, K and Ma, X and Rubin, DB and Williams, ZM and Cash, SS and Hochberg, LR and Miller, LE and Pandarinath, C}, title = {Stabilizing brain-computer interfaces through alignment of latent dynamics.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {4662}, pmid = {40389429}, issn = {2041-1723}, mesh = {*Brain-Computer Interfaces ; Animals ; *Motor Cortex/physiology ; Macaca mulatta ; Neural Networks, Computer ; Male ; Movement/physiology ; Humans ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high-performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks- to months-long timescales without any supervised recalibration.}, }
@article {pmid40387950, year = {2025}, author = {Wu, P and Zhu, J and He, Q and Wang, Z and Shi, L}, title = {Visual numerical cognition in pigeons: conformity to the Weber-Fechner law.}, journal = {Animal cognition}, volume = {28}, number = {1}, pages = {39}, pmid = {40387950}, issn = {1435-9456}, mesh = {Animals ; *Columbidae/physiology ; *Cognition ; *Visual Perception ; Male ; }, abstract = {As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber-Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber-Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution.}, }
@article {pmid40382989, year = {2025}, author = {Wang, T and Dai, Q and Xiong, W}, title = {Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {189}, number = {}, pages = {107573}, doi = {10.1016/j.neunet.2025.107573}, pmid = {40382989}, issn = {1879-2782}, abstract = {In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.}, }
@article {pmid40382679, year = {2025}, author = {Covelli, E and Filippi, C and Lazzerini, F and Tromboni, E and Tarentini, S and Pizzolante, S and Forli, F and Berrettini, S and Bruschini, L}, title = {Traditional and adaptive speech audiometry in single-sided deaf (SSD) subjects rehabilitated by bone conductive implants (BCI), quality of life and long-term utilization.}, journal = {Acta oto-laryngologica}, volume = {}, number = {}, pages = {1-7}, doi = {10.1080/00016489.2025.2504032}, pmid = {40382679}, issn = {1651-2251}, abstract = {BACKGROUND: Single-sided deafness (SSD) encompasses the presence of a profoundly deaf ear with a normal, contralateral one. Patients with SSD may have difficulty with speech intelligibility in noise and localizing sounds.<br><br>AIMS/OBJECTIVES: This retrospective study aims to evaluate the long-term effectiveness of bone conduction implant (BCI) in a group of patients with SSD.<br><br>MATERIAL AND METHODS: Audiologic benefit was assessed through conventional speech audiometry and adaptive Matrix test. Impact on quality of life was evaluated with the Glasgow Benefit Inventory (GBI) questionnaire. BCI usage data were also obtained from each subject.<br><br>RESULTS: Thirty-two patients were included. No statistically significant improvements were found at standard audiometric tests using BCI, but at Matrix test the mean SRT is reached at S/N -1.16&#x2009;dB without BCI and -2.07 with BCI with a statistically significant difference (p&#x2009;=&#x2009;0.026). The mean GBI score was 25.12, ranging from -8.3 to 47.2. Ten subjects (31%) discontinued the BCI use overtime.<br><br>CONCLUSIONS AND SIGNIFICANCE: Benefit assessment of BCI in SSD recipients can be difficult. Adaptive audiometric test could be useful. Quality of life measures seem to suggest potential 'beyond-auditory' benefits. SSD recipients can be inconsistent users of BCI.}, }
@article {pmid40382338, year = {2025}, author = {Zhou, S and Zhu, Y and Du, A and Niu, S and Du, Y and Yang, Y and Chen, W and Du, S and Sun, L and Liu, Y and Wu, H and Lou, H and Li, XM and Duan, S and Yang, H}, title = {A midbrain circuit mechanism for noise-induced negative valence coding.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {4610}, pmid = {40382338}, issn = {2041-1723}, support = {LR24C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Ventral Tegmental Area/physiology/cytology ; *Noise ; Mice ; *Inferior Colliculi/physiology/cytology ; GABAergic Neurons/physiology/metabolism ; Male ; Mice, Inbred C57BL ; Acoustic Stimulation ; *Emotions/physiology ; Geniculate Bodies/physiology ; *Mesencephalon/physiology ; Auditory Pathways/physiology ; Optogenetics ; Auditory Perception/physiology ; Female ; Dopamine/metabolism ; Neurons/physiology ; }, abstract = {Unpleasant sounds elicit a range of negative emotional reactions, yet the underlying neural mechanisms remain largely unknown. Here we show that glutamatergic neurons in the central inferior colliculus (CIC[glu]) relay noise information to GABAergic neurons in the ventral tegmental area (VTA[GABA]) via the cuneiform nucleus (CnF), encoding negative emotions in mice. In contrast, the CIC[glu]→medial geniculate (MG) canonical auditory pathway processes salient stimuli. By combining viral tracing, calcium imaging, and optrode recording, we demonstrate that the CnF acts downstream of CIC[glu] to convey negative valence to the mesolimbic dopamine system by activating VTA[GABA] neurons. Optogenetic or chemogenetic inhibition of any connection within the CIC[glu]→CnF[glu] → VTA[GABA] circuit, or direct excitation of the mesolimbic dopamine (DA) system is sufficient to alleviate noise-induced negative emotion perception. Our findings highlight the significance of the CIC[glu]→CnF[glu] → VTA[GABA] circuit in coping with acoustic stressors.}, }
@article {pmid40381460, year = {2025}, author = {Qi, G and Zhao, S and Yu, J and Li, P and Guan, W}, title = {Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver's EEG cognitive data.}, journal = {Accident; analysis and prevention}, volume = {219}, number = {}, pages = {108102}, doi = {10.1016/j.aap.2025.108102}, pmid = {40381460}, issn = {1879-2057}, abstract = {Without human participation in driving operations, the adoption of autonomous driving (AD) technology greatly enhances driving safety by reducing human errors. Even though AD can handle common scenarios properly, some exceptions still call for the human takeover with AD failing to engage due to the incomprehensible or intensely conflict situations that rarely occur. To help AD understand and recognize the disengagement scenarios effectively, this paper incorporates the human electroencephalogram (EEG) cognitive data into modeling and proposes a transfer learning framework to let AD absorb the integrative knowledge from the manual driving (MD). Several disengagement scenarios are designed using a driving simulator and EEG data are collected from both "drivers" in MD and "supervisors" in AD. A conditional maximum mean discrepancy (CMMD) function is introduced to identify the common brain activity characteristics, allowing the recognition model to be transferred from the cognitively demanding domain of MD to the less demanding domain of AD. The results indicate that the proposed model can achieve an 80 % recognition rate for typical disengagement scenarios, such as static obstacles, intersection conflict and vehicle cut-in, using only 30 % of AD training labels. The transferable common feature space from EEG data improves the recognition accuracy by 21.2 % compared with the model only using AD domain data. By accurately recognizing the type of disengagement scenarios, the AD system can activate appropriate safety mechanisms or provide more explicit takeover prompts, which could effectively reduce the risk of accidents due to delayed or incorrect takeovers.}, }
@article {pmid40380329, year = {2025}, author = {Chen, W and Chen, H and Jiang, W and Chen, C and Xu, M and Ruan, H and Chen, H and Yu, Z and Chen, S}, title = {Heart rate variability and heart rate asymmetry in adolescents with major depressive disorder during nocturnal sleep period.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {497}, pmid = {40380329}, issn = {1471-244X}, support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; }, mesh = {Humans ; *Depressive Disorder, Major/physiopathology ; *Heart Rate/physiology ; Adolescent ; Male ; Female ; Electrocardiography ; *Sleep/physiology ; Case-Control Studies ; }, abstract = {BACKGROUND: Although reduced heart rate variability (HRV) has been observed in adolescents with major depressive disorder (MDD), substantial between-study heterogeneity and conflicting outcomes exist. Moreover, few studies have investigated heart rate asymmetry (HRA) features despite the high sensitivity of nonlinear indices to heart rate fluctuations. This study aimed to investigate the variations in HRV measures, especially the nonlinear features of HRA, among adolescents with MDD during the nocturnal sleep period.<br><br>METHODS: Adolescents with MDD and healthy controls completed the clinical assessment of depressive symptom severity and sleep quality followed by a three-night sleep electrocardiogram (ECG) monitoring. Traditional time-domain and frequency-domain HRV measures, nonlinear HRA measures, and the prevalence of different HRA forms and HRA compensation were calculated.<br><br>RESULTS: A total of 61 participants with 154 nocturnal ECG time series were available for analysis. Vagally-mediated HRV measures, such as RMSSD, PNN50, and HF, as well as C1d were statistically lower in clinically depressed adolescents compared with healthy controls, whereas C2d was significantly higher. A substantial decrease in the prevalence of short-term HRA, long-term HRA, and the corresponding compensation effect were also observed. In contrast to the medium to large effect sizes observed in traditional HRV indices, nonlinear HRA features showed extremely large effect sizes in discriminating MDD (C1d: Cohen's d= -&#x2009;1.38; C2d: Cohen's d&#x2009;=&#x2009;1.11), and exhibited a statistical correlation with the severity of depression (C1d: rho = -&#x2009;0.269; C2d: rho&#x2009;=&#x2009;0.243). Moreover, there were no significant differences in the distributions of nocturnal HRA measures collected over various nights.<br><br>CONCLUSION: Adolescents with MDD suffered a significant decrease in vagal tone compared to healthy controls, and the features focusing on the directionality of heart rate variations may provide further information on cardiac autonomic activity associated with depression.}, }
@article {pmid40379686, year = {2025}, author = {Bom, MS and Brak, AMA and Raemaekers, M and Ramsey, NF and Vansteensel, MJ and Branco, MP}, title = {Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {804}, pmid = {40379686}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Child ; Adolescent ; Aged ; Adult ; Aged, 80 and over ; Middle Aged ; Young Adult ; Male ; Female ; }, abstract = {Functional Magnetic Resonance Imaging (fMRI) data is commonly used to map sensorimotor cortical organization and to localise electrode target sites for implanted Brain-Computer Interfaces (BCIs). Functional data recorded during motor and somatosensory tasks from both adults and children specifically designed to map and localise BCI target areas throughout the lifespan is rare. Here, we describe a large-scale dataset collected from 155 human participants while they performed motor and somatosensory tasks involving the fingers, hands, arms, feet, legs, and mouth region. The dataset includes data from both adults and children (age range: 6-89 years) performing a set of standardized tasks. This dataset is particularly relevant to study developmental patterns in motor representation on the cortical surface and for the design of paediatric motor-based implanted BCIs.}, }
@article {pmid40378852, year = {2025}, author = {Ding, W and Liu, A and Cheng, L and Chen, X}, title = {Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add9d1}, pmid = {40378852}, issn = {1741-2552}, abstract = {OBJECTIVE: Data augmentation has been demonstrated to improve the classification accuracy of deep learning (DL) models in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG) data. However, current data augmentation methods often rely on signal-level manipulations, which may result in significant distortion of EEG signals. To overcome this limitation, this study proposes a component-level data augmentation method called Masked Principal Component Representation (MPCR).<br><br>APPROACH: MPCR utilizes a principal component-based reconstruction approach, integrating a random masking strategy applied to principal component representations. Specifically, certain principal components are randomly selected and set to zero, thereby introducing random perturbations in the reconstructed samples. Furthermore, reconstructing samples via linear combinations of the remaining components effectively preserves the primary inherent structure of EEG signals. By expanding the input space covered by training samples, MPCR helps the trained model learn more robust features. To validate the effectiveness of MPCR, experiments are performed on two widely utilized public datasets.<br><br>MAIN RESULTS: Experimental results indicate that MPCR substantially enhances classification accuracy across diverse DL models. Additionally, compared to various state-of-the-art data augmentation approaches, MPCR demonstrates both greater performance and high compatibility.<br><br>SIGNIFICANCE: This study proposes a simple yet effective component-level data augmentation method, contributing valuable insights for advancing data augmentation methods in EEG-based BCIs.}, }
@article {pmid40377015, year = {2025}, author = {Yang, T and Zhang, D and Huang, H and Liu, F and Wu, J and Ma, X and Liu, S and Huang, M and Zhou, YD and Shen, Y}, title = {Astrocytic mGluR5-dependent calcium hyperactivity promotes amyloid-β pathology and cognitive impairment.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awaf186}, pmid = {40377015}, issn = {1460-2156}, abstract = {Astrocytic dysfunction is a crucial factor for the pathogenesis of Alzheimer's disease. Metabotropic glutamate receptor 5 (mGluR5) is ubiquitously expressed in the brain and is a key molecule that regulates synaptic transmission and plasticity. It has been shown that mGluR5 is elevated in astrocytes in Alzheimer's disease. However, it remains elusive how astrocytic mGluR5 contributes to the pathogenesis of Alzheimer's disease. Here, we first quantified a high expression level of astrocytic mGluR5 in the hippocampus of Alzheimer's disease brains and demonstrated that the expression of astrocytic mGluR5 was positively correlated with Alzheimer's disease progression in both humans and mice. Upregulating astrocytic mGluR5 in the CA1 area at an early stage accelerated, whereas downregulating these receptors rescued, Aβ pathology and cognitive impairment in Alzheimer's disease mice. Moreover, the activation of mGluR5 led to calcium hyperactivity in astrocytes, causing Aβ pathology progression due to dysregulated Aβ uptake and degradation in astrocytes. Importantly, attenuating astrocytic calcium hyperactivity in the hippocampal CA1 area in the prodromal phase ameliorated Aβ pathology and cognitive defects in Alzheimer's disease mice. Our findings thus reveal a fundamental contribution of astrocytic mGluR5 in presymptomatic Alzheimer's disease that may serve as a potential diagnostic and therapeutic target for early Alzheimer's disease pathogenesis.}, }
@article {pmid40374051, year = {2025}, author = {Wang, M and Wang, Y and Yang, Y}, title = {Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds.}, journal = {NeuroImage}, volume = {314}, number = {}, pages = {121243}, doi = {10.1016/j.neuroimage.2025.121243}, pmid = {40374051}, issn = {1095-9572}, abstract = {Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions.}, }
@article {pmid40373768, year = {2025}, author = {Luo, T and Liu, C and Cheng, T and Zhao, GQ and Huang, Y and Luan, JY and Guo, J and Liu, X and Wang, YF and Dong, Y and Xiao, Y and He, E and Sun, RZ and Chen, X and Chen, J and Ma, J and Megason, S and Ji, J and Xu, PF}, title = {Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers.}, journal = {Cell stem cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.stem.2025.04.011}, pmid = {40373768}, issn = {1875-9777}, abstract = {Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion ("ORDER"), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy.}, }
@article {pmid40372852, year = {2025}, author = {Lo, YT and Maggi, A and Wu, K and Zhong, H and Choi, W and Nguyen, TD and Abedi, A and Agyeman, K and Sakellaridi, S and Edgerton, VR and Kreydin, E and Lee, D and Sideris, C and Liu, CY and Christopoulos, VN}, title = {Exploring the Feasibility of Bidirectional Spinal Cord Machine Interface through Sensing and Stimulation of Axonal Bundles.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3570324}, pmid = {40372852}, issn = {1558-0210}, abstract = {Spinal cord injury (SCI) patients experience long-term deficits in motor and sensory functions. While brain-machine interface (BMI) has shown great promise for restoring neurological functions after SCI, spinal cord-machine interface (SCMI) offers unique advantages, such as more defined somatotopy and the compact organization of neural elements in the spinal cord. In the current study, we aim to demonstrate the feasibility of sensing and evoking compound action potentials (CAPs) via electrode implantation in spinal cord axonal bundles, an essential prerequisite for advancing toward SCMI development. To do so, we designed microelectrode arrays (MEA) optimized for recording and stimulation in the spinal cord. For sensory mapping, the MEAs were inserted into the lumbar dorsal column (i.e., the fasciculus gracilis) to determine somatotopic representations corresponding to tactile stimulation across lower body regions and assess proprioceptive signals with varying hip positions. For stimulations, at the L3 level, we delivered electrical pulses both rostrally, along ascending afferent tracts (dorsal column), and caudally, down descending corticospinal tract. We successfully captured axonal CAPs from the dorsal columns with high spatial precision that corresponded to known dermatomal somatotopy. Proprioceptive changes produced by abduction at the hip resulted in modulation of discharge frequency in the dorsal column axons. We demonstrated that stimulation pulses emitted by a caudally placed electrode could be propagated up the ascending fibers and be intercepted by a rostrally placed electrode array along the same axonal tracts. We also confirmed that electrical pulses can be directed down descending corticospinal tracts resulting in specific activations of lower limb muscles. These findings set a critical groundwork for developing closed-loop, bidirectional SCMI systems capable of sensing and modulating spinal cord activity.}, }
@article {pmid40371570, year = {2025}, author = {Ding, P and Tan, L and Pan, H and Gong, A and Nan, W and Fu, Y}, title = {The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.}, journal = {Brain connectivity}, volume = {}, number = {}, pages = {}, doi = {10.1089/brain.2024.0084}, pmid = {40371570}, issn = {2158-0022}, abstract = {Objectives: Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation. The study aims to explore reasons behind these concerns and propose directions for addressing them. Methods: A search conducted in the Web of Science database up to December 1, 2023, yielded 111 English articles, of which 80 were excluded based on predetermined criteria irrelevant to this study. The remaining 31 original studies were included in the literature review. A checklist was developed to assess the robustness and credibility of these 31 studies. Subsequently, these original studies were classified into electroencephalogram-based NF (EEG-NF) and functional magnetic resonance imaging-based NF (fMRI-NF) based on BCI type. Data regarding target brain regions, target signals, modulation protocols, control group types, assessment methods, data processing strategies, and reported outcomes were extracted and synthesized. Consensus theories from existing research and directions for future improvements in related studies were distilled. Results: Analysis of all included studies revealed that the average sample size of PTSD patients in EEG and fMRI NF studies was 17.4 (SD 7.13) and 14.6 (SD 6.37), respectively. Due to sample and neurofeedback training protocol constraints, 93% of EEG-NF studies and 87.5% of fMRI-NF studies used traditional statistical methods, with minimal utilization of basic machine learning (ML) methods and no studies utilizing deep learning (DL) methods. Apart from approximately 25% of fMRI NF studies supporting exploratory psychoregulatory strategies, the remaining EEG and fMRI studies lacked explicit NF modulation guidance. Only 13% of studies evaluated NF effectiveness methods involving signal classification, decoding during the NF process, and lacking in process monitoring and assessment means. Conclusion: In summary, NF holds promise as an adjunctive intervention technique for PTSD, potentially aiding in symptom alleviation for PTSD patients. However, improvements are necessary in the process evaluation mechanisms for PTSD-NF, clarity in NF modulation guidance, and development of ML/DL methods suitable for PTSD-NF with small sample sizes. To address these challenges, it is crucial to adopt more rigorous methodologies for monitoring NF, and future research should focus on the integration of advanced data analysis techniques to enhance the effectiveness and precision of PTSD-NF interventions. Impact Statement The implications of this study are to address the limited application of Neurofeedback training (NFT) in post-traumatic stress disorder (PTSD) research, where a significant portion of the approaches, foundational research, and conclusions lack consensus. There is a notable absence of retrospective statistical analyses on NFT interventions for PTSD. This study provides a comprehensive statistical analysis and discussion of existing research, offering valuable insights for future studies. The findings hold significance for researchers, clinicians, and practitioners in the field, providing a foundation for informed, evidence-based interventions for PTSD treatment.}, }
@article {pmid40370566, year = {2025}, author = {Hong, W and Ma, H and Yang, Z and Wang, J and Peng, B and Wang, L and Du, Y and Yang, L and Zhang, L and Li, Z and Huang, H and Zhu, D and Yang, B and He, Q and Wang, J and Weng, Q}, title = {Optineurin restrains CCR7 degradation to guide type II collagen-stimulated dendritic cell migration in rheumatoid arthritis.}, journal = {Acta pharmaceutica Sinica. B}, volume = {15}, number = {3}, pages = {1626-1642}, pmid = {40370566}, issn = {2211-3835}, abstract = {Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.}, }
@article {pmid40369268, year = {2025}, author = {Pan, S and Cai, Y and Liu, R and Jiang, S and Zhao, H and Jiang, J and Lin, Z and Liu, Q and Lu, H and Liang, S and Fan, W and Chen, X and Wu, Y and Wang, F and Chen, Z and Hu, R and Yang, L}, title = {Targeting 5-HT to Alleviate Dose-Limiting Neurotoxicity in Nab-Paclitaxel-Based Chemotherapy.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40369268}, issn = {1995-8218}, abstract = {Chemotherapy-induced peripheral neurotoxicity (CIPN) is a severe dose-limiting adverse event of chemotherapy. Presently, the mechanism underlying the induction of CIPN remains unclear, and no effective treatment is available. In this study, through metabolomics analyses, we found that nab-paclitaxel therapy markedly increased serum serotonin [5-hydroxtryptamine (5-HT)] levels in both cancer patients and mice compared to the respective controls. Furthermore, nab-paclitaxel-treated enterochromaffin (EC) cells showed increased 5-HT synthesis, and serotonin-treated Schwann cells showed damage, as indicated by the activation of CREB3L3/MMP3/FAS signaling. Venlafaxine, an inhibitor of serotonin and norepinephrine reuptake, was found to protect against nerve injury by suppressing the activation of CREB3L3/MMP3/FAS signaling in Schwann cells. Remarkably, venlafaxine was found to significantly alleviate nab-paclitaxel-induced CIPN in patients without affecting the clinical efficacy of chemotherapy. In summary, our study reveals that EC cell-derived 5-HT plays a critical role in nab-paclitaxel-related neurotoxic lesions, and venlafaxine co-administration represents a novel approach to treating chronic cumulative neurotoxicity commonly reported in nab-paclitaxel-based chemotherapy.}, }
@article {pmid40368962, year = {2025}, author = {Dias, C and Sousa, T and Cruz, A and Costa, D and Mouga, S and Castelhano, J and Pires, G and Castelo-Branco, M}, title = {A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {16671}, pmid = {40368962}, issn = {2045-2322}, support = {10.54499/UI/BD/150832/2021, https://doi.org/10.54499/UI/BD/150832/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; CEEC: 2021.01469.CEECIND//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PTDC/EEI-AUT/30935/2017;//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; UIDB/4950/2020, https://doi.org/10.54499/UIDB/04950/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PT/FB/BL-2018-306//Funda&#xe7;&#xe3;o Bial/ ; CAIXA Impulse 2024//'la Caixa' Foundation/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Autistic Disorder/physiopathology ; *Theta Rhythm/physiology ; Female ; Adult ; *Executive Function/physiology ; Electroencephalography ; Young Adult ; Memory, Short-Term/physiology ; *Frontal Lobe/physiopathology ; }, abstract = {Midfrontal theta oscillations have been linked to executive function, yet their role in autism-where this function is often compromised-remains unclear. We hypothesized that preparatory increases in theta power may help normalize performance in autism. To test this, we used a challenging interactive executive function task designed to impose a high working memory load and require constant error monitoring. An electroencephalogram (EEG)-based brain-computer interface (BCI) was used to maximize cognitive load and engagement. Neural activity from autistic and non-autistic adults was compared while participants were asked to mentally reverse pseudowords (engaging working memory) and write them using the BCI, which provided real-time performance feedback (maximizing error monitoring). The study focused on theta power modulation during the preparatory (pre-response) and feedback (post-response) periods but also explored the role of posterior alpha oscillations. Results showed similar task performance between groups, but distinct recruitment of brain resources, particularly during the preparatory period. The finding of an increased preparatory theta in autism favors the hypothesis of compensatory recruitment of cognitive control and attentional mechanisms to achieve accurate results.}, }
@article {pmid40367961, year = {2025}, author = {Partovi, A and Grayden, DB and Burkitt, AN}, title = {POC-CSP: A Novel Parameterised and Orthogonally-Constrained Neural Network layer for learning Common Spatial Patterns (CSP) in EEG signals.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add8bc}, pmid = {40367961}, issn = {1741-2552}, abstract = {OBJECTIVE: Common Spatial Patterns (CSP) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal was to transform CSP into a trainable machine learning model that can learn from data, be regularized, and be integrated into end-to-end classification networks.<br><br>APPROACH: We developed a novel Parameterised and Orthogonally-Constrained Neural Network layer for learning Common Spatial Patterns (POC-CSP) that maintains CSP's mathematical properties while allowing trainable weights. The layer uses parameterization based on Lie Group theory to convert constrained optimization into unconstrained optimization, enabling integration with standard neural network training methods. We evaluated the approach on two public motor imagery datasets, focusing on both subject-specific and multi-subject paradigms.<br><br>MAIN RESULTS: POC-CSP outperformed both conventional CSP and existing neural network implementations in subject-specific classification tasks. In a novel multi-subject paradigm, POC-CSP achieved superior generalization. When fine-tuned with just 50% of a new subject's data, POC-CSP achieved 0.95 average accuracy across subjects, substantially outperforming subject-specific models trained with more data.<br><br>SIGNIFICANCE: These findings demonstrate that combining CSP's proven effectiveness with neural networks' flexibility can significantly improve EEG signal processing performance. The ability to generalize across subjects and achieve high accuracy with minimal subject-specific training data makes POC-CSP particularly valuable for practical brain-computer interface applications, where collecting large amounts of training data from each new user is often impractical or unfeasible.}, }
@article {pmid40367953, year = {2025}, author = {Xu, X and Drougard, N and Roy, RN}, title = {Does topological data analysis work for EEG-based brain-computer interfaces?.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add8bd}, pmid = {40367953}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are systems that establish a direct communication pathway with machines through brain activity only, recorded for example via electroencephalography (EEG). Topological data analysis (TDA) extracts topological features of the shape of the data and showed promising results in various applications. However, the work evaluating TDA systematically on EEG-based BCI is rare. Our study aims to fill this gap.<br><br>APPROACH: The hypothesis is that the&#xD;topology of the EEG dynamics is different under different mental states so that the&#xD;topological features are discriminant. By adopting a dynamical system point of view,&#xD;the non-stationary nature of EEG is respected. In practice, topological information is&#xD;encoded by the persistence diagram. To turn it into a feature vector, some classical&#xD;vector- and function-based representations are used. Each feature vector is then&#xD;classified by several basic linear and non-linear classifiers.<br><br>MAIN RESULTS: A benchmark&#xD;comparing TDA with the gold standard methods was established on 3 publicly available&#xD;datasets (2 active BCI datasets based on motor-imagery, 1 passive BCI dataset for&#xD;mental workload estimation). TDA had significantly lower performance in intra-&#xD;subject classification, yet comparable and sometimes higher performance in inter-&#xD;subject classification. The persistence consistently outperformed all other topological&#xD;features. We explained theoretically the link between persistence and spectral power&#xD;and demonstrated it experimentally.<br><br>SIGNIFICANCE: To our knowledge, this is the first&#xD;study that evaluates TDA in both intra- and inter-subject classification on various&#xD;types of datasets. Insights on the connection between persistence and classical EEG&#xD;features are also given for the first time.}, }
@article {pmid40367199, year = {2025}, author = {Yashinski, M}, title = {Neuroprosthesis converts brain activity to speech.}, journal = {Science robotics}, volume = {10}, number = {102}, pages = {eady7192}, doi = {10.1126/scirobotics.ady7192}, pmid = {40367199}, issn = {2470-9476}, mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Neural Prostheses ; }, abstract = {A neuroprosthesis decodes short bits of neural activity and synthesizes speech synchronously with a user's vocal intent.}, }
@article {pmid40366622, year = {2025}, author = {Yang, L and Li, H and Wang, X}, title = {Psilocybin and Obsessive-Compulsive Disorder: Exploring New Therapeutic Horizons.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40366622}, issn = {1995-8218}, }
@article {pmid40366280, year = {2025}, author = {Liu, M and Chang, S and Chen, M and Li, P and Roe, AW and Hu, JM}, title = {How shape information is coded by V4 cortical response of Macaque Monkey.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00520.2024}, pmid = {40366280}, issn = {1522-1598}, support = {32471052//MOST | National Natural Science Foundation of China (NSFC)/ ; 32100802//MOST | National Natural Science Foundation of China (NSFC)/ ; }, abstract = {Previous neural recording studies have shown that monkey V4 can process the shape information across populations of neurons. The responses recorded from each single neuron make it possible to retrieve shape information. However, these studies did not fully characterize the spatial distribution of activity in the cortex. There are multiple types of functional columns (orientation, curvature) in V4; how do these structures respond to different shapes? Here, with intrinsic optical imaging, we explored the cortical responses of V4 to contours (straight and curved) and shapes (circle and square). We found that in V4, the response of neurons to different shapes is highly dependent on the compositional features contained in the shape. A specific local network would have a higher response magnitude to its corresponding shape than other shapes. Meanwhile, the cortical response of V4 exhibits a tolerance to the shift of stimulus location. Our results suggest that two essential cortical response features in V4 are the specificity of the activated response pattern in the cortex and tolerance to the stimulus location variance. These features can help decode shape information from imaging results.}, }
@article {pmid40364497, year = {2025}, author = {Kim, JH and Nam, H and Won, D and Im, CH}, title = {Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.}, journal = {Experimental neurobiology}, volume = {}, number = {}, pages = {}, doi = {10.5607/en25011}, pmid = {40364497}, issn = {1226-2560}, abstract = {Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binary classification tasks were conducted to identify the valence or arousal state of each participant based on a ten-fold cross-validation strategy. The results indicated that the application of DG methods consistently enhanced classification accuracy across datasets. In one dataset, TSception combined with VREx achieved the highest performance for both valence and arousal classifications. In the other dataset, TSception with VREx still yielded the highest valence classification accuracy, while TSception combined with GroupDRO showed the best arousal classification performance among the twelve models, slightly outperforming TSception with VREx. These findings underscore the potential of DG approaches to mitigate distributional shifts caused by intersubject and intersession variabilities to implement robust subject-independent EEG-based emotion recognition systems.}, }
@article {pmid40363359, year = {2025}, author = {Deng, X and Huo, H and Ai, L and Xu, D and Li, C}, title = {A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {9}, pages = {}, doi = {10.3390/s25092922}, pmid = {40363359}, issn = {1424-8220}, support = {No. XTZW2024-KF02//Chongqing Key Laboratory of Germplasm Innovation 755 and Utilization of Native Plants under Grant/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Signal-To-Noise Ratio ; Movement/physiology ; Deep Learning ; Algorithms ; }, abstract = {Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.}, }
@article {pmid40363182, year = {2025}, author = {Hougaard, BI and Knoche, H and Kristensen, MS and Jochumsen, M}, title = {Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {9}, pages = {}, doi = {10.3390/s25092742}, pmid = {40363182}, issn = {1424-8220}, support = {22357//VELUX FONDEN/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Video Games ; Aged ; Adult ; *Stroke/physiopathology ; User-Computer Interface ; }, abstract = {Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain-computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users' experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients' perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal.}, }
@article {pmid40361409, year = {2025}, author = {Marín-Liébana, S and Llor, P and Serrano-García, L and Fernández-Murga, ML and Comes-Raga, A and Torregrosa, D and Pérez-García, JM and Cortés, J and Llombart-Cussac, A}, title = {Gene Expression Signatures for Guiding Initial Therapy in ER+/HER2- Early Breast Cancer.}, journal = {Cancers}, volume = {17}, number = {9}, pages = {}, doi = {10.3390/cancers17091482}, pmid = {40361409}, issn = {2072-6694}, abstract = {In triple-negative (TNBC) and human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients, neoadjuvant systemic therapy is the standard recommendation for tumors larger than 2 cm. Monitoring the response to primary systemic therapy allows for the assessment of treatment effects, the need for breast-conserving surgery (BCS), and the achievement of pathological complete responses (pCRs). In estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer, the benefit of neoadjuvant strategies is controversial, as they have shown lower tumor downstaging and pCR rates compared to other breast cancers. In recent decades, several gene expression assays have been developed to tailor adjuvant treatments in ER+/HER2- early breast cancer (EBC) to identify the patients that will benefit the most from adjuvant chemotherapy (CT) and those at low risk who could be spared from undergoing CT. It is still a challenge to identify patients who will benefit from neoadjuvant systemic treatment (CT or endocrine therapy (ET)). Here, we review the published data on the most common gene expression signatures (MammaPrint (MP), BluePrint (BP), Oncotype Dx, PAM50, the Breast Cancer Index (BCI), and EndoPredict (EP)) and their ability to predict the response to neoadjuvant treatment, as well as the possibility of using them on core needle biopsies. Additionally, we review the changes in the gene expression signatures after neoadjuvant treatment, and the ongoing clinical trials related to the utility of gene expression signatures in the neoadjuvant setting.}, }
@article {pmid40360495, year = {2025}, author = {Li, J and Mo, D and Hu, J and Wang, S and Gong, J and Huang, Y and Li, Z and Yuan, Z and Xu, M}, title = {PEDOT:PSS-based bioelectronics for brain monitoring and modulation.}, journal = {Microsystems &amp; nanoengineering}, volume = {11}, number = {1}, pages = {87}, pmid = {40360495}, issn = {2055-7434}, support = {30802-110690303//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; 2021ZD0204300//National Science Foundation of China | Major Research Plan/ ; MYRGGRG2023-00038-FHS//Universidade de Macau (University of Macau)/ ; 28709-312200502501//Beijing Normal University (BNU)/ ; }, abstract = {The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain-computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.}, }
@article {pmid40360243, year = {2025}, author = {Schurzig, D and Iseke, R and Maier, H and Prenzler, NK and Lenarz, T and Ghoncheh, M}, title = {Clinical Evidence on the Influence of Implant Position onto Maximum Output with the Bonebridge Bone Conduction Implant.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {}, number = {}, pages = {}, doi = {10.1097/MAO.0000000000004533}, pmid = {40360243}, issn = {1537-4505}, abstract = {HYPOTHESIS: In bone conduction implantation, the position of the implant influences the audiological benefit of the patient.<br><br>BACKGROUND: One way of treating hearing loss is the implantation of bone conduction implants (BCIs), which effectively transmit vibrations through the skull bone to the cochlea given that the implant transducer is securely fixated. Laboratory research on the efficacy of bone conduction sound transmission found that a closer proximity of the transducer to the ipsilateral cochlea yields significantly higher cochlear promontory vibrations and hence, higher stimulation efficacy. Up to now, this finding has not been reproduced using clinical data such as the functional or effective gain.<br><br>METHODS: The present, retrospective study was conducted on a cohort of 28 BCI patients to correlate the implantation site of the BC transducer, derived from clinical postoperative imaging and defined in a standardized coordinate system, with maximum output values that are exclusively based on a novel calculation method only employing clinical audiological data.<br><br>RESULTS: It could be shown that the efficacy of BCI stimulation is in fact correlated with the transducer distance to the cochlea, and that this correlation is frequency dependent. Furthermore, the longitudinal distance of the transducer and the ipsilateral external auditory canal is negatively correlated with the maximal output while the sagittal distance is not.<br><br>CONCLUSION: The present study is hence the first one to clinically demonstrate the significance of BCI placement for maximizing patient benefit, which should be considered during the preoperative planning of bone conduction implantation.}, }
@article {pmid40359554, year = {2025}, author = {Liu, MY and Fang, MZ and Zhang, BH and Dang, CX and Zhang, YS and Wu, L and Liu, B and Li, Z}, title = {Bibliometric analysis of brain-computer interface research in spinal cord injury: current landscape and future directions.}, journal = {International journal of surgery (London, England)}, volume = {}, number = {}, pages = {}, doi = {10.1097/JS9.0000000000002475}, pmid = {40359554}, issn = {1743-9159}, }
@article {pmid40358727, year = {2025}, author = {Aamir, A and Siddiqui, M}, title = {Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation.}, journal = {World journal of urology}, volume = {43}, number = {1}, pages = {301}, pmid = {40358727}, issn = {1433-8726}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; *Urinary Bladder, Neurogenic/therapy/physiopathology ; *Lumbosacral Plexus ; }, abstract = {Sacral nerve stimulation (SNS) and brain-computer interfaces (BCI) are emerging neuromodulation therapies that offer innovative solutions for chronic neurological disorders. SNS, primarily used in the management of conditions such as urinary incontinence and chronic pelvic pain, demonstrates significant therapeutic potential. In contrast, BCIs are rapidly advancing in their ability to restore lost motor functions and improve the quality of life of patients with severe neurological impairments, such as spinal cord injury and stroke. The integration of SNS and BCI technologies presents a promising avenue for enhancing neuromodulation outcomes by leveraging the potential of both systems. This article explores the combined operation of SNS and BCI, addressing current challenges, future directions, and the potential for these combined therapies to revolutionise the field of functional neuromodulation.}, }
@article {pmid40358723, year = {2025}, author = {Bénard, A and Maliia, DM and Yochum, M and Köksal-Ersöz, E and Houvenaghel, JF and Wendling, F and Sauleau, P and Benquet, P}, title = {Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.}, journal = {Brain topography}, volume = {38}, number = {4}, pages = {43}, pmid = {40358723}, issn = {1573-6792}, support = {855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Computer Simulation ; *Rest/physiology ; Adult ; Scalp/physiology ; Male ; *Brain Waves/physiology ; Female ; }, abstract = {Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.}, }
@article {pmid40356632, year = {2025}, author = {Liao, XY and Jiang, YE and Xu, RJ and Qian, TT and Liu, SL and Che, Y}, title = {A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1539736}, pmid = {40356632}, issn = {1664-2295}, abstract = {BACKGROUND: Electroencephalography (EEG) has become an indispensable tool in stroke research for real-time monitoring of neural activity, prognosis prediction, and rehabilitation support. In recent decades, EEG applications in stroke research have expanded, particularly in areas like brain-computer interfaces (BCI) and neurofeedback for motor recovery. However, a comprehensive analysis of research trends in this domain is currently unavailable.<br><br>METHODS: The study collected data from the Web of Science Core Collection database, selecting publications related to stroke and EEG from 2005 to 2024. Visual analysis tools such as VOSviewer and CiteSpace were utilized to build knowledge maps of the research field, analyzing the distribution of publications, authors, institutions, journals, and collaboration networks. Additionally, co-occurrence, clustering, and burst detection of keywords were analyzed in detail.<br><br>RESULTS: A total of 2,931 publications were identified, indicating a consistent increase in EEG research in stroke, with significant growth post-2017. The United States, China, and Germany emerged as the leading contributors, with high collaboration networks among Western institutions. Key research areas included signal processing advancements, EEG applications in seizure risk and consciousness disorder assessment, and EEG-driven rehabilitation techniques. Notably, recent studies have focused on integrating EEG with machine learning and multimodal data for more precise functional evaluations.<br><br>CONCLUSION: The findings reveal that EEG has evolved from a diagnostic tool to a therapeutic support platform in the context of stroke care. The advent of deep learning and multimodal integration has positioned EEG for expanded applications in personalized rehabilitation. It is recommended that future studies prioritize interdisciplinary collaboration and standardized EEG methodologies in order to facilitate clinical adoption and enhance translational potential in stroke management.}, }
@article {pmid40356046, year = {2025}, author = {Chen, S and Hu, J and Zhang, D and Li, Z and Zheng, Z and Gui, S and He, N}, title = {Preparation and hemostatic evaluation of carboxymethyl chitosan/gelatin/clinodiside a composite sponges.}, journal = {Journal of biomaterials science. Polymer edition}, volume = {}, number = {}, pages = {1-21}, doi = {10.1080/09205063.2025.2499285}, pmid = {40356046}, issn = {1568-5624}, abstract = {In trauma resuscitation, rapid hemostasis is a top priority for rescuing patients from the risk of hemorrhagic shock and infection. Traditional hemostatic materials are not effective for hemostasis and have some limitations. We added "Duanxue Liu" saponin A (Clinodiside A) to a hemostatic sponge based on carboxymethyl chitosan (CMCS) and gelatin to improve its hemostatic effect. Clinodiside A has hemostatic, anti-inflammatory and antibacterial effects, and its preparation into a sponge can help to improve the coagulation ability, prolong the action time of the drug, increase the bioavailability and improve the stability of the drug. The results showed that the prepared hemostatic sponge had a honeycomb porous structure, strong shape recovery ability, good water absorption and porosity, low hemolysis rate and no obvious cytotoxicity. The results of in vitro coagulation test showed that the coagulation time of GOC, GOC-1, GOC-2 and GOC-3 sponges was shorter than that of the control group, and the BCI index was much lower than that of the commercially available sponges. In the tail-breaking experiment of SD rats, GOC-3 showed the lowest blood loss of 0.2549 g and the hemostasis time of 55 s. In the experiment of rabbit ear artery, GOC-3 showed the lowest blood loss of 98.75 mg and the hemostasis time of 95 s. This indicates that the Clinique A hemostatic sponges have highly efficient hemostatic properties. Therefore, the prepared CMCS/Gel/Clinodiside A sponge has a good application prospect as a hemostatic dressing.}, }
@article {pmid40355527, year = {2025}, author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S}, title = {Author Correction: A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {4381}, doi = {10.1038/s41467-025-59792-1}, pmid = {40355527}, issn = {2041-1723}, }
@article {pmid40355001, year = {2025}, author = {Ognard, J and El Hajj, G and Verma, O and Ghozy, S and Kadirvel, R and Kallmes, DF and Brinjikji, W}, title = {Advances in endovascular brain computer interface: Systematic review and future implications.}, journal = {Journal of neuroscience methods}, volume = {420}, number = {}, pages = {110471}, doi = {10.1016/j.jneumeth.2025.110471}, pmid = {40355001}, issn = {1872-678X}, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) translate neural activity into real-world commands. While traditional invasive BCIs necessitate craniotomy, endovascular BCIs offer a minimally invasive alternative using the venous system for electrode placement.<br><br>NEW METHOD: This systematic review evaluates the technical feasibility, safety, and clinical outcomes of endovascular BCIs, discussing their future implications. A systematic review was conducted per PRISMA guidelines. The search spanned PubMed, Web of Science, and Scopus databases using keywords related to neural interfaces and endovascular approaches. Studies were included if they reported on endovascular BCIs in preclinical or clinical settings. Dual independent screening and extraction focused on electrode material, recording capabilities, safety parameters, and clinical efficacy.<br><br>RESULTS: From 1385 initial publications, 26 met the inclusion criteria. Seventeen studies investigated the Stentrode device. Among the 24 preclinical studies, 16 used ovine or rodent models, and 9 addressed engineering or simulation aspects. Two clinical studies reported six ALS patients successfully using an endovascular BCI for digital communication. Preclinical data established the endovascular ovine model, demonstrating stable neural recordings and vascular changes with long-term implantation. Key challenges include thrombosis risk, long-term electrode stability, and anatomical variability.<br><br>Endovascular BCI reduced invasiveness, improved safety profiles, with comparable neural recording fidelity to invasive methods, and promising preliminary clinical outcomes in severely paralyzed patients.<br><br>CONCLUSIONS: Early results are promising, but clinical data remain scarce. Further research is needed to optimize signal processing, enhance electrode biocompatibility, and refine endovascular procedures for broader clinical applications.}, }
@article {pmid40354812, year = {2025}, author = {Poli, R and Mercimek, ACC and Cinel, C}, title = {Novel Sequential BCI Speller based on ERPs and Event-Related Slow Cortical Potentials.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add772}, pmid = {40354812}, issn = {1741-2552}, abstract = {One of the most effective Brain-Computer Interfaces (BCI) spellers, Donchin and Farwell's matrix speller, uses visual stimulus presentation and the oddball effect, eliciting P300 event-related potentials to rare and randomly presented stimuli of interest. Although proposed almost 4 decades ago, most BCI spellers still rely on this principle and the original matrix speller design although some of the issues that affect oddball spellers have progressively been addressed over the years with significant, but very incremental, performance improvements. Farwell and Donchin seminal paper suggested the future possibility of abandoning the oddball paradigm, for a regular/periodic presentation pattern which they predicted might produce a Contingent Negative Variation (CNV) thus improve speller performance. However, this has never been investigated. Building on our past research on a BCI for cursor control which adopted a periodic stimulation protocol, here we explore whether a periodic presentation pattern could be a viable alternative to the oddball paradigm in a BCI speller. Approach. We tested the periodic presentation principle in a BCI speller where 36 letters are organised around a circle and are highlighted sequentially, and compared it to the original matrix speller at two stimulus presentation rates. Main Results. Our periodic speller produces not only clear P300s, but also equally clear CNVs, as postulated by Farwell and Donchin, as well as other Slow Cortical Potentials (SCPs). At the higher stimulation rate, this leads to significantly higher AUC, classification accuracy, ITR and utility w.r.t. Donchin's speller. Significance. Our findings suggest that periodic stimulation can not only produce clear P300s but also a variety of event-related SCPs, leading to significant performance improvements over Donchin's paradigm. This work opens new avenues for BCI spelling where ERPs are combined with naturally-triggered (rather than trained) SCPs, that will hopefully result in more efficient communication systems for individuals with severe motor impairments.}, }
@article {pmid40354807, year = {2025}, author = {Ladouce, S and Torre-Tresols, JJ and Le Goff, K and Dehais, F}, title = {EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add771}, pmid = {40354807}, issn = {1741-2552}, abstract = {Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies. Approach: Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N = 16) during two sessions of a 45-minute sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent. Main results: Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience. Significance: This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving. .}, }
@article {pmid40353311, year = {2025}, author = {Shah, AM}, title = {Unlocking Naturalistic Speech With Brain-Computer Interface.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.15021}, pmid = {40353311}, issn = {1525-1594}, abstract = {Novel speech brain-computer interface poses the ability to decode detected neural signals in nearly real time. This decreases brain-to-voice latency and has the opportunity to restore naturalistic communication. Trial Registration: ClinicalTrials.gov: NCT03698149.}, }
@article {pmid40352906, year = {2025}, author = {Besharat, A and Samadzadehaghdam, N and Ghadiri, T}, title = {A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1544452}, doi = {10.3389/fnins.2025.1544452}, pmid = {40352906}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) rely on the brain's response to visual stimuli. However, accurately recognizing target frequencies using training-based methods remains challenging due to the time-consuming calibration sessions required by subject-specific training methods.<br><br>METHOD: To address this limitation, this study proposes a novel hybrid method called Hybrid task-related component and canonical correlation analysis (H-TRCCA). In the training phase, four spatial filters are derived using canonical correlation analysis (CCA) to maximize the correlation between the training data and reference signals. Additionally, a spatial filter is also computed using task-related component analysis (TRCA). In the test phase, correlation coefficients obtained from the CCA method are clustered using the k-means++ clustering algorithm. The cluster with the highest average correlation identifies the candidate stimuli. Finally, for each candidate, the correlation values are summed and combined with the TRCA-based correlation coefficients.<br><br>RESULTS: The H-TRCCA algorithm was validated using two publicly available benchmark datasets. Experimental results using only two training trials per frequency with 1s data length showed that H-TRCCA achieved average accuracies of 91.44% for Dataset I and 80.46% for Dataset II. Additionally, it achieved maximum average information transfer rates of 188.36 bits/min and 139.96 bits/min for Dataset I and II, respectively.<br><br>DISCUSSION: Remarkably H-TRCCA achieves comparable performance to other methods that require five trials, utilizing only two or three training trials. The proposed H-TRCCA method outperforms state-of-the-art techniques, showing superior performance and robustness with limited calibration data.}, }
@article {pmid40352453, year = {2025}, author = {Li, Y and Mei, Z and Liu, Z and Li, J and Sun, G and Ong, MEH and Chen, J and Fan, H and Cao, C}, title = {Cardiometabolic multimorbidity and the risk of sudden cardiac death among geriatric community dwellers using longitudinal EHR-derived data.}, journal = {Frontiers in endocrinology}, volume = {16}, number = {}, pages = {1515495}, doi = {10.3389/fendo.2025.1515495}, pmid = {40352453}, issn = {1664-2392}, mesh = {Humans ; Aged ; Male ; Female ; *Multimorbidity ; *Electronic Health Records/statistics &amp; numerical data ; Retrospective Studies ; Aged, 80 and over ; China/epidemiology ; *Death, Sudden, Cardiac/epidemiology/etiology ; *Independent Living/statistics &amp; numerical data ; Risk Factors ; Longitudinal Studies ; *Cardiovascular Diseases/epidemiology ; Prevalence ; *Metabolic Diseases/epidemiology ; }, abstract = {BACKGROUND: Cardiometabolic multimorbidity (CMM) has increased globally in recent years, especially among geriatric community dwellers. However, it is currently unclear how SCD risk is impacted by CMM in older adults. This study aimed to examine the associations between CMM and SCD among geriatric community dwellers in a province of China.<br><br>METHODS: This study was a retrospective, population-based cohort design based on electronic health records (EHRs) of geriatric community dwellers (&#x2265;65 years old) in four towns of Tianjin, China. 55,130 older adults were included in our study. Older adults were categorized into different CMM patterns according to the cardiometabolic disease (CMD) status at baseline. The count of CMDs was also entered as a continuous variable to examine the potential additive effect of CMM on SCD. Cox proportional hazard models were used to evaluate associations between CMM and SCD. The results are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).<br><br>RESULTS: The prevalence of CMM was approximately 25.3% in geriatric community dwellers. Among participants with CMM, hypertension and diabetes was the most prevalent combination (9,379, 17.0%). The highest crude mortality rates for SCD were 7.5 (2.9, 19.1) per 1000 person-years in older adults with hypertension, coronary heart disease, diabetes and stroke (HR, 4.496; 95% CI, 1.696, 11.917), followed by those with hypertension, coronary heart disease, and stroke (HR, 3.290; 95% CI, 1.056, 10.255). The risks of SCD were significantly increased with increasing numbers of CMDs (HR, 1.787; 95% CI, 1.606, 1.987). The demographic, risk factors, serum measures and ECG-adjusted HR for SCD was 1.488 (1.327, 1.668) for geriatric community dwellers with an increasing number of CMDs.<br><br>CONCLUSION: The risk of SCD varied by the pattern of CMM, and increased with increasing number of CMM among geriatric community dwellers.}, }
@article {pmid40351848, year = {2025}, author = {Jin, X and Yuan, Y and Chang, C and Wu, X and Tan, X and Liu, Z}, title = {Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.}, journal = {Digital health}, volume = {11}, number = {}, pages = {20552076251341163}, pmid = {40351848}, issn = {2055-2076}, abstract = {OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.<br><br>METHODS: To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.<br><br>RESULTS: Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.<br><br>CONCLUSION: The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.}, }
@article {pmid40351570, year = {2025}, author = {Sercek, I and Sampathila, N and Tasci, I and Ekmekyapar, T and Tasci, B and Barua, PD and Baygin, M and Dogan, S and Tuncer, T and Tan, RS and Acharya, UR}, title = {A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {71}, pmid = {40351570}, issn = {1871-4080}, abstract = {Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.}, }
@article {pmid40350042, year = {2025}, author = {Xie, C and Wang, L and Yang, J and Guo, J}, title = {A subject transfer neural network fuses Generator and Euclidean Alignment for EEG-based motor imagery classification.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {110483}, doi = {10.1016/j.jneumeth.2025.110483}, pmid = {40350042}, issn = {1872-678X}, abstract = {BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the significant difference in EEG signals among individuals hinders users from further utilizing the BCI system.<br><br>NEW METHOD: Addressing this difference and improving BCI classification accuracy remain key challenges. In this paper, we propose a transfer learning model based on deep learning to transfer the data distribution from the source domain to the target domain, named a subject transfer neural network combining the Generator with Euclidean alignment (ST-GENN). It consists of three parts: 1) Align the original EEG signals in the Euclidean space; 2) Send the aligned data to the Generator to obtain the transferred features; 3) Utilize the Convolution-attention-temporal (CAT) classifier to classify the transferred features.<br><br>RESULTS: The model is validated on BCI competition IV 2a, BCI competition IV 2b and SHU datasets to evaluate its classification performance, and the results are 82.85%, 86.28% and 67.2% for the three datasets, respectively.<br><br>The results have been shown to be robust to subject variability, with the average accuracy of the proposed method outperforming baseline algorithms by ranging from 2.03% to 15.43% on the 2a dataset, from 0.86% to 10.16% on the 2b dataset and from 3.3% to 17.9% on the SHU dataset.<br><br>The advantage of our model lies in its ability to effectively transfer the experience and knowledge of the source domain data to the target domain, thus bridging the gap between them. Our method can improve the practicability of MI-BCI systems.}, }
@article {pmid40349743, year = {2025}, author = {Dong, T and Lee, HH and Zang, H and Lee, H and Tian, Q and Wan, L and Fan, Q and Huang, SY}, title = {In Vivo Cortical Microstructure Mapping Using High-Gradient Diffusion MRI Accounting for Intercompartmental Water Exchange Effects.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121258}, doi = {10.1016/j.neuroimage.2025.121258}, pmid = {40349743}, issn = {1095-9572}, abstract = {In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves cortical microstructure mapping of the in vivo human brain with the evaluation of water exchange effects. This approach potentially provides a more appropriate description of in vivo cortical microstructure for improving data interpretation in future neurobiological studies.}, }
@article {pmid40348851, year = {2025}, author = {Sun, G and Yu, C and Cai, R and Li, M and Fan, L and Sun, H and Lyu, C and Lin, Y and Gao, L and Wang, KH and Li, X}, title = {Neural representation of self-initiated locomotion in the secondary motor cortex of mice across different environmental contexts.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {725}, pmid = {40348851}, issn = {2399-3642}, support = {32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170991//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Motor Cortex/physiology ; *Locomotion/physiology ; Mice ; Male ; *Neurons/physiology ; Mice, Inbred C57BL ; Environment ; }, abstract = {The secondary motor cortex (M2) plays an important role in the adaptive control of locomotor behaviors. However, it is unclear how M2 neurons encode the same type of locomotor control variables in different environmental contexts. Here we image the neuronal activity in M2 with a miniscope while mice are moving freely in each of three environments: a Y-maze, a running-wheel, and an open-field. These animals show distinct locomotor patterns in different environmental contexts. Surprisingly, a large population of M2 neurons are active before starting and after ceasing locomotion, while maintaining decreased neural activity during locomotion. Furthermore, the majority of these neurons are consistently engaged across various contexts, suggesting egocentric voluntary control functions. In contrast, the smaller populations of locomotion-activated M2 neurons are mostly context-specific, suggesting exocentric navigation functions. Thus, our results demonstrate that M2 neurons encode motor control variables for self-initiated locomotor behaviors in both context-dependent and context-independent manners.}, }
@article {pmid40347675, year = {2025}, author = {Chen, L and Liu, Y and Wang, Z and Zhang, L and Cheng, S and Ming, D}, title = {Using non-invasive brain stimulation to modulate performance in visuomotor rotation adaptation: A scoping review.}, journal = {Cortex; a journal devoted to the study of the nervous system and behavior}, volume = {187}, number = {}, pages = {144-158}, doi = {10.1016/j.cortex.2025.04.010}, pmid = {40347675}, issn = {1973-8102}, abstract = {As research on the visuomotor rotation (VMR) adaptation expands its scope from behavioral science to encompass neuropsychological perspectives, an increasing number of studies have employed non-invasive brain stimulation (NIBS) techniques to explore the specific contributions of different neural structures to VMR adaptation. Despite early studies suggesting that cerebellar stimulation influenced the rate of adaptation and that stimulating primary motor cortex led to an enhanced retention of newly learned adaptation, subsequent studies could not always achieve consistent results. To probe this inconsistency, we systematically comb through past studies and extract numerous details, including paradigm designs, context settings, and modulation protocols in this scoping review. In summary, the paradigm design primarily serves two purposes: to dissociate implicit and explicit adaptation and to assess the retention of motor memory, whilst context settings such as apparatus, movement-related parameters and the information provided for subjects may complicate the modulated neuropsychological processes. We also conclude key NIBS parameters such as target regions and timing in stimulation protocols. Furthermore, we recognize the potential of neurophysiological biomarkers to support future VMR studies that incorporate NIBS and advocate for the use of several newly emerging NIBS techniques to enrich the field.}, }
@article {pmid40347660, year = {2025}, author = {Chen, X and Chen, Y and McNamara, TP}, title = {Processing spatial cue conflict in navigation: Distance estimation.}, journal = {Cognitive psychology}, volume = {158}, number = {}, pages = {101734}, doi = {10.1016/j.cogpsych.2025.101734}, pmid = {40347660}, issn = {1095-5623}, abstract = {Spatial navigation involves the use of various cues. This study examined how cue conflict influences navigation by contrasting landmarks and optic flow. Participants estimated spatial distances under different levels of cue conflict: minimal conflict, large conflict, and large conflict with explicit awareness of landmark instability. Whereas increased cue conflict alone had little behavioral impact, adding explicit awareness reduced reliance on landmarks and impaired the precision of spatial localization based on them. To understand the underlying mechanisms, we tested two cognitive models: a Bayesian causal inference (BCI) model and a non-Bayesian sensory disparity model. The BCI model provided a better fit to the data, revealing two independent mechanisms for reduced landmark reliance: increased sensory noise for unstable landmarks and lower weighting of unstable landmarks when landmarks and optic flow were judged to originate from different causes. Surprisingly, increased cue conflict did not decrease the prior belief in a common cause, even when explicit awareness of landmark instability was imposed. Additionally, cue weighting in the same-cause judgment was determined by bottom-up sensory reliability, while in the different-cause judgment, it correlated with participants' subjective evaluation of cue quality, suggesting a top-down metacognitive influence. The BCI model further identified key factors contributing to suboptimal cue combination in minimal cue conflicts, including the prior belief in a common cause and prior knowledge of the target location. Together, these findings provide critical insights into how navigators resolve conflicting spatial cues and highlight the utility of the BCI model in dissecting cue interaction mechanisms in navigation.}, }
@article {pmid40342556, year = {2025}, author = {Haseeb, M and Nadeem, R and Sultana, N and Naseer, N and Nazeer, H and Dehais, F}, title = {Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator.}, journal = {Frontiers in robotics and AI}, volume = {12}, number = {}, pages = {1441801}, doi = {10.3389/frobt.2025.1441801}, pmid = {40342556}, issn = {2296-9144}, abstract = {Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a specific level can alter auditory and visual attention, resulting in pilot errors. The objective of this research is to monitor pilots' mental workload using advanced machine learning techniques to achieve improved accuracy compared to previous studies. Electroencephalogram (EEG) data were recorded from 22 pilots operating under visual flight rules (VFR) conditions using a six dry-electrode Enobio Neuroelectrics system, and the Riemannian artifact subspace reconstruction (rASR) filter was used for data cleaning. An information gain (IG) attribute evaluator was used to select 25 optimal features out of 72 power spectral and statistical extracted features. In this study, 15 classifiers were used for classification. Multinomial logistic regression with a ridge estimator was selected, achieving a significant mean accuracy of 84.6% on the dataset from 17 subjects. Data were initially collected from 22 subjects, but 5 were excluded due to data synchronization issues. This work has several limitations, such as the author did not counter balance the order of scenario, could not control all the variables such as wind conditions, and workload was not stationary in each leg of the flight pattern. This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight scenarios.}, }
@article {pmid40341884, year = {2025}, author = {Bertheau, MAK and Boetzel, C and Herrmann, CS}, title = {Event-related potentials reveal incongruent behavior of autonomous vehicles in the moral machine dilemma.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {16048}, pmid = {40341884}, issn = {2045-2322}, mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; *Morals ; Electroencephalography ; Adult ; Young Adult ; *Decision Making/physiology ; *Artificial Intelligence ; }, abstract = {We investigated event-related potentials (ERPs) in the context of autonomous vehicles (AVs)-specifically in ambiguous, morally challenging traffic situations. In our study, participants (n = 34) observed a putative artificial intelligence (AI) making decisions in a dilemma situation involving an AV, expanding on the Moral Machine (MM) experiment. Additionally to the original MM experiment, we incorporated electroencephalography recordings. We were able to replicate most of the behavioral findings of the original MM: In case of an unavoidable traffic accident, participants consistently favored sparing pedestrians over passengers, more characters over fewer characters, and humans over pets. Beyond that, in the ERP we observed an increased P3 (322-422 ms), and late positive potential (LPP) (500-900 MS) amplitude in fronto-central regions when the putative AI's decision on a moral dilemma was incongruent to the participants' decision. As P3, and LPP are associated with the processing of stimulus significance, our findings suggest that these ERP components could potentially be used to identify critical, or unacceptable situations during human-AI interactions involving moral decision-making. This might be useful in brain computer interfaces research when, classifying single-trial ERP components, to dynamically adopt an AV's behavior.}, }
@article {pmid40341243, year = {2025}, author = {Lore, S and Poganik, JR and Atala, A and Church, G and Gladyshev, VN and Scheibye-Knudsen, M and Verdin, E}, title = {Replacement as an aging intervention.}, journal = {Nature aging}, volume = {}, number = {}, pages = {}, pmid = {40341243}, issn = {2662-8465}, support = {1U01AI180158-01//U.S. Department of Health &amp; Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; 1U01AI180158-01//U.S. Department of Health &amp; Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; }, abstract = {Substantial progress in aging research continues to deepen our understanding of the fundamental mechanisms of aging, yet there is a lack of interventions conclusively shown to attenuate the processes of aging in humans. By contrast, replacement interventions such as joint replacements, pacemaker devices and transplant therapies have a long history of restoring function in injury or disease contexts. Here, we consider biological and synthetic replacement-based strategies as aging interventions. We discuss innovations in tissue engineering, such as the use of scaffolds or bioprinting to generate functional tissues, methods for enhancing donor-recipient compatibility through genetic engineering and recent progress in both cell therapies and xenotransplantation strategies. We explore synthetic approaches including prostheses, external devices and brain-machine interfaces. Additionally, we evaluate the evidence from heterochronic parabiosis experiments in mice and donor-recipient age-mismatched transplants to consider whether systemic benefits could result from personalized replacement approaches. Finally, we outline key challenges and future directions required to advance replacement therapies as viable, scalable and ethical interventions for aging.}, }
@article {pmid40340020, year = {2025}, author = {Yu, X and Jian, Z and Dang, L and Zhang, X and He, P and Xiong, X and Feng, Y and Rehman, AU}, title = {Chemogenetic modulation in stroke recovery: A promising stroke therapy Approach.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.brs.2025.05.003}, pmid = {40340020}, issn = {1876-4754}, abstract = {Stroke remains a leading cause of long-term disability and mortality worldwide, necessitating novel therapeutic strategies to enhance recovery. Traditional rehabilitation approaches, including physical therapy and pharmacological interventions, often provide limited functional improvement. Neuromodulation has emerged as a promising strategy to promote post-stroke recovery by enhancing neuroplasticity and functional reorganization. Among various neuromodulatory techniques, chemogenetics, particularly Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), offers precise, cell-type-specific, and temporally controlled modulation of neuronal and glial activity. This review explores the mechanisms and therapeutic potential of chemogenetic modulation in stroke recovery. Preclinical studies have demonstrated that activation of excitatory DREADDs (hM3Dq) in neurons located within the peri-infarct area or contralateral M1 has been shown to enhance neuroplasticity, facilitate axonal sprouting, and lead to improved behavioral recovery following stroke. Conversely, stimulation of inhibitory DREADDs (hM4Di) suppresses stroke-induced excitotoxicity, mitigates peri-infarct spreading depolarizations (PIDs), and modulates neuroinflammatory responses. By targeting specific neuronal and glial populations, chemogenetics enables phase-specific interventions-early inhibition to minimize damage during the acute phase and late excitation to promote plasticity during the recovery phase. Despite its advantages over traditional neuromodulation techniques, such as optogenetics and deep brain stimulation, several challenges remain before chemogenetics can be translated into clinical applications. These include optimizing viral vector delivery, improving ligand specificity, minimizing off-target effects, and ensuring long-term receptor stability. Furthermore, integrating chemogenetics with existing stroke rehabilitation strategies, including brain-computer interfaces and physical therapy, may enhance functional recovery by facilitating adaptive neuroplasticity. Future research should focus on refining chemogenetic tools to enable clinical application. By offering a highly selective, reversible, and minimally invasive approach, chemogenetics holds great potential for revolutionizing post-stroke therapy and advancing personalized neuromodulation strategies.}, }
@article {pmid40338888, year = {2025}, author = {Faisal, M and Khosa, I and Waris, A and Gilani, SO and Khan, MJ and Hazzazi, F and Ijaz, MA}, title = {Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.}, journal = {PloS one}, volume = {20}, number = {5}, pages = {e0322580}, doi = {10.1371/journal.pone.0322580}, pmid = {40338888}, issn = {1932-6203}, mesh = {Humans ; *Electromyography/methods ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Movement/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; }, abstract = {Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative and effective interventions, this study investigates the efficacy of electromyography (EMG) decoding in improving motor function outcomes. While existing literature has extensively explored classifier selection and feature set optimization, the choice of preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a significant knowledge gap. This study presents upper limb movement classification by providing a comprehensive comparison of eight time-domain windowing techniques. For this purpose, the EMG data from volunteers is recorded involving fifteen distinct movements of fingers. The rectangular window technique among others emerged as the most effective, achieving a classification accuracy of 99.98% while employing 40 time-domain features and a L-SVM classifier, among other classifiers. This optimal combination has implications for the development of more accurate and reliable myoelectric control systems. The achieved high classification accuracy demonstrates the feasibility of using surface EMG signals for accurate upper limb movement classification. The study's results have the potential to improve the accuracy and reliability of prosthetic limbs and wearable sensors and inform the development of personalized rehabilitation programs. The findings can contribute to the advancement of human-computer interaction and brain-computer interface technologies.}, }
@article {pmid40338479, year = {2025}, author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA}, title = {Predicting fixations and gaze location from EEG.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40338479}, issn = {1741-0444}, support = {CHIST-ERA-20-BCI-001//HORIZON EUROPE Framework Programme/ ; 101071147//HORIZON EUROPE European Innovation Council/ ; PCI2021-122036-2A//Agencia Estatal de Investigaci&#xf3;n/ ; }, abstract = {Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.}, }
@article {pmid40336015, year = {2025}, author = {Yang, P and Zhang, X and Song, H and Zhang, X}, title = {An investigation into mental illness and its comorbidities from the perspective of supervenience physicalism.}, journal = {Philosophy, ethics, and humanities in medicine : PEHM}, volume = {20}, number = {1}, pages = {10}, pmid = {40336015}, issn = {1747-5341}, abstract = {The exploration into the origin of human spirituality has always been a hot spot with many unsolved questions in the philosophy of mind, and issues concerning mental illness and its comorbidities are still unclear. In the 1970s, Donald Davidson first proposed anomalous monism with the supervenience concept, a theory that both insists on physicalism and transcends traditional reductionism. This theory provides solid and accessible proof for perceiving the mind-body relationship of spiritual origin in a non-reductionist approach. This paper develops arguments in two aspects. First, three principles of anomalous monism are employed to explore the origin of mental illness. Second, the comorbidity of mental illness is explained with the help of the supervenience theory.}, }
@article {pmid40335704, year = {2025}, author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y}, title = {Publisher Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41586-025-09086-9}, pmid = {40335704}, issn = {1476-4687}, }
@article {pmid40334964, year = {2025}, author = {Dong, L and Qi, Y and Luan, M and Liu, Q and Wang, M and Tian, C and Zheng, Y}, title = {A multi-channel implantable micro-magnetic stimulator for synergistic magnetic neuromodulation.}, journal = {Brain research}, volume = {}, number = {}, pages = {149679}, doi = {10.1016/j.brainres.2025.149679}, pmid = {40334964}, issn = {1872-6240}, abstract = {Micro-magnetic stimulation (μMS) is an emerging technology in magnetic neuromodulation. However, for larger brain structures with complex neural pathways, such as deep brain neural clusters, traditional implantable single-point μMS devices are immobile and incapable of multi-regional magnetic modulation. While multi-channel μMS can effectively address this limitation, its large size, difficulty in implantation, and unclear synergistic modulation patterns restrict its application. To tackle these challenges, this study designs a 4 × 4 array micro-coil structure targeted at the deep hippocampal region of the mouse brain. Numerical simulations were performed to analyze the coupling coefficients among the micro-coils and the distribution of the electromagnetic field in the structure, indicating that, with optimized parameters, the effective magnetic stimulation threshold can be achieved. Based on this, a multi-channel μMS device was fabricated, solving key issues such as waterproofing, biocompatibility, and dual-brain-region implantation of both stimulation and recording electrodes. A multi-point synergistic magnetic stimulation protocol was developed. After determining the synergistic magnetic stimulation parameters and effective target positions through in vitro experiments, real-time monitoring of calcium signal changes in the CA1 region of the hippocampus in mice during synergistic magnetic stimulation was performed. The results demonstrate that synergistic magnetic stimulation significantly enhances synaptic plasticity and calcium signal activity. This validates the feasibility of the implantable multi-channel micro-magnetic stimulator.}, }
@article {pmid40334847, year = {2025}, author = {Yuan, J and Pan, H and Sun, Y and Wang, Y and Jia, J}, title = {Neural responses to global and local visual information processing provide neural signatures of ADHD symptoms.}, journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology}, volume = {}, number = {}, pages = {112582}, doi = {10.1016/j.ijpsycho.2025.112582}, pmid = {40334847}, issn = {1872-7697}, abstract = {Individuals with ADHD are thought to exhibit a reduced "global bias" in perceptual processing. This bias, found in typically developed individuals, characterizes the tendency to prioritize global over local information processing. However, the relationship between specific ADHD symptoms and global or local processing remains unclear. This study addresses this gap by employing an ensemble perception task with a large sample (N = 465). EEG recordings allowed for the isolation of neural responses to individual and global stimuli using linear regression modeling. The adult ADHD self-report scale was used to assess ADHD symptoms. The results showed a significant association between ensemble perception and early responses to global stimuli. Furthermore, inattention symptoms were associated with early responses to global stimuli, suggesting a reduced global prioritization in individuals with higher inattention scores. Moreover, inattention symptom was associated with later responses to local stimuli, as shown by attenuated neural responses to local stimuli in individuals with more severe symptoms. These findings provide insights that ADHD includes deficits in both global and local processing, challenging earlier theories that focused solely on global processing impairments.}, }
@article {pmid40334819, year = {2025}, author = {Yu, H and Zhou, X and Ru, Q and Anthony, A and Cameron, M and Liu, Y and Klann, IP and Guo, H and Lin, J and Wang, D and Chang, D}, title = {The modulatory effects of persimmon leaf extract on sleep-related neurotransmitters and its potential hypnotic effects.}, journal = {Fitoterapia}, volume = {}, number = {}, pages = {106576}, doi = {10.1016/j.fitote.2025.106576}, pmid = {40334819}, issn = {1873-6971}, abstract = {PURPOSE: Persimmon leaf is a traditional herbal medicine with diverse therapeutic applications. This study aimed to explore the effect of persimmon leaf extract (PLE) on the modulation of neurotransmitters involved in sleep regulation and its overall impact on sleep latency and duration.<br><br>METHODS: The key components of PLE were identified by ultra performance liquid chromatography. The modulatory effects of PLE in sleep and wakefulness-related neurotransmitters were studied in human neuroblastoma SH-SY5Y cells. PLE was also investigated in pentobarbital sodium-induced sleep and para-chlorophenylalanine (PCPA)-induced insomnia models in mice and rats.<br><br>RESULTS: PLE induced chloride influx and increased the intracellular production of gamma-aminobutyric acid (GABA), a neurotransmitter crucial for sleep regulation, in SH-SY5Y cells. Furthermore, PLE influenced the cellular expressions of serotonin, dopamine, and adenosine. It increased monoamine oxidase enzyme-A activity and reduced serotonin levels and its metabolites. It induced dopamine biosynthesis and degradation pathways. In the pentobarbital-induced sleep experiment, PLE significantly prolonged total sleep duration and reduced sleep latency in a dose-dependent manner. In the PCPA-induced insomnia model, PLE consistently increased GABA production, and lowered dopamine expression.<br><br>CONCLUSION: PLE exhibited modulatory effects on sleep-related neurotransmitters in vitro, which may also contribute to its hypnotic effects by extending the sleep duration and shortening sleeping latency in vivo.}, }
@article {pmid40334321, year = {2025}, author = {Chen, X and Jia, T and Wu, D}, title = {Data alignment based adversarial defense benchmark for EEG-based BCIs.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {188}, number = {}, pages = {107516}, doi = {10.1016/j.neunet.2025.107516}, pmid = {40334321}, issn = {1879-2782}, abstract = {Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs.}, }
@article {pmid40332078, year = {2025}, author = {Park, BS and Yang, HR and Kang, H and Kim, KK and Kim, YT and Yang, S and Kim, JG}, title = {α2-Adrenergic Receptors in Hypothalamic Dopaminergic Neurons: Impact on Food Intake and Energy Expenditure.}, journal = {International journal of molecular sciences}, volume = {26}, number = {8}, pages = {}, doi = {10.3390/ijms26083590}, pmid = {40332078}, issn = {1422-0067}, support = {NRF-2021R1C1C2005067//National Research Foundation of Korea/ ; Research Grant (2020)//Incheon National University/ ; }, mesh = {Animals ; *Energy Metabolism/drug effects ; *Hypothalamus/metabolism/cytology/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Receptors, Adrenergic, alpha-2/metabolism/genetics ; Mice ; Male ; *Eating/drug effects ; Mice, Inbred C57BL ; }, abstract = {The adrenergic system plays an active role in modulating synaptic transmission in hypothalamic neurocircuitry. While α2-adrenergic receptors are widely distributed in various organs and are involved in various physiological functions, their specific role in the regulation of energy metabolism in the brain remains incompletely understood. Herein, we investigated the functions of α2-adrenergic receptors in the hypothalamus on energy metabolism in mice. Our study confirmed the expression of α2-adrenergic receptors in hypothalamic dopaminergic neurons and assessed metabolic phenotypes, including food intake and energy expenditure, after treatment with guanabenz, an α2-adrenergic receptor agonist. Guanabenz treatment significantly increased food intake (0.25 ± 0.03 g vs. 0.98 ± 0.05 g, p < 0.001) and body weight (-0.1 ± 0.04 g vs. 0.33 ± 0.03 g, p < 0.001) within 6 h post-treatment. Furthermore, guanabenz markedly elevated energy expenditure parameters, including respiratory exchange ratio (RER, 1.017 ± 0.007 vs. 1.113 ± 0.03, p < 0.01) and carbon dioxide production (1.512 ± 0.018 mL/min vs. 1.635 ± 0.036 mL/min, p < 0.05), compared to vehicle-treated controls. Furthermore, using chemogenetic techniques, we demonstrated that the altered metabolic phenotypes induced by guanabenz treatment were effectively reversed by inhibiting the activity of dopaminergic neurons in the hypothalamic arcuate nucleus (ARC) using a chemogenetic technique. Our findings suggest functional connectivity between hypothalamic α2-adrenergic receptor signals and dopaminergic neurons in metabolic controls.}, }
@article {pmid40330423, year = {2009}, author = {Mollazadeh, M and Aggarwal, V and Thakor, NV and Law, AJ and Davidson, A and Schieber, MH}, title = {Coherency between Spike and LFP Activity in M1 during Hand Movements.}, journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering}, volume = {2009}, number = {}, pages = {506-509}, pmid = {40330423}, issn = {1948-3546}, abstract = {Local field potentials (LFP) represent the dendritic activity of a population of cells near the recording electrode. However, how LFP activity is related to single unit activity, and if it provides any additional information has not been well studied. Previously we have shown that temporal spectral modulation of LFP activity can be used to decode dexterous movements of the hand. Here, we analyze simultaneous spike and LFP recordings from M1 cortex in a rhesus monkey performing fine hand movements. Using multitaper spectral analysis, we found that both LFP and spiking activity show an increase in power in the <12 Hz and 70-200 Hz (high gamma) ranges, but, were significantly coherent only during the pre-movement time at low frequencies (<12 Hz). Furthermore, using either LFP or spiking activity, we were able to decode amongst three different hand grasps with high accuracy (99% using 97 spikes and 70% using 8 LFP channels). However, while spikes were better in decoding movement types, LFPs performed much better (94% success) than spikes (77%) when differentiating between rest and movement. We also found that combining spike and LFP activity can improve decoding performance when fewer spikes are considered, as may be the case when single unit recordings degrade over time (71% using 40 spikes and 76% using 8 LFPs, vs 88% using 40 spikes + 8 LFPs). Thus, the relative stability of LFP activity can help augment single-unit activity for the chronic operation of a multimodal BMI.}, }
@article {pmid40329250, year = {2025}, author = {Karimi, J and Cherono, A and Alegana, V and Mutua, M and Kiarie, H and Muthee, R and Temmerman, M and Gichangi, P}, title = {Geographic inequalities, and social-demographic determinants of reproductive, maternal and child health at sub-national levels in Kenya.}, journal = {BMC public health}, volume = {25}, number = {1}, pages = {1656}, pmid = {40329250}, issn = {1471-2458}, support = {211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; }, abstract = {BACKGROUND: Global initiatives have emphasized tracking indicators to monitor progress, particularly in countries with the highest maternal and child mortality. Routine data can be used to monitor indicators for improved target setting at national and subnational levels. Our objective was to assess the geographic inequalities in estimates of reproductive, maternal and child health indicators from routine data at the subnational level in Kenya.<br><br>METHODS: Monthly data from 47 counties clustered in 8 regions, from January 2018 to December 2021 were assembled from the District Health Information Software version 2 (DHIS2) in Kenya. This included women of reproductive age receiving family planning commodities, pregnant women completing four antenatal care visits, deliveries conducted by skilled birth attendants, fully immunized children at 1&#xa0;year and number of maternal deaths at health facilities, from which five indicators were constructed with denominators. A hierarchical Bayesian model was used to generate estimates of the five indicators at the at sub-national levels(counties and sub counties), adjusting for four determinants of health. A reproductive, maternal, and child health (RMCH) index was generated from the 5 indicators to compare overall performance across the continuum of care in reproductive, maternal and child health across the different counties.<br><br>RESULTS: The DHIS2 data quality for the selected 5 indicators was acceptable with detection of less than 3% outliers for the Facility Maternal Mortality Ratio (FMMR) and less than 1% for the other indicators. Overall, counties in the north-eastern, eastern and coastal regions had the lowest RMCH index due to low service coverage and high FMMR. Full immunization coverage at 1&#xa0;year (FIC) had the highest estimate (79.3%, BCI: 77.8-80.5%), while Women of Reproductive age receiving FP commodities had the lowest estimate (38.6%, BCI: 38.2-38.9%). FMMR was estimated at 105.4, (BCI 67.3-177.1)Health facility density was an important determinant in estimating all five indicators. Maternal education was positively correlated with higher FIC coverage, while wealthier sub counties had higher FMMR.<br><br>CONCLUSIONS: Tracking of RMCH indicators revealed geographical inequalities at the County and subcounty level, often masked by national-level estimates. These findings underscore the value of routine monitoring indicators as a potential for evidence-based sub-national planning and precision targeting of interventions to marginalized populations.}, }
@article {pmid40327905, year = {2025}, author = {Sarankumar, R and Ramkumar, M and Karthik, V and Muthuvel, SK}, title = {Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.}, journal = {Biochemical and biophysical research communications}, volume = {768}, number = {}, pages = {151856}, doi = {10.1016/j.bbrc.2025.151856}, pmid = {40327905}, issn = {1090-2104}, abstract = {Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient. This research aims to improve HER2 status prediction accuracy by proposing an Enhanced Hybrid Model with Optimized Attention Network (EHMOA-net) for histopathology image analysis. The methodology involves patch segmentation using an Encoder-Decoder-based hybrid weights alignment with Multi-Dilated U-net (EDMDU) model applied to the TCGA dataset, followed by preprocessing through enhanced Macenko stain normalization for segmented patches and images from the BCI dataset. Improved non-subsampled shearlet transform is utilized for feature extraction, and the Hybrid Enhanced Rough k-means clustering and Fuzzy C-Means (HERFCM) algorithm is employed to cluster neighboring image patches based on similar features. Finally, HER2 prediction is performed using nested graph neural networks integrated with a visual attention network. The proposed method, implemented in Python, achieves an accuracy of 97.85 %, surpassing existing techniques. These findings demonstrate the effectiveness of EHMOA-net in improving HER2 prediction accuracy and its potential utility in clinical applications.}, }
@article {pmid40327498, year = {2025}, author = {Xu, Y and Wang, X and Li, J and Zhang, X and Li, F and Gao, Q and Fu, C and Leng, Y}, title = {A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3567392}, pmid = {40327498}, issn = {1558-0210}, abstract = {The anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping. To address these challenges, we propose a vision-powered prosthetic hand system that integrates two innovations. Spatial Geometry-based Gesture Mapping (SG-GM) dynamically models finger joint angles as polynomial functions of hand-object distance, derived from geometric features of human grasping sequences. These functions enable continuous anthropomorphic gesture transitions, mimicking natural hand movements. Motion Trajectory Regressionbased Grasping Intent Estimation (MTR-GIE) predicts user intent in multi-object environments by regressing wrist trajectories and spatially segmenting candidate objects. Experiments with eight daily objects demonstrated high anthropomorphism (similarity coefficient R[2]=0.911, root mean squared error RMSE=2.47°), rapid execution (3.0710.41 s), and robust success rates (95.43% single-object; 88.75% multi-object). The MTR-GIE achieved 94.35% intent estimation accuracy under varying object spacing. This work pioneers vision-driven dynamic gesture synthesis for prosthetics, eliminating dependency on invasive sensors and advancing real-world usability.}, }
@article {pmid40326979, year = {2026}, author = {Du, X and Wang, Y and Wang, X and Tian, X and Jing, W}, title = {Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels.}, journal = {Neural regeneration research}, volume = {21}, number = {2}, pages = {455-465}, doi = {10.4103/NRR.NRR-D-24-00537}, pmid = {40326979}, issn = {1673-5374}, abstract = {Epilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy. Therefore, this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies, including electroencephalography, magnetic resonance imaging, optogenetics, chemogenetics, deep brain stimulation, and brain-computer interfaces. Additionally, this review discusses these mechanisms from three perspectives: structural, synaptic, and transmitter circuits. The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures, interactions within the same structure, and the maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels. These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.}, }
@article {pmid40323825, year = {2025}, author = {Kim, H and Chang, WK and Kim, WS and Jang, JH and Lee, YA and Vermehren, M and Peekhaus, N and Colucci, A and Angerhöfer, C and Hömberg, V and Soekadar, SR and Paik, NJ}, title = {Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {218}, pages = {}, doi = {10.3791/67601}, pmid = {40323825}, issn = {1940-087X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Male ; Female ; Middle Aged ; Electroencephalography/methods ; Activities of Daily Living ; Aged ; Adult ; Electrooculography/methods ; }, abstract = {This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.}, }
@article {pmid40321898, year = {2025}, author = {Zhang, S and Song, Y and Lv, S and Jing, L and Wang, M and Liu, Y and Xu, W and Jiao, P and Zhang, S and Wang, M and Liu, J and Wu, Y and Cai, X}, title = {Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0249}, pmid = {40321898}, issn = {2692-7632}, abstract = {Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity in primates enhances our understanding of neural mechanisms, facilitates the study of major brain diseases, enables brain-computer interactions, and supports advancements in artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, and scalp electroencephalogram are limited in spatial resolution. They cannot accurately capture deep brain signals from individual neurons. With the progress of microelectromechanical systems and other micromachining technologies, single-neuron level detection and stimulation technology in rodents based on microelectrodes has made important progress. However, compared with rodents, human and nonhuman primates have larger brain volume that needs deeper implantation depth, and the test object has higher safety and device preparation requirements. Therefore, high-resolution devices suitable for long-term detection in the brains of primates are urgently needed. This paper reviewed electrode array devices used for electrophysiological and electrochemical detections in primates' deep brains. The research progress of neural recording and stimulation technologies was introduced from the perspective of electrode type and device structures, and their potential value in neuroscience research and clinical disease treatments was discussed. Finally, it is speculated that future electrodes will have a lot of room for development in terms of flexibility, high resolution, deep brain, and high throughput. The improvements in electrode forms and preparation process will expand our understanding of deep brain neural activities, and bring new opportunities and challenges for the further development of neuroscience.}, }
@article {pmid40321282, year = {2025}, author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B}, title = {Continuous Reaching and Grasping with a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.04.16.25325551}, pmid = {40321282}, abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.}, }
@article {pmid40318536, year = {2025}, author = {Tang, E and Li, J and Liu, H and Peng, C and Zhou, D and Hu, S and Chen, H}, title = {Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia.}, journal = {Journal of psychiatric research}, volume = {186}, number = {}, pages = {434-444}, doi = {10.1016/j.jpsychires.2025.04.030}, pmid = {40318536}, issn = {1879-1379}, abstract = {People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, ηp[2] = .092; task two: p = .021, ηp[2] = .074; task three: p = .015, ηp[2] = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = -.344, p = .040) or higher medication dosages (r = -.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication.}, }
@article {pmid40317785, year = {2025}, author = {Stieglitz, T and Bersch, I and Mrachacz-Kersting, N and Pasluosta, C}, title = {Differences and Commonalities of Electrical Stimulation Paradigms After Central Paralysis and Amputation.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.15017}, pmid = {40317785}, issn = {1525-1594}, abstract = {BACKGROUND: Patients with spinal cord injury (SCI) or with severe brain stroke suffer from life-lasting functional and sensory impairments. Other traumatic injuries such as limb loss after an accident or disease also affect motor function and sensory feedback and impair quality of life in those individuals. Invasive and non-invasive functional electrical stimulation (FES) is a well-established method to partially restore function and sensory feedback of paralyzed and phantom limbs. It is also a supporting technology for the rehabilitation of the neuromuscular system and for complementing assistive devices.<br><br>METHODS: This work reviews the current state-of-the-art of FES as a technology for restoring function and supporting rehabilitation therapy and assistive devices.<br><br>RESULTS: Electrodes, electrical stimulation, use of brain signals for rehabilitation and control, and sensory feedback are covered as parts of the whole. A perspective is given on how clinical and research protocols developed for patients with SCI and brain injuries can be translated to the treatment of patients with an amputation and vice versa. We further elaborate on how motor learning strategies with quantitative electrophysiological and kinematic measurements may help caregivers in the rehabilitation process. Insights from practitioners (collected during a workshop of the IFESS 2025) have been integrated to summarize common needs, open questions, and challenges.<br><br>CONCLUSIONS: The information from the literature and from practitioners was integrated to propose the next steps towards establishing common guidelines and measures of FES in clinical practice towards evidence-driven treatment and objective assessments.}, }
@article {pmid40317558, year = {2025}, author = {Du, X and Shen, F and Yu, C and Wang, Y and Yu, J and Yao, L and Liu, N and Zhuang, S}, title = {SMYD3 as an Epigenetic Regulator of Renal Tubular Cell Survival and Regeneration Following Acute Kidney Injury in Mice.}, journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology}, volume = {39}, number = {9}, pages = {e70533}, doi = {10.1096/fj.202500089R}, pmid = {40317558}, issn = {1530-6860}, support = {82370698//MOST | National Natural Science Foundation of China (NSFC)/ ; 82070700//MOST | National Natural Science Foundation of China (NSFC)/ ; }, mesh = {Animals ; *Acute Kidney Injury/metabolism/pathology/genetics ; Mice ; *Histone-Lysine N-Methyltransferase/metabolism/genetics/antagonists &amp; inhibitors ; *Epigenesis, Genetic ; *Kidney Tubules/metabolism/pathology ; *Regeneration ; Male ; Cell Survival ; Mice, Inbred C57BL ; Apoptosis ; Cell Proliferation ; Reperfusion Injury/metabolism/pathology ; Histones/metabolism ; }, abstract = {The protein SET and MYND-Domain Containing 3 (SMYD3) is a methyltransferase that modifies various non-histone and histone proteins, linking it to tumorigenesis and cyst formation. However, its role in acute kidney injury (AKI) remains unclear. This study investigates the role and mechanism of AKI using a murine model of ischemia-reperfusion (IR)-induced AKI. After IR injury, SMYD3 and H3K4me3 levels increased in the kidneys, correlating with renal dysfunction, tubular cell injury, and apoptosis. Administration of BCI-121, a selective SMYD3 inhibitor, exacerbated IR-induced tubular cell injury and apoptosis, leading to more severe renal dysfunction and pathological changes. Pharmacological inhibition of SMYD3 also impaired the dedifferentiation and proliferation of renal tubular cells, key regenerative processes in injured kidneys, as evidenced by decreased expression of vimentin, snail, proliferating cell nuclear antigen (PCNA), cyclin D1, and retinoblastoma protein (RB). Additionally, SMYD3 inhibition reduced phosphorylation of the epithelial growth factor receptor (EGFR) and AKT, as well as EGFR expression in damaged kidneys. Finally, both BCI-121 and SMYD3 siRNA reduced EGF-induced expression of vimentin, snail, cyclin D1, PCNA, and EGFR, along with phosphorylation of RB and AKT in cultured renal tubular cells. Chip assay indicated that SMYD3 and H3K4me3 are enriched at the promoter of EGFR and SMYD3 inhibition blocked this response. These data suggest that SMYD3 plays an important role as an epigenetic regulator of renal tubular cell survival and regenerative pathways following kidney injury. Targeting SMYD3 or its epigenetic effects could offer therapeutic potential for enhancing kidney regeneration in AKI and related renal diseases.}, }
@article {pmid40315903, year = {2025}, author = {Chueh, SY and Chen, Y and Subramanian, N and Goolsby, B and Navarro, P and Oweiss, K}, title = {Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add37b}, pmid = {40315903}, issn = {1741-2552}, abstract = {Brain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain representational drift - a frequent and widespread phenomenon that adversely affects BCI control and continued use. Approach: We developed an all-optical approach to characterize IP, BTSP and SS with single cell resolution in awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmineKv2.1. We further trained mice on a one-dimensional (1D) BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles. Main results: On the time scale of seconds, substantial BTSP could be induced and was followed by significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control. Significance: Our results provide early experimental support for a meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms. With the power of modern-day machine learning (ML) and artificial Intelligence (AI), fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention. .}, }
@article {pmid40315795, year = {2025}, author = {Chen, TY and Lien, KH and Yeh, KT and Tu, JC and Wai-Yee Ho, V and Chan, KC}, title = {Bonebridge BCI 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia.}, journal = {International journal of pediatric otorhinolaryngology}, volume = {193}, number = {}, pages = {112370}, doi = {10.1016/j.ijporl.2025.112370}, pmid = {40315795}, issn = {1872-8464}, abstract = {OBJECTIVE: To evaluate the safety and efficacy of Bonebridge bone conduction implant (BCI) 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia (AA).<br><br>METHODS: This retrospective study included 15 patients (3 syndromic, 12 non-syndromic) with bilateral microtia and AA who underwent BCI 602 implantation at a tertiary medical center between January 2022 and June 2024. Intraoperative and postoperative complications were recorded, with a minimum follow-up of six months. Audiological outcomes, including functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were analyzed.<br><br>RESULTS: No intraoperative complications occurred in any cases. One minor postoperative complication (6.7&#xa0;%) was reported in a non-syndromic patient during follow-up. The mean unaided and aided sound field threshold pure tone averages were 60.3&#xa0;&#xb1;&#xa0;8.7&#xa0;dB HL and 23.8&#xa0;&#xb1;&#xa0;3.9&#xa0;dB HL, respectively, yielding an FHG of 36.6&#xa0;&#xb1;&#xa0;9.3&#xa0;dB HL (p&#xa0;<&#xa0;0.05). SRT improved from 57.0&#xa0;&#xb1;&#xa0;5.9&#xa0;dB HL to 27.0&#xa0;&#xb1;&#xa0;6.5&#xa0;dB HL in quiet and from 0.3&#xa0;&#xb1;&#xa0;8.5&#xa0;dB SNR to -10.7&#xa0;&#xb1;&#xa0;4.2&#xa0;dB SNR in noise. WRS increased from 45.1&#xa0;&#xb1;&#xa0;20.7&#xa0;% to 89.9&#xa0;&#xb1;&#xa0;5.6&#xa0;% in quiet and from 40.9&#xa0;&#xb1;&#xa0;20.9&#xa0;% to 80.9&#xa0;&#xb1;&#xa0;13.8&#xa0;% in noise (p&#xa0;<&#xa0;0.05). Improvements in FHG, SRT, and WRS were comparable between syndromic and non-syndromic groups (p&#xa0;>&#xa0;0.05).<br><br>CONCLUSIONS: The Bonebridge BCI 602 is a safe and effective option for hearing restoration in both syndromic and non-syndromic patients with bilateral microtia and AA. Its compact design enhances surgical safety and minimizes risks to critical structures, particularly in syndromic patients with complex temporal bone anatomy.}, }
@article {pmid40315092, year = {2025}, author = {Zeng, F and Wen, X and Tang, H and Hu, G and Hou, W and Zhang, X}, title = {Age-related Changes in Action Observation EEG Response and its Effect on BCI Performance.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3566371}, pmid = {40315092}, issn = {1558-0210}, abstract = {Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% ± 14.67%) compared to younger subjects (87.22% ± 15.22%). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in α and β band connectivity strength; 3) θ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the β/θ RPR during stimulation and overall accuracy. And the β/θ RPR and the β band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.}, }
@article {pmid40313536, year = {2025}, author = {Xiong, W and Ma, L and Li, H}, title = {Synthesizing intelligible utterances from EEG of imagined speech.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1565848}, pmid = {40313536}, issn = {1662-4548}, abstract = {Decoding natural language directly from neural activity is of great interest to people with limited communication means. Being a non-invasive and convenient approach, the electroencephalogram (EEG) offers better portability and wider application potentiality. We present an EEG-to-speech system (ETS) that synthesizes audible, and highly understandable language by EEG of imagined speech. Our ETS applies a specially designed X-shape deep neural network (DNN) to realize an End-to-End correspondence between imagined speech EEG and the speech. The system innovatively incorporates dynamic time warping into the network's training process, using actual speech EEG data as a bridge to temporally align imagined speech EEG signals with speech signals. The ETS performance was evaluated on 13 participants who pretraining four Chinese disyllabic words. These words cover all four tones and 40% of the phonemes in Chinese. Our synthesized utterances' average accuracy across all subjects was 91.23%, the average MOS value was 3.50, and the best accuracy was 99% with an MOS value of 3.99. Furthermore, a partial feedback mechanism for DNN and spectral subtraction-based speech enhancement are introduced to improve the quality of generated speech. Our findings prove that non-invasive approaches can be a significant step in regaining verbal language ability.}, }
@article {pmid40313458, year = {2025}, author = {Xu, Y and Yu, B and Chen, X and Peng, A and Tao, Q and He, Y and Wang, Y and Li, XM}, title = {DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing.}, journal = {National science review}, volume = {12}, number = {5}, pages = {nwaf030}, pmid = {40313458}, issn = {2053-714X}, abstract = {Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.}, }
@article {pmid40313281, year = {2025}, author = {Lee, J and Park, H and Spencer, A and Gong, X and DeNardo, M and Vashahi, F and Pollet, F and Norris, S and Hinton, H and El Fakiri, M and Mehrotra, A and Huang, R and Bar, J and Swann, J and Affonseca, D and Armitage, O and Garry, R and Grumbles, E and Murali, A and Tasserie, J and Fragoso, C and Albouy, R and Couturier, CP and Paulk, AC and Coughlin, B and Cash, SS and Costine-Bartell, B and Baskin, B and Stinson, T and Moradi Chameh, H and Movahed, M and Bazrgar, B and Falby, M and Zhang, D and Valiante, TA and Francis, A and Candanedo, C and Bermudez, R and Liu, J and Ye, T and Le Floch, P}, title = {Clinical translation of ultrasoft Fleuron probes for stable, high-density, and bidirectional brain interfaces.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.04.24.25326126}, pmid = {40313281}, abstract = {Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome these trade-offs, by introducting Fleuron, a family of ultrasoft, ultra-low-k dielectric materials compatible with thin-film scalable microfabrication techniques. We successfully integrate up to 1,024 sites within a single minimally-invasive Fleuron depth electrode. The combination of the novel implant material and geometry enables single-unit level recordings for 18 months in rodent models, and achieves a large number of units detected per electrode across animals. 128-site Fleuron probes, that cover 8x larger tissue volume than state-of-the-art polyimide counterparts, can track over 100 single-units over months. Stability in neural recordings correlates with reduced glial encapsulation compared to polyimide controls up to 9-month post-implantation. Fleuron probes are integrated with a low-power, mixed-signal ASIC to achieve over 1,000 channels electronic interfaces and can be safely implanted in depth using minimally-invasive surgical techniques via a burr hole approach without requiring specialized robotics. Fleuron probes further create a unique contrast in clinical 3T MRI, allowing for post-operative position confirmation. Large-animal and ex vivo human tissue studies confirm safety and functionality in larger brains. Finally, Fleuron probes are used for the first time ever intraoperatively during planned resection surgeries, confirming in-human usability, and demonstrating the potential of the technology for clincical translation in iBCIs.}, }
@article {pmid40313239, year = {2025}, author = {Sun, S and Li, C and Xie, X and Wan, X and Liu, T and Li, D and Duan, D and Yu, H and Wen, D}, title = {Digital therapeutics for cognitive impairments associated with schizophrenia: our opinion.}, journal = {Frontiers in psychiatry}, volume = {16}, number = {}, pages = {1535309}, pmid = {40313239}, issn = {1664-0640}, }
@article {pmid40311414, year = {2025}, author = {Pattanayak, S and Dash, P and Satpathi, S and Sahoo, AK and Das, NR and Nayak, B and Sahoo, SK}, title = {Additive manufacturing of 316 L stainless steel orthopedic implant with improved in vitro hemocompatibility and hydrophilicity for osteoinduction in Wistar rat model.}, journal = {Biomaterials advances}, volume = {175}, number = {}, pages = {214322}, doi = {10.1016/j.bioadv.2025.214322}, pmid = {40311414}, issn = {2772-9508}, abstract = {Long-term implantation is still challenging for 316 L stainless steel (SS) due to low hydrophilicity and borderline corrosion, which further advances a coating to induce osteoinduction and prevent metallic ions leaching. Here, arc-based direct energy deposition technology is introduced to fabricate 316 L SS via additive manufacturing (AM). The AM 316 L SS are subjected to metallurgical, mechanical, chemical, in vitro and in vivo analyses for their possible orthopedic applications. Compared to commercially available 316 L SS implant, the AM implants encompass γ-austenite phases with δ-ferrite structures that induce pinning dislocations, improve resistance to crack propagation and enhance mechanical performances. The evolution of δ-ferrite structures with higher inter-layer dwell times promotes Cr and Mo content, improving passive layer thickness and thereby enhancing the corrosion resistance, which prevents the release of toxic ions into the bloodstream and cellular metabolism. Additionally, improved BCI with less adherence and activation of platelets on the AM deposits indicates uninterrupted blood flow along the site of implantation and improved thrombo-resistance. The reduction in contact angle (highly hydrophilic) promotes the adsorption of body fluid and proteinaceous materials that boost the adhesion, proliferation, and cytoplasmic extension of cells (from in vitro), marrow spaces, collagen fibers, and tissue adherences (from in vivo). The AM implants do not show any acute toxicity in blood profiles and vital organs (liver and kidney) after long-term implantation in Wistar rats. These peculiarities highlight the hemocompatibility and osteointegration capabilities of AM implants with a faster bone regeneration rate.}, }
@article {pmid40309860, year = {2025}, author = {Gong, J and Liu, H and Duan, F and Che, Y and Yan, Z}, title = {Research on Adaptive Discriminating Method of Brain-Computer Interface for Motor Imagination.}, journal = {Brain sciences}, volume = {15}, number = {4}, pages = {}, doi = {10.3390/brainsci15040412}, pmid = {40309860}, issn = {2076-3425}, support = {MKF202203//Open Foundation of Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University/ ; }, abstract = {(1) Background: Brain-computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual's imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability.}, }
@article {pmid40309849, year = {2025}, author = {Omer, K and Ferracuti, F and Freddi, A and Iarlori, S and Vella, F and Monteriù, A}, title = {Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.}, journal = {Brain sciences}, volume = {15}, number = {4}, pages = {}, doi = {10.3390/brainsci15040359}, pmid = {40309849}, issn = {2076-3425}, abstract = {BACKGROUND/OBJECTIVES: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.<br><br>METHODS: The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.<br><br>RESULTS: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.<br><br>CONCLUSIONS: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.}, }
@article {pmid40309789, year = {2025}, author = {Khan, S and Kallis, L and Mee, H and El Hadwe, S and Barone, D and Hutchinson, P and Kolias, A}, title = {Invasive Brain-Computer Interface for Communication: A Scoping Review.}, journal = {Brain sciences}, volume = {15}, number = {4}, pages = {}, doi = {10.3390/brainsci15040336}, pmid = {40309789}, issn = {2076-3425}, abstract = {BACKGROUND: The rapid expansion of the brain-computer interface for patients with neurological deficits has garnered significant interest, and for patients, it provides an additional route where conventional rehabilitation has its limits. This has particularly been the case for patients who lose the ability to communicate. Circumventing neural injuries by recording from the intact cortex and subcortex has the potential to allow patients to communicate and restore self-expression. Discoveries over the last 10-15 years have been possible through advancements in technology, neuroscience, and computing. By examining studies involving intracranial brain-computer interfaces that aim to restore communication, we aimed to explore the advances made and explore where the technology is heading.<br><br>METHODS: For this scoping review, we systematically searched PubMed and OVID Embase. After processing the articles, the search yielded 41 articles that we included in this review.<br><br>RESULTS: The articles predominantly assessed patients who had either suffered from amyotrophic lateral sclerosis, cervical cord injury, or brainstem stroke, resulting in tetraplegia and, in some cases, difficulty speaking. Of the intracranial implants, ten had ALS, six had brainstem stroke, and thirteen had a spinal cord injury. Stereoelectroencephalography was also used, but the results, whilst promising, are still in their infancy. Studies involving patients who were moving cursors on a screen could improve the speed of movement by optimising the interface and utilising better decoding methods. In recent years, intracortical devices have been successfully used for accurate speech-to-text and speech-to-audio decoding in patients who are unable to speak.<br><br>CONCLUSIONS: Here, we summarise the progress made by BCIs used for communication. Speech decoding directly from the cortex can provide a novel therapeutic method to restore full, embodied communication to patients suffering from tetraplegia who otherwise cannot communicate.}, }
@article {pmid40307763, year = {2025}, author = {Zhang, L and Guan, X and Xue, H and Liu, X and Zhang, B and Liu, S and Ming, D}, title = {Sex-specific patterns in social visual attention among individuals with autistic traits.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {440}, pmid = {40307763}, issn = {1471-244X}, support = {Grant Nos. 23JCZDJC01030//Natural Science Foundation of Tianjin (Key Program)/ ; 2022YGZD02//Tianjin Education Commission Research Program Project/ ; Grant Nos. 81925020//National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Autism is a neurodevelopmental condition more prevalent in males, with sex differences emerging in both prevalence and core symptoms. However, most studies investigating behavioral and cognitive features of autism tend to include more male samples, leading to a male-biased understanding. The sex imbalance limits the specificity of these features, especially in female individuals with autism. Hence, it is necessary to explore sex-related differences in behavioral-cognitive traits linked to autism in the general population.<br><br>METHODS: In this study, we designed a dynamic emotion-discrimination task to investigate sex differences in attention to emotional stimuli among the general population with autistic traits. Behavioral and eye movement data were recorded during the task, and the Autism-Spectrum Quotient (AQ) was used to assess autistic traits. Qualitative and quantitative methods were used to analyze gaze patterns in male and female groups. Additionally, correlation analyses were conducted to examine the relationship between AQ scores and proportion of fixation time in both groups.<br><br>RESULTS: Significant sex differences in attention to the eye regions of faces were observed, with females focusing more on the eyes than males. Correlation analyses revealed that, in males, lower eye-looking was associated with higher levels of autistic traits, whereas no such association was found in females.<br><br>CONCLUSIONS: Overall, these results reveal that attention patterns to emotional faces differed between females and males, and autistic traits predicted the trend of eye-looking in males. These findings suggest that sex-related stratification in social attention should be considered in clinical&#xa0;contexts.}, }
@article {pmid40307561, year = {2025}, author = {,  and Ferrante, O and Gorska-Klimowska, U and Henin, S and Hirschhorn, R and Khalaf, A and Lepauvre, A and Liu, L and Richter, D and Vidal, Y and Bonacchi, N and Brown, T and Sripad, P and Armendariz, M and Bendtz, K and Ghafari, T and Hetenyi, D and Jeschke, J and Kozma, C and Mazumder, DR and Montenegro, S and Seedat, A and Sharafeldin, A and Yang, S and Baillet, S and Chalmers, DJ and Cichy, RM and Fallon, F and Panagiotaropoulos, TI and Blumenfeld, H and de Lange, FP and Devore, S and Jensen, O and Kreiman, G and Luo, H and Boly, M and Dehaene, S and Koch, C and Tononi, G and Pitts, M and Mudrik, L and Melloni, L}, title = {Adversarial testing of global neuronal workspace and integrated information theories of consciousness.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {40307561}, issn = {1476-4687}, abstract = {Different theories explain how subjective experience arises from brain activity[1,2]. These theories have independently accrued evidence, but have not been directly compared[3]. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)[4,5] and global neuronal workspace theory (GNWT)[6-10] via a theory-neutral consortium[11-13]. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof[12]. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here[14-17]. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.}, }
@article {pmid40306303, year = {2025}, author = {Dinh, TH and Singh, AK and Doan, QM and Linh Trung, N and Nguyen, DN and Lin, CT}, title = {An EEG signal smoothing algorithm using upscale and downscale representation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add297}, pmid = {40306303}, issn = {1741-2552}, abstract = {OBJECTIVE: Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface (BCI). This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict processing.<br><br>APPROACH: Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal.<br><br>MAIN RESULTS: Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR &#x2264; 5 dB), where our fitting error is only 86.4%-90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based cognitive conflict processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific cognitive conflict peaks, i.e. the prediction error negativity (PEN) and error-related positive potential (Pe), can be easily observed at multiple line-width levels, while the insignificant ones are eliminated.<br><br>SIGNIFICANCE: These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.}, }
@article {pmid40305243, year = {2025}, author = {Yang, X and Li, Y and Zhang, J and Tian, H and Li, S and Pan, G}, title = {EvoMoE: Evolutionary Mixture-of-Experts for SSVEP-EEG classification with User-Independent Training.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3565882}, pmid = {40305243}, issn = {2168-2208}, abstract = {The analysis of EEG data in BCI systems captures unique individual characteristics, presenting diverse patterns that deviate from conventional identical distribution assumptions. Therefore, applying AI models directly to brain data becomes challenging due to the non-identical distribution issue. Meanwhile, as user numbers in BCI systems rise, scalable models are crucial to handle the growing data volume. Moreover, the limited availability of individual data necessitates the use of collective data for training, requiring models with strong generalization capabilities. To address these challenges, we propose Evolutionary Mixture of Experts (EvoMoE), a framework leveraging a set of diverse experts to model data from individuals. Users with similar distributions are grouped together, allowing experts to handle EEG data with different distribution types. The gating network of EvoMoE selects experts that closely match the distribution of the current sample, effectively tackling non-identical distribution issues. When encountering an unrecognized distribution, a new expert is introduced to accommodate the new data pattern, ensuring model adaptability. Evaluations on two 40-category BCI Speller datasets demonstrate significant performance improvements over state-of-the-art methods. On the BETA dataset, our online EvoMoE achieves 13.06% increase in accuracy and a 27.24-point increase in high information transfer rate (ITR) compared to the online UI method. The Bench dataset shows 3.64% increase in accuracy and a 10.42-point increase in ITR. These qualities make it a promising solution for practical BCI implementation, while setting the stage for the development of comprehensive biological big models.}, }
@article {pmid40302943, year = {2025}, author = {Wu, X and Ye, Y and Sun, M and Mei, Y and Ji, B and Wang, M and Song, E}, title = {Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0192}, pmid = {40302943}, issn = {2692-7632}, abstract = {Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.}, }
@article {pmid40302941, year = {2025}, author = {Feng, C and Cao, L and Wu, D and Zhang, E and Wang, T and Jiang, X and Chen, J and Wu, H and Lin, S and Hou, Q and Zhu, J and Yang, J and Sawan, M and Zhang, Y}, title = {Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0257}, pmid = {40302941}, issn = {2692-7632}, abstract = {Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.}, }
@article {pmid40301357, year = {2025}, author = {Ni, H and Yang, Y and Zhang, F and Sun, Y and Zheng, Y and Zhu, J and Xu, K}, title = {Dataset of long-term multi-site LFP activity with spontaneous chronic seizures in temporal lobe epilepsy rats.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {709}, pmid = {40301357}, issn = {2052-4463}, support = {82272112//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {The characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous seizures and interictal fragments in the chronic period. The LFP data were saved in MATLAB, stored in the Neurodata Without Borders format, and published on the DANDI Archive. We validated the dataset technically through specific signal analysis. In addition, we provided MATLAB codes for basic analyses of this dataset, including power spectral analysis, seizure onset pattern identification, and interictal spike detection. This dataset could reveal how the electrophysiological and epileptic network properties of the brain of rats with chronic TLE changed during epilepsy development, thus help inform the design of adaptive neuromodulation for epilepsy.}, }
@article {pmid40301272, year = {2025}, author = {Tan, Q and Jia, O and Anderson, BA and Jia, K and Gong, M}, title = {Reward history alters priority map based on spatial relationship, but not absolute location.}, journal = {Psychonomic bulletin &amp; review}, volume = {}, number = {}, pages = {}, pmid = {40301272}, issn = {1531-5320}, abstract = {Attention is rapidly directed to stimuli associated with rewards in past experience, independent of current task goals and physical salience of stimuli. However, despite the robust attentional priority given to reward-associated features, studies often indicate negligible priority toward previously rewarded locations. Here, we propose a relational account of value-driven attention, a mechanism that relies on spatial relationship between items to achieve value-guided selections. In three experiments (N = 124), participants were trained to associate specific locations with rewards (e.g., high-reward: top-left; low-reward: top-right). They then performed an orientation-discrimination task where the target's absolute location (top-left or top-right) or spatial relationship ("left of" or "right of") had previously predicted reward. Performance was superior when the target's spatial relationship matched high-reward than low-reward, irrespective of absolute locations. Conversely, the impact of reward was absent when the target matched the absolute location but not the spatial relationship associated with high reward. Our findings challenge the default assumption of location specificity in value-driven attention, demonstrating a generalizable mechanism that humans adopted to integrate value and spatial information into priority maps for adaptive behavior.}, }
@article {pmid40300857, year = {2025}, author = {Zhang, W and Pan, X and Wang, L and Li, W and Dai, X and Zheng, M and Guo, H and Chen, X and Xu, Y and Wu, H and He, Q and Yang, B and Ding, L}, title = {Selective BCL-2 inhibitor triggers STING-dependent antitumor immunity via inducing mtDNA release.}, journal = {Journal for immunotherapy of cancer}, volume = {13}, number = {4}, pages = {}, doi = {10.1136/jitc-2024-010889}, pmid = {40300857}, issn = {2051-1426}, abstract = {BACKGROUND: The stimulator of interferon genes (STING) signaling pathway has been demonstrated to propagate the cancer-immunity cycle and remodel the tumor microenvironment and has emerged as an appealing target for cancer immunotherapy. Interest in STING agonist development has increased, and the candidates hold significant promise; however, most are still in the early stages of human clinical trials. We found that ABT-199 activated the STING pathway to enhance the immunotherapeutic effect, and provided a ready-to-use small molecule drug for STING signaling activation.<br><br>METHODS: Phosphorylation of STING, TBK1, and IRF3, as well as activation of the interferon-I (IFN-I) signaling pathway, were detected following ABT-199 treatment in various colorectal cancer cells. C57BL/6J and BALB/c mice with subcutaneous tumors were employed to evaluate the in vivo therapeutic effects of the ABT-199 and anti-PD-L1 combination. Flow cytometry and ELISA were employed to analyze the level and activity of tumor-infiltrating T lymphocytes. Immunofluorescence and quantitative real-time PCR were conducted to assess the source and accumulation of double stranded DNA (dsDNA) in the cytoplasm. Chemical cross-linking assay, co-immunoprecipitation, and CRISPR/Cas9-mediated knockout were performed to investigate the molecular mechanism underlying ABT-199-induced voltage-dependent anion channel protein 1 (VDAC1) oligomerization and mitochondrial DNA (mtDNA) release.<br><br>RESULTS: ABT-199 significantly activated the STING signaling pathway in various colorectal cancer cells, which was evidenced by increased phosphorylation of TBK1 and IRF3, and upregulation of C-C motif chemokine ligand 5 (CCL5), C-X-C motif chemokine ligand 10 (CXCL10), and interferon beta transcription. By promoting chemokine expression and cytotoxic T-cell infiltration, ABT-199 promoted antitumor immunity and synergized with anti-PD-L1 therapy to improve antitumor efficacy. ABT-199 induced mtDNA accumulation in the cytoplasm and triggered STING signaling via the canonical pathway. cGAS or STING-KO models significantly abolished both STING signaling activation and the antitumor efficacy of ABT-199. Mechanically, ABT-199 promoted VDAC1 oligomerization by disturbing the binding between BCL-2 and VDAC1, thereby facilitating mtDNA release into the cytoplasm. ABT-199-triggered STING signaling was attenuated when VADC1 was knocked out. Consistently, the antitumor effect of ABT-199 in vivo was abolished in the absence of VDAC1.<br><br>CONCLUSIONS: Our results identify a ready-to-use small molecule compound for STING activation, reveal the underlying molecular mechanism through which ABT-199 activates the STING signaling pathway, and provide a theoretical basis for the use of ABT-199 in cancer immunotherapy.}, }
@article {pmid40297854, year = {2025}, author = {Liu, X and Jin, X and Yun, L and Chen, Z}, title = {Prefrontal cortex activity during binocular color fusion and rivalry: an fNIRS study.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1527434}, pmid = {40297854}, issn = {1664-2295}, abstract = {INTRODUCTION: Understanding how the brain processes color information from both the left and right eyes is a significant topic in neuroscience. Binocular color fusion and rivalry, which involve advanced cognitive functions in the prefrontal cortex (PFC), provide a unique perspective for exploring brain activity.<br><br>METHODS: This study used functional near-infrared spectroscopy (fNIRS) to examine PFC activity during binocular color fusion and rivalry conditions. The study included two fNIRS experiments: Experiment 1 employed long-duration (90 s) stimulation to assess brain functional connectivity, while Experiment 2 used short-duration (10 s) repeated stimulation (eight trials), analyzed with a generalized linear model to evaluate brain activation levels. Statistical tests were then conducted to compare the differences in brain functional connectivity strength and activation levels.<br><br>RESULTS: The results indicated that functional connectivity strength was significantly higher during the color fusion condition than the color rivalry condition, and the color rivalry condition was stronger than the Mid-Gray field condition. Additionally, brain activation levels during binocular color fusion were significantly greater, with significant differences concentrated in channel (CH) 12, CH13, and CH14. CH12 is located in the dorsolateral prefrontal cortex, while CH13 and CH14 are in the frontal eye fields, areas associated with higher cognitive functions and visual attention.<br><br>DISCUSSION: These findings suggest that binocular color fusion requires stronger brain integration and higher brain activation levels. Overall, this study demonstrates that color fusion is more cognitively challenging than color rivalry, engaging more attention and executive functions. These results provide theoretical support for the development of color-based brain-computer interfaces and offer new insights into future research on the brain's color-visual information processing mechanisms.}, }
@article {pmid40297442, year = {2025}, author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP}, title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.04.08.25325026}, pmid = {40297442}, abstract = {For stroke participants undergoing motor rehabilitation, brain-machine/computer interfaces (BMI/BCI) can potentially improve the efficacy of robotic or exoskeleton-based therapies by ensuring patient engagement and active participation, through monitoring of motor intent. In such interventions, exploring the network-level understanding of the source space, in terms of various cognitive dimensions such as executive control versus reward processing is fruitful in both improving the existing therapy protocols as well as understanding the subject-level differences. This contrasts to traditional approaches that predominantly investigate rehabilitation from resting state data. Moreover, conventional BMIs used for stroke rehabilitation barely accommodate people suffering from moderate to severe cognitive impairments. In this first-of-the-kind study, we explore the cognitive dimensions of a BMI trial by probing the networks that are core to the BMI performance and propose a network connectivity-based measurement with the potential to characterize the cognitive impairments in patients for closed-loop intervention. Specifically, we tease apart the extent of cognitive evaluation versus executive control aspects of impairments in these patients, by measuring the activation power of a major cognitive evaluation network-the Cingulo-Opercular Network (CON) and a major executive control circuit-the Fronto-Parietal network (FPN), and the connectivity between FPN-CON. We test our hypothesis in a previously collected dataset of electroencephalography (EEG) and structural imaging performed on stroke patients with upper limb impairments, while they underwent an exoskeleton-based BMI intervention for about 12 sessions over 4 weeks. Our logistic regression modeling results suggest that the connectivity between FPN and CON networks and their source powers predict trial failure accurately to about 84.2%. In the future, we aim to integrate these observations into a closed-loop design to adaptively control the cognitive difficulty and passively increase the subject's motivation and attention factor for effective BMI learning.}, }
@article {pmid40296528, year = {2025}, author = {Yang, C and Wang, H and Wang, K and Cao, Z and Ren, F and Zhou, G and Chen, Y and Sun, B}, title = {Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications.}, journal = {ACS nano}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsnano.5c02480}, pmid = {40296528}, issn = {1936-086X}, abstract = {In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.}, }
@article {pmid40295498, year = {2025}, author = {Fang, Z and Sims, CR}, title = {Humans learn generalizable representations through efficient coding.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3989}, pmid = {40295498}, issn = {2041-1723}, support = {2024M761999//China Postdoctoral Science Foundation/ ; }, abstract = {Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.}, }
@article {pmid40294568, year = {2025}, author = {Pan, L and Wang, K and Huang, Y and Sun, X and Meng, J and Yi, W and Xu, M and Jung, TP and Ming, D}, title = {Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {188}, number = {}, pages = {107511}, doi = {10.1016/j.neunet.2025.107511}, pmid = {40294568}, issn = {1879-2782}, abstract = {Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insights and opportunities for the development of robust MI-BCI systems. The code for this research is available at https://github.com/PLC-TJU/RSF.}, }
@article {pmid40292964, year = {2025}, author = {Kasawala, E and Mouli, S}, title = {Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {6}, pages = {}, doi = {10.3390/s25061802}, pmid = {40292964}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; }, abstract = {In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.}, }
@article {pmid40292808, year = {2025}, author = {Gao, D and Wang, Y and Fu, P and Qiu, J and Li, H}, title = {Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {6}, pages = {}, doi = {10.3390/s25061706}, pmid = {40292808}, issn = {1424-8220}, mesh = {*Evoked Potentials, Visual/physiology ; Humans ; Brain-Computer Interfaces ; *Neurons/physiology ; *Models, Neurological ; Visual Cortex/physiology ; Electroencephalography ; Photic Stimulation ; }, abstract = {While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.}, }
@article {pmid40292646, year = {2025}, author = {Jeon, Y and Kim, M and Song, KH}, title = {Development of Hydrogels Fabricated via Stereolithography for Bioengineering Applications.}, journal = {Polymers}, volume = {17}, number = {6}, pages = {}, doi = {10.3390/polym17060765}, pmid = {40292646}, issn = {2073-4360}, support = {Incheon National University (International Cooperative) Research Grant in 2020//Incheon National University/ ; }, abstract = {The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models.}, }
@article {pmid40292025, year = {2025}, author = {Pawlak, WA and Howard, N}, title = {Neuromorphic algorithms for brain implants: a review.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1570104}, pmid = {40292025}, issn = {1662-4548}, abstract = {Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.}, }
@article {pmid40290410, year = {2025}, author = {Ng, JY}, title = {Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.}, journal = {Integrative medicine research}, volume = {14}, number = {2}, pages = {101142}, pmid = {40290410}, issn = {2213-4220}, abstract = {Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores the potential intersection of BCIs and traditional, complementary, and integrative medicine (TCIM). BCIs have shown promise in enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights and measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold potential for improving personalization and expanding the therapeutic efficacy of TCIM interventions. Despite these opportunities, integrating BCIs with TCIM presents considerable ethical, cultural, and practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, and regulatory frameworks must be addressed to ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, including TCIM and conventional practitioners, researchers, and policymakers among other relevant stakeholders is crucial for developing integrative healthcare models that balance innovation with patient safety and respect for diverse healing traditions. Future directions include expanding evidence bases to validate TCIM practices through BCI-enhanced research, fostering equitable access to neurotechnological advancements, and promoting global ethical guidelines to navigate complex sociocultural dynamics. BCIs have the potential to revolutionize TCIM, offering novel solutions for complex health challenges and fostering a more inclusive, integrative approach to healthcare, provided that they are utilized responsibly and ethically.}, }
@article {pmid40289727, year = {2025}, author = {Lin, X and Zhang, X and Chen, J and Liu, J}, title = {Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2413938}, doi = {10.1002/adma.202413938}, pmid = {40289727}, issn = {1521-4095}, support = {DMR-2011754//Directorate for Engineering/ ; }, abstract = {Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties-Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.}, }
@article {pmid40289349, year = {2025}, author = {Pitt, KM and Boster, JB}, title = {Identifying P300 brain-computer interface training strategies for AAC in children: a focus group study.}, journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/07434618.2025.2495912}, pmid = {40289349}, issn = {1477-3848}, abstract = {The integration of Brain-Computer Interface (BCI) technology into Augmentative and Alternative Communication (AAC) systems introduces new complexities in training, particularly for children with diverse cognitive, sensory, motor, and linguistic abilities. Effective AAC training is crucial for enabling individuals to achieve personal goals and enhance social participation. This study aimed to explore potential training strategies for children using P300 based BCI-AAC systems through focus group discussions with experts in AAC and BCI technologies. Participants identified six key themes for effective training: (1) Scaffolding-developing adaptive systems tailored to each child's developmental level, including preteaching, visual display adaptations, and gamification; (2) Verbal Instructions-emphasizing the use of clear, simple language and spoken prompts; (3) Feedback-incorporating immediate feedback and biofeedback methods to reinforce learning; (4) Positioning-ensuring proper trunk stability and addressing electrode placement; (5) Modeling and Physical Supports-using physical cues and demonstrating BCI-AAC use; and (6) Considerations for Visual Impairment-accommodating cortical visual impairment (CVI) with suitable stimuli and environmental adjustments. These insights offer an initial foundation for identifying P300 BCI-AAC training strategies for children. Further systematic research with end users, support networks, and professionals is needed to validate, refine, and expand interventions that support diverse communication needs.}, }
@article {pmid40289107, year = {2025}, author = {Zhi, Y and Guo, Y and Li, S and He, X and Wei, H and Laster, K and Wu, Q and Zhao, D and Xie, J and Ruan, S and Lemoine, NR and Li, H and Dong, Z and Liu, K}, title = {FBL promotes hepatocellular carcinoma tumorigenesis and progression by recruiting YY1 to enhance CAD gene expression.}, journal = {Cell death &amp; disease}, volume = {16}, number = {1}, pages = {348}, pmid = {40289107}, issn = {2041-4889}, abstract = {Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence suggests that epigenetic dysregulation contributes to the initiation and progression of HCC. We aimed to investigate key epigenetic regulators that contribute to tumorigenesis and progression, providing a theoretical basis for targeted therapy for HCC. We performed a comprehensive epigenetic analysis of differentially expressed genes in LIHC from the TCGA database. We identified fibrillarin (FBL), an rRNA 2'-O-methyltransferase, as an essential contributor to HCC. A series of in vitro and in vivo biological experiments were performed to investigate the potential mechanisms of FBL. FBL knockdown suppressed the proliferation of HCC cells. In vivo studies using cell-derived xenograft (CDX), patient-derived xenograft (PDX), and diethylnitrosamine (DEN)-induced HCC models in Fbl liver-specific knockout mice demonstrated the critical role of FBL in HCC carcinogenesis and progression. Mechanistically, FBL regulates the expression of CAD in HCC cells by recruiting YY1 to the CAD promoter region. We also revealed that fludarabine phosphate is a novel inhibitor of FBL and can inhibit HCC growth in vitro and in vivo. The antitumor activity of lenvatinib has been shown to be synergistically enhanced by fludarabine phosphate. Our study highlights the cancer-promoting role of the FBL-YY1-CAD axis in HCC and identifies fludarabine phosphate as a novel inhibitor of FBL. A schematic diagram depicting the FBL-YY1-CAD signaling pathway and its regulatory role in HCC progression.}, }
@article {pmid40288968, year = {2025}, author = {Pan, L and Sun, X and Wang, K and Cao, Y and Xu, M and Ming, D}, title = {[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {2}, pages = {272-279}, doi = {10.7507/1001-5515.202411035}, pmid = {40288968}, issn = {1001-5515}, abstract = {Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.}, }
@article {pmid40288455, year = {2025}, author = {Tang, Y and Tang, Z and Zhou, Y and Luo, Y and Wen, X and Yang, Z and Jiang, T and Luo, N}, title = {A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression.}, journal = {Journal of affective disorders}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jad.2025.04.142}, pmid = {40288455}, issn = {1573-2517}, abstract = {BACKGROUND: Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.<br><br>METHODS: PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.<br><br>RESULTS: After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.<br><br>CONCLUSION: The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.}, }
@article {pmid40288311, year = {2025}, author = {Li, L and Jiang, C}, title = {Electrodeposited coatings for neural electrodes: A review.}, journal = {Biosensors &amp; bioelectronics}, volume = {282}, number = {}, pages = {117492}, doi = {10.1016/j.bios.2025.117492}, pmid = {40288311}, issn = {1873-4235}, abstract = {Neural electrodes play a pivotal role in ensuring safe stimulation and high-quality recording for various bioelectronics such as neuromodulation devices and brain-computer interfaces. With the miniaturization of electrodes and the increasing demand for multi-functionality, the incorporation of coating materials via electrodeposition to enhance electrodes performance emerges as a highly effective strategy. These coatings not only substantially improve the stimulation and recording performance of electrodes but also introduce additional functionalities. This review began by outlining the application scenarios and critical requirements of neural electrodes. It then delved into the deposition principles and key influencing factors. Furthermore, the advancements in the electrochemical performance and adhesion stability of these coatings were reviewed. Ultimately, the latest innovative works in the electrodeposited coating applications were highlighted, and future perspectives were summarized.}, }
@article {pmid40287824, year = {2025}, author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG}, title = {The potential power of neuralink - how brain-machine interfaces can revolutionize medicine.}, journal = {Expert review of medical devices}, volume = {}, number = {}, pages = {}, doi = {10.1080/17434440.2025.2498457}, pmid = {40287824}, issn = {1745-2422}, }
@article {pmid40287725, year = {2025}, author = {Zhang, X and Xie, L and Liu, W and Liang, S and Huang, L and Wang, M and Tian, L and Zhang, L and Liang, Z and Li, H and Huang, G}, title = {Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {97}, pmid = {40287725}, issn = {1743-0003}, support = {62201356//National Natural Science Foundation of China/ ; 62276169//National Natural Science Foundation of China/ ; 62271326//National Natural Science Foundation of China/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; JCYJ20210324134401004//Shenzhen Science and Technology Innovation Program/ ; JCYJ20241202124222027//Shenzhen Science and Technology Innovation Program/ ; C2401028//Shenzhen Medical Research Foundation/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Exoskeleton Device ; Movement/physiology ; Adult ; Aged ; Algorithms ; Stroke/physiopathology ; Evoked Potentials/physiology ; Motor Cortex/physiopathology ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.<br><br>METHODS: EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.<br><br>RESULTS: Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 &#xb1; 4.51 &#xa0;&#x3bc;V; ipsilateral: - 4.33 &#xb1; 3.69 &#xa0;&#x3bc;V). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.<br><br>CONCLUSIONS: These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.}, }
@article {pmid40281719, year = {2025}, author = {Wan, X and Liu, Z and Yao, Y and Wan Hasan, WZ and Liu, T and Duan, D and Xie, X and Wen, D}, title = {Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {4}, pages = {}, doi = {10.3390/bioengineering12040359}, pmid = {40281719}, issn = {2306-5354}, support = {62206014//National Natural Science Foundation of China/ ; 62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; }, abstract = {Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.}, }
@article {pmid40281692, year = {2025}, author = {Acuña Luna, KP and Hernandez-Rios, ER and Valencia, V and Trenado, C and Peñaloza, C}, title = {Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive-Motor Rehabilitation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {12}, number = {4}, pages = {}, doi = {10.3390/bioengineering12040331}, pmid = {40281692}, issn = {2306-5354}, abstract = {This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.}, }
@article {pmid40281628, year = {2025}, author = {Atkinson, C and Lombardi, L and Lang, M and Keesey, R and Hawthorn, R and Seitz, Z and Leuthardt, EC and Brunner, P and Seáñez, I}, title = {Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {95}, pmid = {40281628}, issn = {1743-0003}, support = {K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; U24-NS109103/NH/NIH HHS/United States ; U24-NS109103/NH/NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; Male ; Female ; Adult ; Electroencephalography ; *Spinal Cord Stimulation/methods ; *Brain-Computer Interfaces ; Middle Aged ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Movement/physiology ; Young Adult ; *Transcutaneous Electric Nerve Stimulation/methods ; Sensorimotor Cortex/physiology ; Discriminant Analysis ; }, abstract = {Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). However, its efficacy is limited to areas with remaining sensorimotor function. Spinal cord stimulation (SCS) creates a temporary prosthetic effect that may allow further rehabilitation-induced recovery in individuals without remaining sensorimotor function, thereby extending the therapeutic reach of motor rehabilitation to individuals with more severe injuries. In this work, we report our first steps in developing a non-invasive brain-spine interface (BSI) based on electroencephalography (EEG) and transcutaneous spinal cord stimulation (tSCS). The objective of this study was to identify EEG-based neural correlates of lower limb movement in the sensorimotor cortex of unimpaired individuals (N = 17) and to quantify the performance of a linear discriminant analysis (LDA) decoder in detecting movement onset from these neural correlates. Our results show that initiation of knee extension was associated with event-related desynchronization in the central-medial cortical regions at frequency bands between 4 and 44 Hz. Our neural decoder using µ (8-12 Hz), low β (16-20 Hz), and high β (24-28 Hz) frequency bands achieved an average area under the curve (AUC) of 0.83 ± 0.06 s.d. (n = 7) during a cued movement task offline. Generalization to imagery and uncued movement tasks served as positive controls to verify robustness against movement artifacts and cue-related confounds, respectively. With the addition of real-time decoder-modulated tSCS, the neural decoder performed with an average AUC of 0.81 ± 0.05 s.d. (n = 9) on cued movement and 0.68 ± 0.12 s.d. (n = 9) on uncued movement. Our results suggest that the decrease in decoder performance in uncued movement may be due to differences in underlying cortical strategies between conditions. Furthermore, we explore alternative applications of the BSI system by testing neural decoders trained on uncued movement and imagery tasks. By developing a non-invasive BSI, tSCS can be timed to be delivered only during voluntary effort, which may have implications for improving rehabilitation.}, }
@article {pmid40280929, year = {2025}, author = {Bai, Y and Tang, Q and Zhao, R and Liu, H and Zhang, S and Guo, M and Guo, M and Wang, J and Wang, C and Xing, M and Ni, G and Ming, D}, title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {701}, pmid = {40280929}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Reading ; *Semantics ; China ; *Language ; *Natural Language Processing ; East Asian People ; }, abstract = {Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400-450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models.}, }
@article {pmid40280532, year = {2025}, author = {Gazerani, P}, title = {The neuroplastic brain: current breakthroughs and emerging frontiers.}, journal = {Brain research}, volume = {}, number = {}, pages = {149643}, doi = {10.1016/j.brainres.2025.149643}, pmid = {40280532}, issn = {1872-6240}, abstract = {Neuroplasticity, the brain's capacity to reorganize itself by forming new neural connections, is central to modern neuroscience. Once believed to occur only during early development, research now shows that plasticity continues throughout the lifespan, supporting learning, memory, and recovery from injury or disease. Substantial progress has been made in understanding the mechanisms underlying neuroplasticity and their therapeutic applications. This overview article examines synaptic plasticity, structural remodeling, neurogenesis, and functional reorganization, highlighting both adaptive (beneficial) and maladaptive (harmful) processes across different life stages. Recent strategies to harness neuroplasticity, ranging from pharmacological agents and lifestyle interventions to cutting-edge technologies like brain-computer interfaces (BCIs) and targeted neuromodulation are evaluated in light of current empirical evidence. Contradictory findings in the literature are addressed, and methodological limitations that hamper widespread clinical adoption are discussed. The ethical and societal implications of deploying novel neuroplasticity-based interventions, including issues of equitable access, data privacy, and the blurred line between treatment and enhancement, are then explored in a structured manner. By integrating mechanistic insights, empirical data, and ethical considerations, the aim is to provide a comprehensive and balanced perspective for researchers, clinicians, and policymakers working to optimize brain health across diverse populations.}, }
@article {pmid40280369, year = {2025}, author = {Zhang, Y and Gao, Y and Zhou, J and Zhang, Z and Feng, M and Liu, Y}, title = {Advances in Brain-Computer Interface Controlled Functional Electrical Stimulation for Upper Limb Recovery After Stroke.}, journal = {Brain research bulletin}, volume = {}, number = {}, pages = {111354}, doi = {10.1016/j.brainresbull.2025.111354}, pmid = {40280369}, issn = {1873-2747}, abstract = {Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.}, }
@article {pmid40280291, year = {2025}, author = {Han, MJ and Oh, Y and Ann, Y and Kang, S and Baeg, E and Hong, SJ and Sohn, H and Kim, SG}, title = {Whole-brain effective connectivity of the sensorimotor system using 7T fMRI with electrical microstimulation in non-human primates.}, journal = {Progress in neurobiology}, volume = {}, number = {}, pages = {102760}, doi = {10.1016/j.pneurobio.2025.102760}, pmid = {40280291}, issn = {1873-5118}, abstract = {The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.}, }
@article {pmid40280150, year = {2025}, author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, S and Brandman, D}, title = {Speech motor cortex enables BCI cursor control and click.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add0e5}, pmid = {40280150}, issn = {1741-2552}, abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. Approach. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. Main results. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. Significance. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041).}, }
@article {pmid40280132, year = {2025}, author = {Zhou, N and Chen, J and Hu, M and Wen, N and Cai, W and Li, P and Zhao, L and Meng, Y and Zhao, D and Yang, X and Liu, S and Huang, F and Zhao, C and Feng, X and Jiang, Z and Xie, E and Pan, H and Cen, Z and Chen, X and Luo, W and Tang, B and Min, J and Wang, F and Yang, J and Xu, H}, title = {SLC7A11 is an unconventional H[+] transporter in lysosomes.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2025.04.004}, pmid = {40280132}, issn = {1097-4172}, abstract = {Lysosomes maintain an acidic pH of 4.5-5.0, optimal for macromolecular degradation. Whereas proton influx is produced by a V-type H[+] ATPase, proton efflux is mediated by a fast H[+] leak through TMEM175 channels, as well as an unidentified slow pathway. A candidate screen on an orphan lysosome membrane protein (OLMP) library enabled us to discover that SLC7A11, the protein target of the ferroptosis-inducing compound erastin, mediates a slow lysosomal H[+] leak through downward flux of cystine and glutamate, two H[+] equivalents with uniquely large but opposite concentration gradients across lysosomal membranes. SLC7A11 deficiency or inhibition caused lysosomal over-acidification, reduced degradation, accumulation of storage materials, and ferroptosis, as well as facilitated α-synuclein aggregation in neurons. Correction of abnormal lysosomal acidity restored lysosome homeostasis and prevented ferroptosis. These studies have revealed an unconventional H[+] transport conduit that is integral to lysosomal flux of protonatable metabolites to regulate lysosome function, ferroptosis, and Parkinson's disease (PD) pathology.}, }
@article {pmid40280131, year = {2025}, author = {Xin, Q and Wang, J and Zheng, J and Tan, Y and Jia, X and Ni, Z and Xu, Z and Feng, J and Wu, Z and Li, Y and Li, XM and Ma, H and Hu, H}, title = {Neuron-astrocyte coupling in lateral habenula mediates depressive-like behaviors.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2025.04.010}, pmid = {40280131}, issn = {1097-4172}, abstract = {The lateral habenula (LHb) neurons and astrocytes have been strongly implicated in depression etiology, but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely moving mice, we discover that stress induces a most rapid astrocytic calcium rise and a bimodal neuronal response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/adenosine, LHb astrocytes mediate the second-wave LHb neuronal activation and norepinephrine (NE) release. Activation or inhibition of LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors, respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.}, }
@article {pmid40280000, year = {2025}, author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T}, title = {Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia.}, journal = {Stem cell research}, volume = {86}, number = {}, pages = {103713}, doi = {10.1016/j.scr.2025.103713}, pmid = {40280000}, issn = {1876-7753}, abstract = {Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo.}, }
@article {pmid40279233, year = {2025}, author = {Aung, HW and Jiao Li, J and An, Y and Su, SW}, title = {A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3558171}, pmid = {40279233}, issn = {2162-2388}, abstract = {Brain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson correlation coefficient (PCC) method in the same framework. In this research, we advance the field by applying a reinforcement learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG_GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric dueling deep Q network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 ms. This model illustrates the transformative effect of the RL in EEG MI time point classification.}, }
@article {pmid40277624, year = {2025}, author = {Zhang, W and Tang, X and Dang, X and Wang, M}, title = {A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {4}, pages = {}, doi = {10.3390/biomimetics10040225}, pmid = {40277624}, issn = {2313-7673}, abstract = {Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject's EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods.}, }
@article {pmid40277574, year = {2025}, author = {Jiao, P and Jia, Q and Li, S and Shan, J and Xu, W and Wang, Y and Liu, Y and Wang, M and Song, Y and Zhang, Y and Yu, Y and Wang, M and Cai, X}, title = {Distinct Neural Activities in Hippocampal Subregions Revealed Using a High-Performance Wireless Microsystem with PtNPs/PEDOT:PSS-Enhanced Microelectrode Arrays.}, journal = {Biosensors}, volume = {15}, number = {4}, pages = {}, doi = {10.3390/bios15040262}, pmid = {40277574}, issn = {2079-6374}, support = {2022YFC2402501//the National Key Research and Development Program of China/ ; 2022YFC2402503//the National Key Research and Development Program of China/ ; 2022YFB3205602//the National Key Research and Development Program of China/ ; T2293730//the National Natural Science Foundation of China/ ; T2293731//the National Natural Science Foundation of China/ ; 61960206012//the National Natural Science Foundation of China/ ; 62121003//the National Natural Science Foundation of China/ ; 62333020//the National Natural Science Foundation of China/ ; 62171434//the National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; PTYQ2024BJ0009//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; }, mesh = {Animals ; Microelectrodes ; *Wireless Technology ; Rats ; *Hippocampus/physiology ; Platinum/chemistry ; Polymers/chemistry ; Polystyrenes/chemistry ; Metal Nanoparticles/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Biosensing Techniques ; Rats, Sprague-Dawley ; Male ; }, abstract = {Wireless microsystems for neural signal recording have emerged as a solution to overcome the limitations of tethered systems, which restrict the mobility of subjects and introduce noise interference. However, existing microsystems often face data throughput, signal processing, and long-distance wireless transmission challenges. This study presents a high-performance wireless microsystem capable of 32-channel, 30 kHz real-time recording, featuring Field Programmable Gate Array (FPGA)-based signal processing to reduce transmission load. The microsystem is integrated with platinum nanoparticles/poly (3,4-ethylenedioxythiophene) polystyrene sulfonate-enhanced microelectrode arrays for improved signal quality. A custom NeuroWireless platform was developed for seamless data reception and storage. Experimental validation in rats demonstrated the microsystem's ability to detect spikes and local field potentials from the hippocampal CA1 and CA2 subregions. Comparative analysis of the neural signals revealed distinct activity patterns between these subregions. The wireless microsystem achieves high accuracy and throughput over distances up to 30 m, demonstrating its resilience and potential for neuroscience research. This work provides a compact, adaptable solution for multi-channel neural signal detection and offers a foundation for future applications in brain-computer interfaces.}, }
@article {pmid40277527, year = {2025}, author = {Tavakolidakhrabadi, A and Stark, M and Küenzi, A and Carrara, S and Bessire, C}, title = {Optimized Microfluidic Biosensor for Sensitive C-Reactive Protein Detection.}, journal = {Biosensors}, volume = {15}, number = {4}, pages = {}, doi = {10.3390/bios15040214}, pmid = {40277527}, issn = {2079-6374}, support = {52116.1 IP-LS//Innosuisse - Swiss Innovation Agency/ ; }, mesh = {*C-Reactive Protein/analysis ; *Biosensing Techniques ; Metal Nanoparticles/chemistry ; Gold/chemistry ; Immunoassay ; Humans ; Point-of-Care Testing ; Microfluidics ; }, abstract = {Lateral flow immunoassays (LFIAs) were integrated into microfluidic chips and tested to enhance point-of-care testing (POCT), with the aim of improving sensitivity and expanding the range of CRP detection. The microfluidic approach improves upon traditional methods by precisely controlling fluid speed, thus enhancing sensitivity and accuracy in CRP measurements. The microfluidic approach also enables a one-step detection system, eliminating the need for buffer solution steps and reducing the nitrocellulose (NC) pad area to just the detection test line. This approach minimizes the non-specific binding of conjugated antibodies to unwanted areas of the NC pad, eliminating the need to block those areas, which enhances the sensitivity of detection. The gold nanoparticle method detects CRP in the high-sensitivity range of 1-10 μg/mL, which is suitable for chronic disease monitoring. To broaden the CRP detection range, including infection levels beyond 10 μg/mL, fluorescent labels were introduced, extending the measuring range from 1 to 70 μg/mL. Experimental results demonstrate that integrating microfluidic technology significantly enhances operational efficiency by precisely controlling the flow rate and optimizing the mixing efficiency while reducing fabrication resources by eliminating the need for separate pads, making these methods suitable for resource-limited settings. Microfluidics also provides greater control over fluid dynamics compared to traditional LFIA methods, which contributes to enhanced detection sensitivity even with lower sample volumes and no buffer solution, helping to enhance the usability of POCT. These findings highlight the potential to develop accessible, accurate, and cost-effective diagnostic tools essential for timely medical interventions at the POC.}, }
@article {pmid40275590, year = {2025}, author = {Haghani Dogahe, M and Mahan, MA and Zhang, M and Bashiri Aliabadi, S and Rouhafza, A and Karimzadhagh, S and Feizkhah, A and Monsef, A and Habibi Roudkenar, M}, title = {Advancing Prosthetic Hand Capabilities Through Biomimicry and Neural Interfaces.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683251331593}, doi = {10.1177/15459683251331593}, pmid = {40275590}, issn = {1552-6844}, abstract = {Background and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand through innovative prosthetic designs. Central to this endeavor is NIT, facilitating seamless communication between artificial devices and the human nervous system. Recent advances in fabrication methods have propelled brain-computer interfaces, enabling precise control of prosthetic hands by decoding neural activity.ResultsAnatomical complexities of the human hand underscore the importance of understanding biomechanics, neuroanatomy, and control mechanisms for crafting effective prosthetic solutions. Furthermore, achieving the goal of a fully functional cyborg hand necessitates a multidisciplinary approach and biomimetic design to replicate the body's inherent capabilities. By incorporating the expertise of clinicians, tissue engineers, bioengineers, electronic and data scientists, the next generation of the implantable devices is not only anatomically and biomechanically accurate but also offer intuitive control, sensory feedback, and proprioception, thereby pushing the boundaries of current prosthetic technology.ConclusionBy integrating machine learning algorithms, biomechatronic principles, and advanced surgical techniques, prosthetic hands can achieve real-time control while restoring tactile sensation and proprioception. This manuscript contributes novel approaches to prosthetic hand development, with potential implications for enhancing the functionality, durability, and safety of the prosthetic limb.}, }
@article {pmid40274133, year = {2025}, author = {Meng, L and Wang, D and Ma, J and Shi, Y and Zhao, H and Wang, Y and Shi, Q and Zhu, X and Ming, D}, title = {Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.}, journal = {Neurobiology of disease}, volume = {}, number = {}, pages = {106915}, doi = {10.1016/j.nbd.2025.106915}, pmid = {40274133}, issn = {1095-953X}, abstract = {BACKGROUND: Despite prior studies on early-stage PD brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes, remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in Parkinson's disease (PD) for improving personalized treatment.<br><br>METHODS: Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.<br><br>RESULTS: Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.<br><br>CONCLUSIONS: This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.<br><br>TRIAL REGISTRATION: ChiCTR2300067657.}, }
@article {pmid40273947, year = {2025}, author = {Ahmadi, H and Mesin, L}, title = {Universal semantic feature extraction from EEG signals: A task-independent framework.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/add08f}, pmid = {40273947}, issn = {1741-2552}, abstract = {Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations. Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials (SSVEP), and event-related potentials (ERP, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features. Main Results: Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV 2a and BCICIV 2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-SNE and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the selected model configuration. Significance: This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning (DL) methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface (BCI) appli- cations, cross-task EEG analysis, and future developments in semantic EEG processing.}, }
@article {pmid40271395, year = {2025}, author = {Han, CH and Kim, SU and Lim, KS and Jung, YJ and Lee, S and Kim, SH and Hwang, HJ}, title = {Evaluation of a silicone-based flexible dry electrode for measuring human bioelectrical signals.}, journal = {Biomedical engineering letters}, volume = {15}, number = {3}, pages = {563-574}, doi = {10.1007/s13534-025-00471-x}, pmid = {40271395}, issn = {2093-985X}, abstract = {The development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior wearability and reliable bioelectrical signal monitoring. To evaluate its performance, we compared its impedance and flexibility with those of a commercial electrode. Additionally, its compatibility for measuring biological signals was assessed through performance comparisons across various bioelectrical signals. Fourteen healthy participants performed three experimental paradigms: (1) eyes closed and open to measure alpha electroencephalography (EEG) as well as resting-state electrocardiography (ECG), (2) eye blinking to measure electrooculography (EOG), and (3) wrist movement to measure electromyography (EMG). All bioelectrical signals were measured simultaneously using both the silicone-based dry electrode and a commercial dry electrode. The performance of the silicone-based dry electrode was evaluated by comparing the signal quality of both electrodes. The silicone-based dry electrode exhibited lower electrical impedance (39.43 kΩ on average, p = 0.0058) and greater flexibility (Young's modulus: silicone 1.51 ± 0.10 MPa vs. commercial 2.46 ± 0.38 MPa) compared to the commercial dry electrode. Overall, there were minimal differences in signal quality between the two electrodes: i) EEG (α power SNR: silicone 1.39 ± 0.34 vs. commercial 1.36 ± 0.29), ii) ECG (R-peak recall: 99.20 ± 2.50%, correlation coefficient: 0.96 ± 0.08), iii) EOG (eye blink recall: 100.00%, correlation coefficient: 0.98 ± 0.03), and iv) EMG (no significant difference in SNR values). These findings indicate that the developed electrode not only ensures superior flexibility but also maintains compatible electrical properties for measuring various bioelectrical signals.}, }
@article {pmid40270987, year = {2025}, author = {Jiang, Z and Hu, K and Qu, J and Bian, Z and Yu, D and Zhou, J}, title = {Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1559335}, doi = {10.3389/fninf.2025.1559335}, pmid = {40270987}, issn = {1662-5196}, abstract = {INTRODUCTION: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.<br><br>METHODS: To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.<br><br>RESULTS AND DISCUSSION: The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.}, }
@article {pmid40270567, year = {2025}, author = {Hammond, L and Rowley, D and Tuck, C and Floreani, ED and Wieler, A and Kim, VS and Bahari, H and Andersen, J and Kirton, A and Kinney-Lang, E}, title = {BCI move: exploring pediatric BCI-controlled power mobility.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1456692}, doi = {10.3389/fnhum.2025.1456692}, pmid = {40270567}, issn = {1662-5161}, abstract = {INTRODUCTION: Children and young people (CYP) with severe physical disabilities often experience barriers to independent mobility, placing them at risk for developmental impairments and restricting their independence and participation. Pilot work suggests that brain-computer interface (BCIs) could enable powered mobility control for children with motor disabilities. We explored how severely disabled CYP could use BCI to achieve individualized, functional power mobility goals and acquire power mobility skills. We also explored the practicality of pediatric BCI-enabled power mobility.<br><br>METHODS: Nine CYP aged 7-17 years with severe physical disabilities and their caregivers participated in up to 12 BCI-enabled power mobility training sessions focused on a personalized power mobility goal. Goal achievement was assessed using the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS). The Assessment for Learning Powered Mobility (ALP) was used to measure session-by-session power mobility skill acquisition. BCI set-up and calibration metrics, perceived workload, and participant engagement were also reported.<br><br>RESULTS: Significant improvements in COPM performance (Z&#x202f;=&#x202f;-2.869, adjusted p&#x202f;=&#x202f;0.012) and satisfaction scores (Z&#x202f;=&#x202f;-2.809, adjusted p&#x202f;=&#x202f;0.015) and GAS T scores (Z&#x202f;=&#x202f;-2.805, p&#x202f;=&#x202f;0.005) were observed following the intervention. ALP scores displayed a small but significant increase over time (R [2]&#x202f;=&#x202f;0.07-0.19; adjusted p&#x202f;=&#x202f;<0.001-0.039), with 7/9 participants achieving increased overall ALP scores following the intervention. Setup and calibration times were practical although calibration consistency was highly variable. Participants reported moderate workload with no significant change over time (R [2]&#x202f;=&#x202f;0.00-0.13; adjusted p&#x202f;=&#x202f;0.006-1.000), although there was a trend towards increased frustration over time(R [2]&#x202f;=&#x202f;0.13; adjusted p&#x202f;=&#x202f;0.006).<br><br>DISCUSSION: Participants were highly engaged throughout the intervention. BCI-enabled power mobility appears to help CYP with severe physical disabilities achieve personalized power mobility goals and acquire power mobility skills. BCI-enabled power mobility training also appears to be practical, but BCI performance optimization and skill acquisition may be needed to translate this technology into clinical use.}, }
@article {pmid39711704, year = {2025}, author = {Deng, G and Niu, M and Luo, Y and Rao, S and Xie, J and Yu, Z and Liu, W and Zhao, S and Pan, G and Li, X and Deng, W and Guo, W and Li, T and Jiang, H}, title = {A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39711704}, abstract = {Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain gaps. Traditional scoring is labor-intensive, while deep learning models often fail to generalize across datasets. Here, we present LPSGM, a unified and flexible large polysomnography (PSG) model designed to enhance cross-center generalization in sleep staging and enable fine-tuning for disease diagnosis. Trained on 220,500 hours of PSG data from 16 public datasets, LPSGM integrates domain-adaptive learning and supports variable-channel configurations, achieving performance comparable to models trained directly on target-center data. In a prospective clinical study, LPSGM matches expert-level accuracy with lower variability. When fine-tuned, it attains 88.01% accuracy in narcolepsy detection and 100% in depression detection. These results establish LPSGM as a scalable, plug-and-play solution for automated PSG analysis, bridging the gap between sleep staging and clinical deployment.}, }
@article {pmid40269846, year = {2025}, author = {Mansour, S and Giles, J and Nair, KPS and Marshall, R and Ali, A and Arvaneh, M}, title = {A clinical trial evaluating feasibility and acceptability of a brain-computer interface for telerehabilitation in patients with stroke.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {91}, pmid = {40269846}, issn = {1743-0003}, support = {MC-PC-19051//UK Medical Research Council/ ; }, abstract = {BACKGROUND: We have created a groundbreaking telerehabilitation system known as Tele BCI-FES. This innovative system merges brain-computer interface (BCI) and functional electrical stimulation (FES) technologies to rehabilitate upper limb function following a stroke. Our system pioneers the concept of allowing patients to undergo BCI therapy from the comfort of their homes, while ensuring supervised therapy and real-time adjustment capabilities. In this paper, we introduce our single-arm clinical trial, which evaluates the feasibility and acceptance of this proposed system as a telerehabilitation solution for upper extremity recovery in stroke survivors.<br><br>METHOD: The study involved eight chronic patients with stroke and their caregivers who were recruited to attend nine home-based Tele BCI-FES sessions (three sessions per week) while receiving remote support from the research team. The primary outcomes of this study were recruitment and retention rates, as well as participants perception on the adoption of technology. The secondary outcomes involved assessing improvements in upper extremity function using the Fugl-Meyer Assessment for Upper Extremity (FMA_UE) and the Leeds Arm Spasticity Impact Scale.<br><br>RESULTS: Seven chronic patients with stroke completed the home-based Tele BCI-FES sessions, with high retention (87.5%) and recruitment rates (86.7%). Although participants provided mixed feedback on setup ease, they found the system progressively easier to use, and the setup process became more efficient with continued sessions. Participants suggested modifications to enhance user experience. Following the intervention, a significant increase in FMA_UE scores was observed, with an average improvement of 3.83 points (p = 0.032). The observed improvement of 3.83 points in the FMA-UE score approaches the reported Minimal clinically important difference of 4.25 points for patients with chronic stroke.<br><br>CONCLUSION: This study serves as a proof of concept, showcasing the feasibility and acceptability of the proposed Tele BCI-FES system for rehabilitating the upper extremities of stroke survivors. While some participants demonstrated significant improvements in FMA-UE scores, these findings are not generalizable, as they were derived from a small-scale feasibility study. The results should be interpreted cautiously within the study's specific context. Additionally, the intervention was not compared to other therapeutic approaches, limiting conclusions regarding its relative effectiveness. To further validate the efficacy of the proposed Tele BCI-FES system, it is essential to conduct additional research with larger sample sizes and extended rehabilitation sessions. Moreover, future studies should include comparisons with other therapeutic approaches to better evaluate the relative effectiveness of this intervention. Trial registration This clinical study is registered at clinicaltrials.gov https://clinicaltrials.gov/study/NCT05215522 under the study identifier (NCT05215522) and registered with the ISRCTN registry https://doi.org/10.1186/ISRCTN42991002 (ISRCTN42991002).}, }
@article {pmid40264507, year = {2025}, author = {Decker, J and Daeglau, M and Zich, C and Kranczioch, C}, title = {Nature documentaries vs. quiet rest: no evidence for an impact on event-related desynchronization during motor imagery and neurofeedback.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1539172}, pmid = {40264507}, issn = {1662-5161}, abstract = {Motor imagery (MI) in combination with neurofeedback (NF) has emerged as a promising approach in motor neurorehabilitation, facilitating brain activity modulation and promoting motor learning. Although MI-NF has been demonstrated to enhance motor performance and cortical plasticity, its efficacy varies considerably across individuals. Various context factors have been identified as influencing neurophysiological outcomes in motor execution and MI, however, their specific impact on event-related desynchronization (ERD), a key neurophysiological marker in NF, remains insufficiently understood. Previous research suggested that declarative interference following MI-NF may serve as a context factor hindering the progression of ERD. Yet, no significant changes in ERD within the mu and beta (8-30 Hz) frequency bands were observed across blocks in either a declarative interference or a control condition. This raises the question of whether the absence of ERD modulation could be attributed to the break task that was common to both declarative interference and control condition: watching nature documentaries immediately after MI blocks. To investigate this, we conducted a follow-up study replicating the original methodology while collecting new data. We compared NF-MI-ERD between groups with and without nature documentaries as a post-MI condition. Participants completed three sessions of kinesthetic MI-NF training involving a finger-tapping task over two consecutive days, with quiet rest as the post-MI condition (group quiet rest). 64-channel EEG data were analyzed from 17 healthy participants (8 females, 18-35 years, M and SD: 25.2 ± 4.2 years). Data were compared to a previously recorded dataset (group documentaries), in which 17 participants (10 females, 23-32 years, M and SD: 25.8 ± 2.5 years) watched nature documentaries after MI blocks. The results showed no significant main effects for blocks or group, though a session-by-group interaction was observed. Post-hoc tests, however, did not reveal significant differences in ERD development between the groups across individual blocks. These findings do not provide evidence that nature documentaries used as a post-MI condition negatively affect across-block development of NF-MI-ERD. This study highlights the importance of exploring additional context factors in MI-NF training to better understand their influence on ERD development.}, }
@article {pmid40263649, year = {2025}, author = {Li, X and Dai, P and Yuan, Y}, title = {[Perioperative safety assessment and complications follow-up of simultaneous bilateral cochlear implantation in young infants].}, journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery}, volume = {39}, number = {5}, pages = {413-418;424}, doi = {10.13201/j.issn.2096-7993.2025.05.004}, pmid = {40263649}, issn = {2096-7993}, mesh = {Humans ; *Cochlear Implantation/adverse effects/methods ; Infant ; *Postoperative Complications ; *Hearing Loss, Sensorineural/surgery ; Follow-Up Studies ; Male ; Perioperative Period ; Female ; Cochlear Implants ; }, abstract = {Objective:To evaluate the perioperative safety and long-term complications of simultaneous bilateral cochlear implantation（BCI） in young infants, providing reference data for clinical BCI in young children. Methods:Seventy-four infants aged 6-23 months with congenital severe to profound sensorineural hearing loss who were candidates for cochlear implantation at the Department of Otolaryngology, Chinese PLA General Hospital between August 2018 and August 2019 were consecutively enrolled. Parents made the decision to implant either unilaterally or bilaterally. Participants were divided into unilateral cochlear implantation（UCI） group（before and after 12 months of age） and simultaneous BCI group（before and after 12 months of age）. Safety indicators, including perioperative risk variables, complications, and other postoperative adverse events were monitored, with complications followed up for 5-6 years. Comparisons were made between the BCI and UCI, as well as between implantation before and after 12 months of age regarding perioperative safety and long-term complications. Results:A total of 40 BCI patients（23 before 12 months, 17 after 12 months） and 34 UCI patients（20 before 12 months, 14 after 12 months） were included in the study. Regarding perioperative risk variables, the BCI group showed significantly longer anesthesia duration, operative time, and greater blood loss compared to the UCI group, though less than twice that of the UCI group; no anesthetic complications occurred in either group; and there was no significant difference in postoperative hospital stay between the groups. Regarding surgical complications during the 5-year follow-up period, the BCI group experienced 7 complications（2 major, 5 minor）, while the UCI group had 7 complications（1 major, 6 minor）, with no statistical differences between groups. Regarding other postoperative adverse events, the BCI group demonstrated significantly higher total adverse event rates than the UCI group（80.0% vs 38.2%）, with higher rates of moderate to severe anemia（60.0% vs 20.6%） and lower mean hemoglobin levels[（92.35±12.14） g/L vs（102.39±13.09） g/L]. No significant differences were found in postoperative fever rates（50.0% vs 52.9%） or C-reactive protein levels between groups. Within the BCI group, patients implanted before 12 months indicated notably higher rates of total adverse events（91.3% vs 64.7%）, high fever（26.1% vs 0）, and moderate to severe anemia（78.3% vs 35.3%） compared to those implanted after 12 months. Conclusion:Simultaneous BCI in young children under 2 years of age demonstrates controllable overall risks. Compared to UCI, while it shows no increase in anesthetic or surgical complications, it presents higher perioperative risks and adverse event rates, especially in patients implanted before 12 months of age, warranting special attention from medical staff.}, }
@article {pmid40262425, year = {2025}, author = {Bao, X and Feng, X and Huang, H and Li, M and Chen, D and Wang, Z and Li, J and Huang, Q and Cai, Y and Li, Y}, title = {Day-night hyperarousal in tinnitus patients.}, journal = {Sleep medicine}, volume = {131}, number = {}, pages = {106519}, doi = {10.1016/j.sleep.2025.106519}, pmid = {40262425}, issn = {1878-5506}, abstract = {Tinnitus, which affects 12-30 % of the population, is associated with sleep disturbances and daytime dysfunction, yet the neural mechanisms that link wake-up states remain unclear. This study investigated electroencephalographic (EEG) characteristics of 51 tinnitus patients and 51 controls across wakefulness (eyes-open, eyes-closed, mental arithmetic) and sleep stages (N1, N2, N3, REM) to clarify day-night pathological mechanisms. The key findings showed persistent hyperarousal in tinnitus: wakefulness revealed enhanced gamma power (30-45 Hz) in eyes-closed and task states, while sleep demonstrated elevated gamma/beta power across all stages accompanied by reduced delta/theta power in deep sleep (N2/N3).). An analysis of sleep structure indicates impaired stability in maintaining the N2 stage among tinnitus patients, corroborating a reduction in N3 duration and an increased proportion of the N2 stage. From the wake states to the sleep stages, group × state interactions for the delta/theta power suggest an impaired state regulation capacity in tinnitus patients. Correlation clustering further revealed aberrant integration of wake-related gamma/beta activity into non-rapid eye movement sleep, indicating neuroplastic overgeneralization of wake hyperarousal into sleep. These results extend the so-called loss-of-inhibition theory to sleep, proposing that deficient low-frequency oscillations fail to suppress hyperarousal, impairing sleep-dependent neuroplasticity, and perpetuating daytime symptoms. Furthermore, this study establishes sleep as a critical therapeutic target to interrupt the 24-h dysfunctional cycle of tinnitus.}, }
@article {pmid40262392, year = {2025}, author = {Li, X and Dong, X and Wang, J and Mao, H and Tu, X and Li, W and He, J and Li, Q and Zhang, P}, title = {Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces.}, journal = {Computers in biology and medicine}, volume = {192}, number = {Pt A}, pages = {110231}, doi = {10.1016/j.compbiomed.2025.110231}, pmid = {40262392}, issn = {1879-0534}, abstract = {Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs.}, }
@article {pmid40262075, year = {2025}, author = {Shao, WW and Shao, Q and Xu, HH and Qiao, GJ and Wang, RX and Ma, ZY and Meng, WW and Yang, ZB and Zang, YL and Li, XH}, title = {Repetitive training enhances the pattern recognition capability of cultured neural networks.}, journal = {PLoS computational biology}, volume = {21}, number = {4}, pages = {e1013043}, doi = {10.1371/journal.pcbi.1013043}, pmid = {40262075}, issn = {1553-7358}, abstract = {Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.}, }
@article {pmid40261790, year = {2025}, author = {Luo, J and Liu, Q and Tai, P and Li, G and Li, Y}, title = {A Multi-level Integrated EEG-Channel Selection Method Based on the Lateralization Index.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3563416}, pmid = {40261790}, issn = {1558-0210}, abstract = {The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM); 3.8%, 2.8%, 4.5%(RF); and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery Brain-Computer Interface (MI-BCI) and effectiveness in practical applications.}, }
@article {pmid40260641, year = {2025}, author = {Xu, Y and Li, YL and Yu, G and Ou, Z and Yao, S and Li, Y and Huang, Y and Chen, J and Ding, Q}, title = {Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {4}, pages = {e70400}, pmid = {40260641}, issn = {1755-5949}, support = {2024A04J3082//Guangzhou Science and Technology Program/ ; A2024500//Guangdong Medical Research Foundation/ ; 82102678//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Spectroscopy, Near-Infrared ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Young Adult ; *Frontal Lobe/physiology/diagnostic imaging ; Adult ; Attention/physiology ; *Nerve Net/physiology/diagnostic imaging ; Executive Function/physiology ; }, abstract = {AIMS: Inattention in young people is one of the main reasons for their declining learning ability. Frontoparietal networks (FPNs) are associated with attention and executive function. Brain computer interface (BCI) training has been applied in neurorehabilitation, but there is a lack of research on its application to cognition. This study aimed to investigate the effect of BCI on the attention network in healthy young adults.<br><br>METHODS: Twenty-seven healthy people performed BCI training for 5 consecutive days. An attention network test (ANT) was performed at baseline and immediately after the fifth day of training and included simultaneous functional near-infrared spectroscopy recording.<br><br>RESULTS: BCI performance improved significantly after BCI training (p&#x2009;=&#x2009;0.005). The efficiencies of the alerting and executive control networks were enhanced after BCI training (p&#x2009;=&#x2009;0.032 and 0.003, respectively). The functional connectivity in the bilateral prefrontal cortices and the right posterior parietal cortex increased significantly after BCI training (p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.}, }
@article {pmid40260139, year = {2025}, author = {Zhang, T and Wang, N and Chai, X and He, Q and Cao, T and Yuan, L and Lan, Q and Yang, Y and Zhao, J}, title = {Evaluation of pressure-induced pain in patients with disorders of consciousness based on functional near infrared spectroscopy.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1542691}, pmid = {40260139}, issn = {1664-2295}, abstract = {OBJECTIVE: This study aimed to investigate the brain's hemodynamic responses (HRO) and functional connectivity in patients with disorders of consciousness (DoC) in response to acute pressure pain stimulation using near-infrared spectroscopy (NIRS).<br><br>METHODS: Patients diagnosed with DoC underwent pressure stimulation while brain activity was measured using NIRS. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations were monitored across several regions of interest (ROIs), including the primary somatosensory cortex (PSC), primary motor cortex (PMC), dorsolateral prefrontal cortex (dPFC), somatosensory association cortex (SAC), temporal gyrus (TG), and frontopolar area (FPA). Functional connectivity was assessed during pre-stimulation, stimulation, and post-stimulation phases.<br><br>RESULTS: No significant changes in HbO or HbR concentrations were observed during the stimulation vs. baseline or stimulation vs. post-stimulation comparisons, indicating minimal activation of the targeted brain regions in response to the pressure stimulus. However, functional connectivity between key regions, particularly the PSC, PMC, and dPFC, showed significant enhancement during the stimulation phase (r > 0.9, p < 0.001), suggesting greater coordination among sensory, motor, and cognitive regions. These changes in connectivity were not accompanied by significant activation in pain-related brain areas.<br><br>CONCLUSION: Although pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.}, }
@article {pmid40257892, year = {2025}, author = {Hashemi, M and Depannemaecker, D and Saggio, M and Triebkorn, P and Rabuffo, G and Fousek, J and Ziaeemehr, A and Sip, V and Athanasiadis, A and Breyton, M and Woodman, M and Wang, H and Petkoski, S and Sorrentino, P and Jirsa, V}, title = {Principles and Operation of Virtual Brain Twins.}, journal = {IEEE reviews in biomedical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/RBME.2025.3562951}, pmid = {40257892}, issn = {1941-1189}, abstract = {Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.}, }
@article {pmid40257874, year = {2025}, author = {Li, S and Liu, G and Feng, F and Chang, Z and Li, W and Duan, F}, title = {An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3562922}, pmid = {40257874}, issn = {1558-0210}, abstract = {Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.}, }
@article {pmid40257872, year = {2025}, author = {Huang, S and Liu, Y and Wang, Z and Wu, W and Guo, J and Xu, W and Ming, D}, title = {Enhanced Brain Functional Interaction Following BCI-guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3562700}, pmid = {40257872}, issn = {1558-0210}, abstract = {Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, the neuroplasticity effects of BCI-actuated SRF (BCI-SRF) training based on the "six finger" motor imagery paradigm are still unclear. This study recruited 20 healthy right-handed participants and randomly assigned them to either a BCI-SRF training group or a sham SRF training group. During the testing phase before and after 4 weeks of training, all participants were tested for SRF-finger opposition sequence behavior, resting state fMRI (rs-fMRI), and task-based fMRI (tb-fMRI). The results showed that compared with the Sham group, the BCI-SRF group improved the accuracy rate of the SRF-finger opposition sequence by 132%. The activation analysis of tb-fMRI before and after training revealed a significant increase in left middle frontal gyrus only in the BCI-SRF group. In addition, the BCI-SRF group showed an increase in FC between the right primary motor cortex and left cerebellum inferior lobe, as well as between the left middle frontal gyrus and the right precuneus lobe after training, while there was no significant change in the Sham group. In addition, only the BCI-SRF group showed a significant increase in clustering coefficients after training. Moreover, the increase in the clustering coefficients of the two groups is positively correlated with the improvement of the accuracy of the SRF-finger opposition sequences. These results indicate that the integration of BCI and SRF significantly regulates the functional interaction between motor learning and cognitive imagery brain regions, enhances the integration and processing ability of brain networks for local information, and improves human-machine interaction behavioral performance.}, }
@article {pmid40254808, year = {2025}, author = {Wang, H and Wang, X}, title = {Exploring the Role of Psychedelics in Modulating Ego and Treating Neuropsychiatric Disorders.}, journal = {ACS chemical neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1021/acschemneuro.5c00247}, pmid = {40254808}, issn = {1948-7193}, abstract = {This viewpoint explores the therapeutic potential of psychedelics in treating neuropsychiatric disorders, particularly through the modulation of brain entropy and the experience of ego dissolution. Psychedelics disrupt rigid neural patterns, facilitating enhanced connectivity and fostering profound emotional breakthroughs that may alleviate symptoms of disorders like depression, anxiety, PTSD, and addiction. Despite their promising potential, the clinical application of psychedelics presents significant challenges, including the need for careful patient screening, managing adverse experiences, and addressing ethical considerations, all of which are essential for their safe integration into therapy.}, }
@article {pmid40254540, year = {2025}, author = {Fu, Q and Tong, L and Zhang, H and Xu, H}, title = {Multimodal Imaging Diagnosis of Apical Ventricular Aneurysm With Thrombosis Resulting From Blunt Myocardial Injury: A Case Report.}, journal = {Journal of clinical ultrasound : JCU}, volume = {}, number = {}, pages = {}, doi = {10.1002/jcu.24026}, pmid = {40254540}, issn = {1097-0096}, support = {20210101260JC//the Science and Technology Development Program of the Jilin Province/ ; }, abstract = {This article presents the case of a male patient who sustained blunt myocardial injury following a traffic accident. A series of diagnostic imaging procedures were conducted on the patient, including electrocardiography, echocardiography, computed tomography angiography, and cardiac magnetic resonance imaging, which demonstrated edema in a portion of the myocardium and the formation of a ventricular aneurysm with thrombus in the left ventricular apex. After 6 months and 1 year, echocardiography demonstrated no detection of thrombus, but the apical left ventricular aneurysm was not significantly different from the anterior film, leading to a final clinical diagnosis of blunt cardiac injury (BCI).}, }
@article {pmid40253420, year = {2025}, author = {Wang, B and Zhang, X and Zhang, L and Kong, XZ}, title = {A naturalistic fMRI dataset in response to public speaking.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {659}, pmid = {40253420}, issn = {2052-4463}, support = {32171031//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Magnetic Resonance Imaging ; Female ; Male ; *Speech ; Young Adult ; Adult ; *Brain/physiology ; *Communication ; }, abstract = {Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.}, }
@article {pmid40253415, year = {2025}, author = {He, T and Wei, M and Wang, R and Wang, R and Du, S and Cai, S and Tao, W and Li, H}, title = {VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {657}, pmid = {40253415}, issn = {2052-4463}, support = {62271432//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; *Speech ; *Language ; Brain-Computer Interfaces ; }, abstract = {Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.}, }
@article {pmid40253381, year = {2025}, author = {Xue, S and Jin, B and Jiang, J and Guo, L and Zhou, J and Wang, C and Liu, J}, title = {A multi-subject and multi-session EEG dataset for modelling human visual object recognition.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {663}, pmid = {40253381}, issn = {2052-4463}, support = {U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; Brain-Computer Interfaces ; Machine Learning ; *Visual Perception ; *Pattern Recognition, Visual ; Algorithms ; }, abstract = {We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.}, }
@article {pmid40252874, year = {2025}, author = {Ye, Z and Lv, C and Zhou, H and Bao, Y and Hong, T and He, Q and Hu, Y}, title = {Neural substrates of attack event prediction in video games: the role of ventral posterior cingulate cortex and theory of mind network.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121228}, doi = {10.1016/j.neuroimage.2025.121228}, pmid = {40252874}, issn = {1095-9572}, abstract = {Action anticipation, the ability to observe actions and predict the intent of others, plays a crucial role in social interaction and fields such as electronic sports. However, the neural mechanisms underlying the inference of purpose from action observation remain unclear. In this study, we conducted an fMRI experiment using video game combat scenarios to investigate the neural correlates of action anticipation and its relationship with task performance. The results showed that the higher level of ability to infer the purpose from action observation during experiment associates with higher level of proficiency in real world electric gaming competition. The action anticipation task activates visual streams, fronto-parietal network, and the ventral posterior cingulate cortex (vPCC), a key hub in the theory of mind network. The strength of vPCC activation during action anticipation, but not movement direction judgment, was positively correlated with gaming proficiency. Finite impulse response analysis revealed distinct dynamic response profiles in the vPCC compared to other theory of mind regions. These findings suggest that theory of mind ability may be an important factor influencing individual competitive performance, with the vPCC serving as a core neural substrate for inferring purpose from action observation.}, }
@article {pmid40250541, year = {2025}, author = {Liu, Y and Wang, M and Rao, H}, title = {Common Neural Activations of Creativity and Exploration: A Meta-analysis of Task-based fMRI Studies.}, journal = {Neuroscience and biobehavioral reviews}, volume = {}, number = {}, pages = {106158}, doi = {10.1016/j.neubiorev.2025.106158}, pmid = {40250541}, issn = {1873-7528}, abstract = {Creativity is a common, complex, and multifaceted cognitive activity with significant implications for technological progress, social development, and human survival. Understanding the neurocognitive mechanisms underlying creative thought is essential for fostering individual creativity. While previous studies have demonstrated that exploratory behavior positively influences creative performance, few studies investigated the relationship between creativity and exploration at the neural level. To address this gap, we conducted a quantitative meta-analysis comprising 80 creativity experiments (1,850 subjects) and 23 exploration experiments (646 subjects) to examine potential shared neural activations between creativity and exploration. Furthermore, we analyzed the neural similarities and differences among three forms of creative thinking-divergent thinking (DT), convergent thinking (CT), and artistic creativity-and their relationship with exploration. The conjunction analysis of creativity and exploration revealed significant activations in the bilateral IFJ and left preSMA. Further conjunction analyses revealed that both CT and artistic creativity exhibited common neural activations with exploration, with CT co-activating the left IFJ and artistic creativity co-activating both the right IFJ and left preSMA, while DT did not. Additionally, the conjunction analyses across the three forms of creativity did not identify shared neural activations. Further functional decoding analyses of the overlapping brain regions associated with CT and exploration, as well as artistic creativity and exploration, revealed correlations with inhibitory control mechanisms. These results enhance our understanding of the role of exploration in the creative thinking process and provide valuable insights for developing strategies to foster innovative thinking.}, }
@article {pmid40249697, year = {2025}, author = {Cao, B and Tsai, CL and Zhou, N and Do, T and Lin, CT}, title = {A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3562217}, pmid = {40249697}, issn = {1558-0210}, abstract = {Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm's superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency.}, }
@article {pmid40247883, year = {2025}, author = {Uszko, JM and Schroeder, JC and Eichhorn, SJ and Patil, AJ and Hall, SR}, title = {Morphological control of cuprate superconductors using sea sponges as templates.}, journal = {RSC advances}, volume = {15}, number = {14}, pages = {11189-11193}, pmid = {40247883}, issn = {2046-2069}, abstract = {Functional porous superconducting sponges, consisting of YBa2Cu3O6+δ (YBCO) and Bi2Sr2CaCu2O8+δ (BSCCO), were created by biotemplating with natural sea sponges. Naturally occurring calcium in the spongin fibers was utilized to dope YBCO and to form BSCCO without adding any external calcium source. The sample morphology was confirmed with scanning electron microscopy, and the sample composition was confirmed with energy-dispersive X-ray spectroscopy, powder electron diffraction and high-resolution transmission electron microscopy. The YBCO sponge exhibited a critical temperature (T c) of approximately 70 K, and the BSCCO sponge showed a T c of 77 K. This proof-of-concept study demonstrates the feasibility of using sea sponges as a greener, more sustainable template for superconductor synthesis.}, }
@article {pmid40247859, year = {2025}, author = {Kuo, YT and Wang, HL and Chen, BW and Wang, CF and Lo, YC and Lin, SH and Chen, PC and Chen, YY}, title = {Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.}, journal = {APL bioengineering}, volume = {9}, number = {2}, pages = {026106}, pmid = {40247859}, issn = {2473-2877}, abstract = {Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing signals are essential to restore neural signal integrity, thereby improving decoding accuracy and system robustness over long-term recordings with fluctuating signal quality. This study introduces a confidence-weighted Bayesian linear regression (CW-BLR) approach to impute neural signals affected by degradation, enhancing the robustness and consistency of decoding. The performance of CW-BLR was compared to traditional methods-mean imputation (Mean-imp) and Gaussian-mixture-model-based expectation-maximization (GMM-EM)-using a kernel-sliced inverse regression (kSIR) decoder to evaluate decoding outcomes. Four Wistar rats were trained to perform a forelimb-reaching task while neural activity (MUA and LFPs) was recorded over 27 days. CW-BLR imputed signals degraded during days 8-27. Decoding performance was evaluated using kSIR and compared with Mean-imp and GMM-EM. CW-BLR demonstrated superior performance by effectively preserving both temporal and spatial dependencies within the neural signals. CW-BLR-imputed data significantly improved decoding accuracy over traditional imputation methods, with the kSIR decoder showing consistently higher performance, particularly in maintaining signal quality from the degraded period. CW-BLR offers a robust and effective imputation framework for iBMI applications, addressing signal degradation challenges and maintaining accurate decoding over prolonged recordings. By utilizing confidence-based quality metrics, CW-BLR surpasses traditional methods, providing stable neural decoding across fluctuating signal quality scenarios.}, }
@article {pmid40246195, year = {2025}, author = {Isis Yonza, AK and Tao, L and Zhang, X and Postnov, D and Kucharz, K and Lind, B and Asiminas, A and Han, A and Sonego, V and Kim, K and Cai, C}, title = {Spatially and temporally mismatched blood flow and neuronal activity by high-intensity intracortical microstimulation.}, journal = {Brain stimulation}, volume = {18}, number = {3}, pages = {885-896}, doi = {10.1016/j.brs.2025.04.015}, pmid = {40246195}, issn = {1876-4754}, abstract = {INTRODUCTION: Intracortial microstimulation (ICMS) is widely used in neuroprosthetic brain-machine interfacing, particularly in restoring lost sensory and motor functions. Spiking neuronal activity requires increased cerebral blood flow to meet local metabolic demands, a process conventionally denoted as neurovascular coupling (NVC). However, it is unknown precisely how and to what extent ICMS elicits NVC and how the neuronal and blood flow responses to ICMS correlate. Suboptimal NVC by ICMS may compromise oxygen and energy delivery to the activated neurons thus impair neuroprosthetic functionality.<br><br>MATERIAL AND METHOD: We used wide-field imaging (WFI), laser speckle imaging (LSI) and two-photon microscopy (TPM) to study living, transgenic mice expressing calcium (Ca[2+]) fluorescent indicators in either neurons or vascular mural cells (VMC), as well as to measure vascular inner lumen diameters.<br><br>RESULT: By testing a range of stimulation amplitudes and examining cortical tissue responses at different distances from the stimulating electrode tip, we found that high stimulation intensities (&#x2265;50&#xa0;&#x3bc;A) elicited a spatial and temporal neurovascular decoupling in regions most adjacent to electrode tip (<200&#xa0;&#x3bc;m), with significantly delayed onset times of blood flow responses to ICMS and compromised maximum blood flow increases. We attribute these effects respectively to delayed Ca[2+] signalling and decreased Ca[2+] sensitivity in VMCs.<br><br>CONCLUSION: Our study offers new insights into ICMS-associated neuronal and vascular physiology with potentially critical implications towards the optimal design of ICMS in neuroprosthetic therapies: low intensities preserve NVC; high intensities disrupt NVC responses and potentially precipitate blood supply deficits.}, }
@article {pmid40246009, year = {2025}, author = {Ren, J and Wang, Y and Wang, Y and Zhang, Y and Xing, M and Deng, S and Tong, S and Wang, L and Zheng, C and Yang, J and Ni, G and Ming, D}, title = {Dynamic changes of hippocampal dendritic spines in Alzheimer's disease mice among the different stages.}, journal = {Experimental neurology}, volume = {}, number = {}, pages = {115266}, doi = {10.1016/j.expneurol.2025.115266}, pmid = {40246009}, issn = {1090-2430}, abstract = {Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) peptides and a progressive decline in cognitive function. Hippocampus as a crucial brain area for learning and memory, is also adversely affected by AD's pathology. The accumulation of Aβ is often associated with the loss of dendritic spines of the hippocampus. However, the dynamic alterations in dendritic spines throughout AD progression are not fully understood. To investigate it, we conducted in-vivo imaging in two mouse models representing the early and late stages of AD pathology: young mice injected with Aβ1-42 oligomers and APP/PS1 transgenic mice. In the early-stage AD model, imaging was conducted at third- and fifth- weeks post-injection. In the late-stage AD model, a four-month imaging began at 14 months old. The imaging results showed spine elimination in both models. Notably, acute Aβ exposure was linked to heightened spine loss on secondary dendrites, while in the late stage the primary effect was on tertiary dendrites. Concurrently, with the metabolism of Aβ, cognition recovered to some extent by five weeks post Aβ1-42 exposure. These findings suggested that dendritic spine plasticity was impaired during the development of AD, as evidenced by increasing spine loss at different levels. However, the cognitive recovery observed in early-stage AD model mice may indicate a compensatory structural reorganization, highlighting the potential of early intervention to mitigate disease progression. Our results provide novel insights into the neurotoxic effects of Aβ1-42 and may contribute to the development of therapeutic strategies for AD.}, }
@article {pmid40245876, year = {2025}, author = {Zhao, D and Dong, G and Pei, W and Gao, X and Wang, Y}, title = {Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adce32}, pmid = {40245876}, issn = {1741-2552}, abstract = {Objective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.Main results.The online information transfer rates for the three stimulus paradigms were 53.77 bits min[-1], 51.41 ± 3.55 bits min[-1], and 52.07 ± 3.09 bits min[-1], respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.Significance.These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.}, }
@article {pmid40245253, year = {2025}, author = {Singh, K and Lin, CC and Huang, WH and Lei, WL and Chiueh, H and Wang, YH and Chang, PH and Lin, RZ and Huang, WC}, title = {Ultrabioconformal, Self-Healable, and Antioxidized Polydopamine-Inspired Nanowire Hydrogels Enable Resolving Power in Forehead and Ear Electroencephalograms for Brain Function Assessment.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.4c23013}, pmid = {40245253}, issn = {1944-8252}, abstract = {Continuous brain function monitoring by high-performance electroencephalogram (EEG) suggests a high impact for advancing precision personalized medication of neurodevelopmental or neurodegenerative disorders. Forehead and ear EEGs are nonhairy recording strategies that allow the recording of brain activity using only a few electrodes. However, they require well-designed electrodes that are easy and comfortable to carry while simultaneously performing durable high-quality EEG acquisition. Herein, we propose a new ultrabiocompliant EEG sensor that enables seamless contact to surfaces of both earhole and forehead, while permitting prolonged and high-quality EEG signal identification. Bioinspired polydopamine/platinum-silver nanowires, called PDA-Ag@Pt NWs, are synthesized with noticeable performances in electrical conductivity, antioxidation ability, cytocompatibility, and adhesion. PDA-Ag@Pt NWs can promote synchronic gelation and interlinks within polydopamine-polyacrylamide (PDA-PAM) hydrogels, in turn leading to the one-step formation of a nanowire/hydrogel matrix, called PDA-PAM/NW, as an electrode patch in the presence of adhesive and self-healing capabilities. Combined with a self-designed signal processor, a portable electrophysiological signal recording system was realized. The PDA-PAM/NW electrode patch outperformed commercial electrodes in terms of reliability and resolution for electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) recording. In addition, through brain cognitive assessment by frontal- and ear-EEG recording, the ultrathin design and comfortable adhesion of PDA-PAM/NW electrodes make participants comfortable over time, subsequently providing the identification of the brain activity in high resolution. This work underscores the potential of the ultrabiocompliant and durable patch in the development of comfy, long-lasting, and high-performance wearable brain-machine interfaces for the revolution in neuroscience.}, }
@article {pmid40245060, year = {2025}, author = {Akhter, J and Nazeer, H and Naseer, N and Naeem, R and Kallu, KD and Lee, J and Ko, SY}, title = {Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.}, journal = {PloS one}, volume = {20}, number = {4}, pages = {e0314447}, pmid = {40245060}, issn = {1932-6203}, mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; *Brain-Computer Interfaces ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Hand Strength/physiology ; }, abstract = {The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional long-short-term memory (Bi-LSTM) are employed to extract features. The stack method classifies these features using a stack model and the fft method enhances features by applying fast Fourier transformation which is followed by classification using a stack model. The proposed methods are applied to fNIRS signals from twenty participants engaged in a two-class hand-gripping motor activity. The classification performance of the proposed methods is compared with conventional CNN, LSTM, and Bi-LSTM algorithms and one another. The proposed fft and stack methods yield 90.11% and 87.00% classification accuracies respectively, which are significantly higher than those achieved by CNN (85.16%), LSTM (79.46%), and Bi-LSTM (81.88%) conventional algorithms. The results show that the proposed stack and fft methods can be effectively used for the classification of the two and three-class problems in fNIRS-BCI applications.}, }
@article {pmid40244939, year = {2025}, author = {Noel, JP and Bockbrader, M and Bertoni, T and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A}, title = {Neuronal responses in the human primary motor cortex coincide with the subjective onset of movement intention in brain-machine interface-mediated actions.}, journal = {PLoS biology}, volume = {23}, number = {4}, pages = {e3003118}, pmid = {40244939}, issn = {1545-7885}, support = {K99 NS128075/NS/NINDS NIH HHS/United States ; R00 NS128075/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Intention ; Movement/physiology ; Male ; Adult ; Female ; *Neurons/physiology ; Electric Stimulation ; Quadriplegia/physiopathology ; }, abstract = {Self-initiated behavior is accompanied by the experience of intending our actions. Here, we leverage the unique opportunity to examine the full intentional chain-from intention to action to environmental effects-in a tetraplegic person outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (intention, action, effect) while probing subjective experience and performing extra-cellular recordings in human M1. Behaviorally, we reveal a novel form of intentional binding: motor intentions are reflected in a perceived temporal attraction between the onset of intentions and that of actions. Neurally, we demonstrate that evoked spiking activity in M1 largely coincides in time with the onset of the experience of intention and that M1 spike counts and the onset of subjective intention may co-vary on a trial-by-trial basis. Further, population-level dynamics, as indexed by a decoder instantiating movement, reflect intention-action temporal binding. The results fill a significant knowledge gap by relating human spiking activity in M1 with the onset of subjective intention and complement prior human intracranial work examining pre-motor and parietal areas.}, }
@article {pmid40242802, year = {2025}, author = {Iosif, R and Skrbinšek, T and Erős, N and Konec, M and Boljte, B and Jan, M and Promberger-Fürpass, B}, title = {Wolf Population Size and Composition in One of Europe's Strongholds, the Romanian Carpathians.}, journal = {Ecology and evolution}, volume = {15}, number = {4}, pages = {e71200}, pmid = {40242802}, issn = {2045-7758}, abstract = {Strategies of coexistence with large carnivores should integrate scientific evidence, population monitoring providing an opportunity for advancing outdated management paradigms. We estimated wolf population density and social dynamics across a 1400 km[2] area in a data-poor region of the Romanian Carpathians. Across three consecutive years (2017-2018 until 2019-2020), we collected and genotyped 505 noninvasive DNA wolf samples (scat, hair and urine) to identify individuals, reconstruct pedigrees, and check for the presence of hybridization with domestic dogs. We identified 27 males, 20 females, and one F1 wolf-dog hybrid male. We delineated six wolf packs, with pack size varying between two and seven individuals, and documented yearly changes in pack composition. Using a spatial capture-recapture approach, we estimated population density at 2.35 wolves/100 km[2] (95% BCI = 1.68-3.03) and population abundance at 70 individuals (95% BCI = 49-89). Noninvasive DNA data collection coupled with spatial capture-recapture has the potential to inform on wolf population size and dynamics at broader spatial scales, across different sampling areas representative of the diverse Carpathian landscapes, and across different levels of human impact, supporting wildlife decision making in one of Europe's main strongholds for large carnivores.}, }
@article {pmid40242584, year = {2025}, author = {Hu, S and Lin, C and Wang, H and Wang, X}, title = {Psychedelics and Eating Disorders: Exploring the Therapeutic Potential for Anorexia Nervosa and Beyond.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {4}, pages = {910-916}, pmid = {40242584}, issn = {2575-9108}, abstract = {Anorexia nervosa (AN) is a severe psychiatric disorder characterized by extreme food restriction, an intense fear of weight gain, and a distorted body image, leading to significant morbidity and mortality. Conventional treatments such as cognitive-behavioral therapy (CBT) and pharmacotherapy often prove inadequate, especially in severe cases, highlighting the need for novel therapeutic approaches. Recent research into psychedelics, such as psilocybin and 3,4-methylenedioxymethamphetamine (MDMA), offers promising avenues for treating anorexia nervosa by targeting its neurobiological and psychological underpinnings. These psychedelics disrupt maladaptive neural circuits, enhance cognitive flexibility, and facilitate emotional processing, offering potential relief for patients unresponsive to traditional therapies. Early studies have shown positive outcomes with psychedelics, including reductions in anorexia nervosa symptoms and improvements in psychological well-being. However, further research is needed to establish their long-term safety, efficacy, and integration into clinical practice. Addressing the legal, ethical, and safety challenges will be crucial in determining whether psychedelics can transform the treatment landscape for anorexia nervosa and other eating disorders.}, }
@article {pmid40242456, year = {2025}, author = {Yan, W and Luo, Q and Du, C}, title = {Channel component correlation analysis for multi-channel EEG feature component extraction.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1522964}, pmid = {40242456}, issn = {1662-4548}, abstract = {INTRODUCTION: Electroencephalogram (EEG) analysis has shown significant research value for brain disease diagnosis, neuromodulation and brain-computer interface (BCI) application. The analysis and processing of EEG signals is complex since EEG are nonstationary, nonlinear, and often contaminated by intense background noise. Principal component analysis (PCA) and independent component analysis (ICA), as the commonly used methods for multi-dimensional signal feature component extraction, still have some limitations in terms of performance and calculation.<br><br>METHODS: In this study, channel component correlation analysis (CCCA) method was proposed to extract feature components of multi-channel EEG. Firstly, empirical wavelet transform (EWT) decomposed each channel signal into different frequency bands, and reconstructed them into a multi-dimensional signal. Then the objective optimization function was constructed by maximizing the covariance between multi-dimensional signals. Finally the feature components of multi-channel EEG were extracted using the calculated weight coefficient.<br><br>RESULTS: The results showed that the CCCA method could find the most relevant frequency band between multi-channel EEG. Compared with PCA and ICA methods, CCCA could extract the common components of multi-channel EEG more effectively, which is of great significance for the accurate analysis of EEG.<br><br>DISCUSSION: The CCCA method proposed in this study has shown excellent performance in the feature component extraction of multi-channel EEG and could be considered for practical engineering applications.}, }
@article {pmid40241786, year = {2025}, author = {Hernández-Gloria, JJ and Jaramillo-Gonzalez, A and Savić, AM and Mrachacz-Kersting, N}, title = {Toward brain-computer interface speller with movement-related cortical potentials as control signals.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1539081}, pmid = {40241786}, issn = {1662-5161}, abstract = {Brain Computer Interface spellers offer a promising alternative for individuals with Amyotrophic Lateral Sclerosis (ALS) by facilitating communication without relying on muscle activity. This study assessed the feasibility of using movement related cortical potentials (MRCPs) as a control signal for a Brain-Computer Interface speller in an offline setting. Unlike motor imagery-based BCIs, this study focused on executed movements. Fifteen healthy subjects performed three spelling tasks that involved choosing specific letters displayed on a computer screen by performing a ballistic dorsiflexion of the dominant foot. Electroencephalographic signals were recorded from 10 sites centered around Cz. Three conditions were tested to evaluate MRCP performance under varying task demands: a control condition using repeated selections of the letter "O" to isolate movement-related brain activity; a phrase spelling condition with structured text ("HELLO IM FINE") to simulate a meaningful spelling task with moderate cognitive load; and a random condition using a randomized sequence of letters to introduce higher task complexity by removing linguistic or semantic context. The success rate, defined as the presence of an MRCP, was manually determined. It was approximately 69% for both the control and phrase conditions, with a slight decrease in the random condition, likely due to increased task complexity. Significant differences in MRCP features were observed between conditions with Laplacian filtering, whereas no significant differences were found in single-site Cz recordings. These results contribute to the development of MRCP-based BCI spellers by demonstrating their feasibility in a spelling task. However, further research is required to implement and validate real-time applications.}, }
@article {pmid40241515, year = {2025}, author = {Jin, F and Li, M and Yang, L and Yang, L and Shang, Z}, title = {Exploring the value learning in pigeons: The role of dual pathways in the basal ganglia and synaptic plasticity.}, journal = {The Journal of experimental biology}, volume = {}, number = {}, pages = {}, doi = {10.1242/jeb.249507}, pmid = {40241515}, issn = {1477-9145}, support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; }, abstract = {Understanding value learning in animals is a key focus in cognitive neuroscience. Current models used in research are often simple, and while more complex models have been proposed, it remains unclear which assumptions align with actual value learning strategies of animals. This study investigated the computational mechanisms behind value learning in pigeons using a free-choice task. Three models were constructed based on different assumptions about the role of the basal ganglia's dual pathways and synaptic plasticity in value computation, followed by model comparison and neural correlation analysis. Among the three models tested, the dual-pathway reinforcement learning model with Hebbian rules most closely matched the pigeons' behavior. Furthermore, the striatal gamma band connectivity showed the highest correlation with the values estimated by this model. Additionally, enhanced beta band connectivity in the nidopallium caudolaterale supported value learning. This study provides valuable insights into reinforcement learning mechanisms in non-human animals.}, }
@article {pmid40240152, year = {2025}, author = {Ma, YN and Karako, K and Song, P and Hu, X and Xia, Y}, title = {Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.}, journal = {Bioscience trends}, volume = {}, number = {}, pages = {}, doi = {10.5582/bst.2025.01109}, pmid = {40240152}, issn = {1881-7823}, abstract = {Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack individualization and yield suboptimal outcomes. In recent years, brain-computer interface (BCI) technology has emerged as a promising neurorehabilitation tool by decoding neural signals and providing real-time feedback to enhance neuroplasticity. This review systematically explores the use of BCI systems in post-stroke rehabilitation, focusing on three core domains: motor function, cognitive capacity, and emotional regulation. This review outlines the neurophysiological principles underpinning BCI-based motor rehabilitation, including neurofeedback training, Hebbian plasticity, and multimodal feedback strategies. It then examines recent advances in upper limb and gait recovery using BCI integrated with functional electrical stimulation (FES), robotics, and virtual reality (VR). Moreover, it highlights BCI's potential in cognitive and language rehabilitation through EEG-based neurofeedback and the integration of artificial intelligence (AI) and immersive VR environments. In addition, it discusses the role of BCI in monitoring and regulating post-stroke emotional disorders via closed-loop systems. While promising, BCI technologies face challenges related to signal accuracy, device portability, and clinical validation. Future research should prioritize multimodal integration, AI-driven personalization, and large-scale randomized trials to establish long-term efficacy. This review underscores BCI's transformative potential in delivering intelligent, personalized, and cross-domain rehabilitation solutions for stroke survivors.}, }
@article {pmid40239679, year = {2025}, author = {Faes, A and Calvo Merino, E and Branco, MP and Van Hoylandt, A and Keirse, E and Theys, T and Ramsey, NF and Van Hulle, MM}, title = {Decoding sign language finger flexions from high-density electrocorticography using graph-optimized block term tensor regression.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adcd9e}, pmid = {40239679}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; *Sign Language ; *Fingers/physiology ; Male ; Gestures ; Female ; Adult ; Regression Analysis ; Middle Aged ; Movement/physiology ; }, abstract = {Objective.A novel method is introduced to regress over the sign language finger movements from human electrocorticography (ECoG) recordings.Approach.The proposed graph-optimized block-term tensor regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a causal graph process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively.Main results.Two ECoG datasets were used, one recorded in five patients expressing four hand gestures of the American sign language alphabet, and another in two patients expressing all gestures of the Flemish sign language alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single-finger scenarios. For the American sign language alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish sign language alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR.Significance.Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the sign language alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a brain-computer interface solution.}, }
@article {pmid40238867, year = {2025}, author = {Li, X and Deng, Z and Zhang, W and Zhou, W and Liu, X and Quan, H and Li, J and Li, P and Li, Y and Hu, C and Li, F and Niu, L and Tian, Z and Meng, L and Zheng, H}, title = {Oscillating microbubble array-based metamaterials (OMAMs) for rapid isolation of high-purity exosomes.}, journal = {Science advances}, volume = {11}, number = {16}, pages = {eadu8915}, pmid = {40238867}, issn = {2375-2548}, mesh = {*Microbubbles ; *Exosomes/metabolism/chemistry ; Humans ; Acoustics ; }, abstract = {Exosomes secreted by cells hold substantial potential for disease diagnosis and treatment. However, the rapid isolation of high-purity exosomes and their subpopulations from biofluids (e.g., undiluted whole blood) remains challenging. This study presents oscillating microbubble array-based metamaterials (OMAMs) for enabling the rapid isolation of high-purity exosomes and their subpopulations from biofluids without labeling or preprocessing. Particularly, leveraging acoustically excited microbubble oscillation, OMAMs can generate numerous acoustofluidic traps for filtering in-fluid micro/nanoparticles, thus allowing for removing bioparticles larger than exosomes to obtain high-purity (93%) exosomes from undiluted whole blood in ~3 minutes. Moreover, exosome subpopulations in different size ranges can be isolated by tuning the microbubble oscillation amplitude. Additionally, as each oscillating microbubble functions as an ultradeep subwavelength (~λ/186) acoustic amplifier and a nonlinear source, OMAMs can generate high-resolution complex acoustic energy patterns and tune the patterns by activating different-sized microbubbles at their distinct resonance frequencies.}, }
@article {pmid40236895, year = {2025}, author = {Wen, D and Xing, Y and Yao, Y and Liang, G and Xing, Y and Jung, TP and Yu, H and Xie, X and Wan, X and Liu, T and Duan, D and Li, D and Zhou, Y}, title = {Transforming long-term adjunctive therapy for cognitive impairment: the role of multimodal self-adaptive digital medicine.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1571817}, pmid = {40236895}, issn = {1664-2295}, }
@article {pmid40236742, year = {2025}, author = {Wei, B and Cheng, S and Feng, Y}, title = {Neural personal information and its legal protection: evidence from China.}, journal = {Journal of law and the biosciences}, volume = {12}, number = {1}, pages = {lsaf006}, doi = {10.1093/jlb/lsaf006}, pmid = {40236742}, issn = {2053-9711}, abstract = {The rapid advancements in neuroscience highlight the pressing need to safeguard neural personal information (NPI). China has achieved significant progress in brain-computer interface technology and its clinical applications. Considering the intrinsic vulnerability of NPI and the paucity of legal scrutiny concerning NPI breaches, a thorough assessment of the adequacy of China's personal information protection legislation is essential. This analysis contends that NPI should be classified as sensitive personal information. The absence of bespoke provisions for NPI in current legislation underscores persistent challenges in its protection. To address these gaps, this work proposes the establishment of a concentric-circle hard-soft law continuum to support a hybrid governance model for NPI, rooted in fundamental human rights principles.}, }
@article {pmid40236412, year = {2025}, author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB}, title = {An intuitive, bimanual, high-throughput QWERTY touch typing neuroprosthesis for people with tetraplegia.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.04.01.25324990}, pmid = {40236412}, abstract = {Recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia - one with ALS and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar, and easy-to-learn paradigm for individuals with impaired communication due to paralysis.}, }
@article {pmid40236074, year = {2025}, author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB}, title = {Accelerated learning of a noninvasive human brain-computer interface via manifold geometry.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.03.29.646109}, pmid = {40236074}, issn = {2692-8205}, abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping did not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.}, }
@article {pmid40235786, year = {2025}, author = {Wang, LP and Yang, C and Fu, JY and Zhang, XY and Shen, XM and Shi, NL and Wu, HL and Gao, XR}, title = {A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.}, journal = {Quantitative imaging in medicine and surgery}, volume = {15}, number = {4}, pages = {3469-3479}, pmid = {40235786}, issn = {2223-4292}, abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors.<br><br>METHODS: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated.<br><br>RESULTS: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients.<br><br>CONCLUSIONS: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.}, }
@article {pmid40234729, year = {2025}, author = {Webster, P}, title = {Can AI-powered brain-computer interfaces boost human intelligence?.}, journal = {Nature medicine}, volume = {31}, number = {4}, pages = {1045-1047}, doi = {10.1038/s41591-025-03641-7}, pmid = {40234729}, issn = {1546-170X}, }
@article {pmid40234486, year = {2025}, author = {Zhao, W and Zhang, B and Zhou, H and Wei, D and Huang, C and Lan, Q}, title = {Multi-scale convolutional transformer network for motor imagery brain-computer interface.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {12935}, pmid = {40234486}, issn = {2045-2322}, support = {3502Z202374054//Natural Science Foundation of Xiamen, China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; JAT191153 and JAT201045//Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province of China/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model's generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer's robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer .}, }
@article {pmid40234393, year = {2025}, author = {Bertoni, T and Noel, JP and Bockbrader, M and Foglia, C and Colachis, S and Orset, B and Evans, N and Herbelin, B and Rezai, A and Panzeri, S and Becchio, C and Blanke, O and Serino, A}, title = {Pre-movement sensorimotor oscillations shape the sense of agency by gating cortical connectivity.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3594}, pmid = {40234393}, issn = {2041-1723}, support = {163951//Schweizerischer Nationalfonds zur F&#xf6;rderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/ ; }, mesh = {Humans ; *Motor Cortex/physiology ; Male ; Adult ; Female ; Electroencephalography ; Young Adult ; Movement/physiology ; Brain-Computer Interfaces ; Alpha Rhythm/physiology ; Hand/physiology ; Sense of Agency ; }, abstract = {Our sense of agency, the subjective experience of controlling our actions, is a crucial component of self-awareness and motor control. It is thought to originate from the comparison between intentions and actions across broad cortical networks. However, the underlying neural mechanisms are still not fully understood. We hypothesized that oscillations in the theta-alpha range, thought to orchestrate long-range neural connectivity, may mediate sensorimotor comparisons. To test this, we manipulated the relation between intentions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor cortex (M1) activity to restore hand functionality. We found that the pre-movement phase of low-alpha oscillations in M1 predicted the participant's agency judgements. Further, using EEG-BMI in healthy participants, we found that pre-movement alpha oscillations in M1 and supplementary motor area (SMA) correlated with agency ratings, and with changes in their functional connectivity with parietal, temporal and prefrontal areas. These findings argue for phase-driven gating as a key mechanism for sensorimotor integration and sense of agency.}, }
@article {pmid40232894, year = {2025}, author = {Wang, D and Wei, Q}, title = {SMANet: A Model Combining SincNet, Multi-branch Spatial-Temporal CNN and Attention Mechanism for Motor Imagery BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3560993}, pmid = {40232894}, issn = {1558-0210}, abstract = {Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.}, }
@article {pmid40231563, year = {2025}, author = {Park, K and Hong, J and Shin, H and Choi, J and Xu, D and Lee, J and Ryu, J and Kim, S and Jeong, H and Choe, J and Yang, S and Yang, S and Ahn, JH}, title = {2D Material-Based Injectable Sensor for Minimally-Invasive Cerebral Blood Flow Monitoring.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {}, number = {}, pages = {e2501744}, doi = {10.1002/smll.202501744}, pmid = {40231563}, issn = {1613-6829}, support = {20012355//Ministry of Trade, Industry and Energy/ ; }, abstract = {Monitoring cerebral blood flow is an important method for diagnosing and treating brain diseases. Thermal transport caused by blood flow provides valuable information for detecting abnormalities in blood flow. Here, a minimally invasive, injectable blood flow sensor is reported, consisting of a flexible, graphene-based thin film heater and MoS2-based temperature sensor array integrated on a mesh-structured polymer substrate. Upon injection through a small skull hole in the skull, the device unfolds and achieves conformal contact on the cortical surface, aligning with the target vessel. By measuring temperature variations in response to the heater activation, the injectable sensor continuously monitors blood flow changes in the underlying vessel. This approach offers a new potential for cerebral blood flow sensing via minimally invasive implantation.}, }
@article {pmid40230710, year = {2025}, author = {Finney, JN and Levy, IR and Chandrasekaran, S and Collinger, JL and Boninger, ML and Gaunt, RA and Helm, ER and Fisher, LE}, title = {Techniques to mitigate lead migration for percutaneous trials of cervical spinal cord stimulation.}, journal = {Frontiers in surgery}, volume = {12}, number = {}, pages = {1458572}, pmid = {40230710}, issn = {2296-875X}, abstract = {INTRODUCTION: Epidural spinal cord stimulation (SCS) is a clinical neuromodulation technique that is commonly used to treat neuropathic pain, with patients typically undergoing a one-week percutaneous trial phase before permanent implantation. Traditional SCS involves stimulation of the thoracic spinal cord, but there has been substantial recent interest in cervical SCS to treat upper extremity pain, restore sensation from the missing hand after amputation, or restore motor function to paretic limbs in people with stroke and spinal cord injury. Because of the additional mobility of the neck, as compared to the trunk, lead migration can be a major challenge for cervical SCS, especially during the percutaneous trial phase. The objective of this study was to optimize the implantation procedure of cervical SCS leads to minimize lead migration and increase lead stability during SCS trials.<br><br>METHODS: In this study, four subjects underwent percutaneous placement of three SCS leads targeting the cervical spinal segments as part of a clinical trial aiming to restore sensation for people with upper-limb amputation. The leads were maintained for up to 29 days and weekly x-ray imaging was used to measure rostrocaudal and mediolateral lead migration based on bony landmarks.<br><br>RESULTS AND DISCUSSION: Lead migration was primarily confined to the rostrocaudal axis with the most migration occurring during the first week. Iterative improvements to the implantation procedure were implemented to increase lead stability across subjects. There was a decrease in lead migration for patients who had more rostral placement of the SCS leads. The average migration from the day of lead implant to lead removal was 29.7&#x2005;mm for Subject 1 (lead placement ranging from T3-T4 to T1-T2), 41.9&#x2005;mm for Subject 2 (T2-T3 to C7-T1), 1.9&#x2005;mm for Subject 3 (T1-T2 to C7-T1), and 16.6&#x2005;mm for Subject 4 (T1-T2 to C7-T1). We found that initial placement of spinal cord stimulator leads in the cervical epidural space as rostral as possible was critical to minimizing subsequent rostrocaudal lead migration.}, }
@article {pmid40228689, year = {2025}, author = {Hesam-Shariati, N and Alexander, L and Stapleton, F and Newton-John, T and Lin, CT and Zahara, P and Chen, K and Restrepo, S and Skinner, IW and McAuley, JH and Moseley, GL and Jensen, MP and Gustin, SM}, title = {The Effect of an EEG Neurofeedback Intervention for Corneal Neuropathic Pain: A Single-Case Experimental Design with Multiple Baselines.}, journal = {The journal of pain}, volume = {}, number = {}, pages = {105394}, doi = {10.1016/j.jpain.2025.105394}, pmid = {40228689}, issn = {1528-8447}, abstract = {Corneal neuropathic pain is a complex condition, rarely responsive to current treatments. This trial investigated the potential effect of a novel home-based self-directed EEG neurofeedback intervention on corneal neuropathic pain using a multiple-baseline single-case experimental design. Four Participants completed a predetermined baseline of 7, 10, 14, and 17 days, randomly assigned to each participant, followed by 20 intervention sessions over four weeks. Two one-week follow-ups occurred immediately and five weeks post-intervention during which participants were encouraged to practice mental strategies. Daily pain severity and pain interference observations were the outcome measures, while anxiety, depression, or sleep problems were the generalisation measures. The results showed a medium effect on pain severity and interference across participants when comparing baseline to five-week post-intervention according to Tau-U effect sizes. At the individual level, both Tau-U and NAP effect sizes indicated significant reductions in pain severity and interference for three participants when comparing baseline to five-week post-intervention. However, the reductions indicated by the visual inspection were not considered clinically meaningful. This single-case experimental design study raises the possibility that the intervention may improve pain severity and interference for some individuals, variability in outcomes highlights the need for future research to better understand individual responses and optimize the intervention effect. REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12623000173695 PERSPECTIVE: This trial demonstrates the potential of EEG neurofeedback to reduce pain severity and interference in individuals with corneal neuropathic pain. It also highlights user preferences for technology-based interventions, emphasizing ease of use, accessibility, and self-administration to enhance adherence, especially for individuals with limited mobility or restricted healthcare access.}, }
@article {pmid40228398, year = {2025}, author = {Grigoryan, KA and Mueller, K and Wagner, M and Masri, D and Pine, KJ and Villringer, A and Sehm, B}, title = {Short-term BCI intervention enhances functional brain connectivity associated with motor performance in chronic stroke.}, journal = {NeuroImage. Clinical}, volume = {46}, number = {}, pages = {103772}, doi = {10.1016/j.nicl.2025.103772}, pmid = {40228398}, issn = {2213-1582}, abstract = {BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood.<br><br>OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke.<br><br>METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4&#xa0;weeks to allow for within-subject (n&#xa0;=&#xa0;21) and cross-group comparisons. Group 1 (n&#xa0;=&#xa0;11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n&#xa0;=&#xa0;10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training.<br><br>RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training.<br><br>CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.}, }
@article {pmid40227907, year = {2025}, author = {Zhong, Y and Wang, Y and Farina, D and Yao, L}, title = {A Closed-Loop Tactile Stimulation Training Protocol for Motor Imagery-Based BCI: Boosting BCI Performance for BCI-Deficiency Users.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3560713}, pmid = {40227907}, issn = {1558-2531}, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable users to control and communicate with the external environment. However, a significant challenge in BCI research is the occurrence of "BCI-illiteracy" or "BCI-deficiency", where a notable percentage of users (estimated at 15 to 30%) are unable to achieve successful BCI control. For those users, they are struggling to generate stable and distinguishable brain activity patterns, which are essential for BCI control. Existing neurofeedback training protocols, often rely on the trial-and-error process, which is time-consuming and inefficient, particularly for these low-performing users.<br><br>METHODS: To address this issue, we propose a closed-loop tactile stimulation training protocol, in which tactile stimulation training is incorporated within the closed neurofeedback loop, providing users with explicit guidance on how to correctly perform MI tasks. When a subject performs an incorrect MI trial, tactile-assisted MI training is provided to guide the user toward the correct brain state, while no training is given during correct performance.<br><br>RESULTS: The results from our study demonstrated that the proposed training protocol significantly enhances BCI decoding performance, with an improvement of 16.9%. Moreover, the BCI-deficiency rate was reduced by 61.5%. Further analysis revealed that the training process also led to enhanced motor imagery-related cortical activation.<br><br>CONCLUSION: The proposed training protocol significantly improved BCI decoding performance, enabling previously BCI-deficient users to surpass the 70% control threshold.<br><br>SIGNIFICANCE: This study demonstrates the effectiveness of closed-loop tactile-assisted training in enhancing BCI accessibility and efficiency, paving the way for more inclusive neurofeedback-based BCI training strategies.}, }
@article {pmid40227903, year = {2025}, author = {Mai, X and Meng, J and Ding, Y and Zhu, X and Guan, C}, title = {SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1460-1472}, doi = {10.1109/TNSRE.2025.3560434}, pmid = {40227903}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Algorithms ; Young Adult ; Photic Stimulation ; Regression Analysis ; Calibration ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; }, abstract = {The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at https://github.com/MaiXiming/SRRNet.}, }
@article {pmid40227525, year = {2025}, author = {Yan, L and Liu, Z and Wang, J and Yu, L}, title = {Integrating Hard Silicon for High-Performance Soft Electronics via Geometry Engineering.}, journal = {Nano-micro letters}, volume = {17}, number = {1}, pages = {218}, pmid = {40227525}, issn = {2150-5551}, abstract = {Soft electronics, which are designed to function under mechanical deformation (such as bending, stretching, and folding), have become essential in applications like wearable electronics, artificial skin, and brain-machine interfaces. Crystalline silicon is one of the most mature and reliable materials for high-performance electronics; however, its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics. Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials, such as transforming them into thin nanomembranes or nanowires. This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics, from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates, and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques. We explore the latest developments in Si-based soft electronic devices, with applications in sensors, nanoprobes, robotics, and brain-machine interfaces. Finally, the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.}, }
@article {pmid40226198, year = {2025}, author = {Nirabi, A and Rahman, FA and Habaebi, MH and Sidek, KA and Yusoff, S}, title = {Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.}, journal = {Data in brief}, volume = {60}, number = {}, pages = {111477}, pmid = {40226198}, issn = {2352-3409}, abstract = {This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.}, }
@article {pmid40225841, year = {2025}, author = {Kashou, N}, title = {Editorial: New horizons in stroke management.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1587791}, doi = {10.3389/fnhum.2025.1587791}, pmid = {40225841}, issn = {1662-5161}, }
@article {pmid40225573, year = {2025}, author = {Bai, R and Jia, Y and Wang, B and Wang, Z and Han, G and Liang, L and Chen, L and Ming, Y and Zhu, G and Hsu, YC and Zhao, P and Zhang, Y and Liu, Z and Liu, C and Li, Z and Liu, Y}, title = {In vivo spatiotemporal mapping of proliferation activity in gliomas via water-exchange dynamic contrast-enhanced MRI.}, journal = {Theranostics}, volume = {15}, number = {10}, pages = {4693-4707}, pmid = {40225573}, issn = {1838-7640}, mesh = {*Glioma/diagnostic imaging/pathology ; *Magnetic Resonance Imaging/methods ; Animals ; Humans ; *Cell Proliferation ; Temozolomide/pharmacology ; Cell Line, Tumor ; *Contrast Media ; Aquaporin 4/metabolism ; *Brain Neoplasms/diagnostic imaging/pathology ; *Water/metabolism ; Mice ; }, abstract = {Proliferation activity mapping is crucial for the guidance of first biopsy and treatment evaluation of gliomas due to the highly heterogenous nature of glioma tumor. Here we propose and demonstrate an ease-of-use way of in vivo spatiotemporal mapping of proliferation activity by simply tracking transmembrane water dynamics with magnetic resonance imaging (MRI). Specifically, we demonstrated that proliferation activity can accelerate the transmembrane water transport in glioma cells. Method: The transmembrane water-efflux rate (k io) measured by water-exchange dynamic contrast-enhanced (DCE) MRI. Immunofluorescence, immunohistochemistry, and immunocytochemistry staining were used to validate results obtained from the in vivo imaging studies. Results: In glioma cell cultures, k io precisely followed the dynamic changes of proliferation activity in growth cycles and response to temozolomide (TMZ) treatment. In both animal glioma model and human glioma, k io linearly and strongly correlated with the spatial heterogeneity of intra-tumoral proliferation activity. More importantly, proliferation activity predicted by the single MRI parameter k io is much more accurate than those predicted by state-of-the-art methods using multimodal standard MRIs and advanced machine learning. Upregulated aquaporin 4 (AQP4) expression were observed in most proliferating glioma cells and the knockout of AQP4 could largely slow down proliferation activity, suggesting AQP4 is the potential molecule connecting MRI-k io with proliferation activity. Conclusion: This study provides an ease-of-use, accurate, and non-invasive imaging method for the spatiotemporal monitoring of proliferation activity in glioma.}, }
@article {pmid40223771, year = {2025}, author = {Kapur, A and Van Til, M and Daignault-Newton, S and Seibel, C and Nagpal, S and Ippolito, GM and Smith, AL and Lucioni, A and Lee, U and Suskind, A and Anger, J and Chung, D and Reynolds, WS and Cameron, A and Tenggardjaja, C and Padmanabhan, P and Brucker, BM and , }, title = {Association Between Urodynamic Findings and Urinary Retention After Onabotulinumtoxin A for Idiopathic Overactive Bladder.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.70050}, pmid = {40223771}, issn = {1520-6777}, support = {//This secondary analysis did not receive any external sources of funding. Funding for the primary analysis which utilized the same original data set as the current study was Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction Foundation (SUFU); National Institutes of Health, Grant/Award Number: UL1TR002240./ ; }, abstract = {INTRODUCTION: Onabotulinumtoxin A (BTX-A) is a minimally invasive therapy for idiopathic overactive bladder (iOAB). Incomplete bladder emptying is a known risk of the procedure, with an overall rate as high as 20% in male and female patients. Risk factors for incomplete bladder emptying after BTX-A have been reported in the literature, but are widely variable amongst studies and therefore patients at increased risk of this adverse effect cannot easily be identified by clinicians. The aim of this study was to evaluate whether pre-procedure urodynamics (UDS) findings are associated with incomplete bladder emptying after intradetrusor BTX-A injection for iOAB.<br><br>METHODS: Data were analyzed from the SUFU Research Network (SURN) multi-institutional retrospective database. Men and women undergoing first-time injection of 100 units BTX-A for iOAB in 2016 were included. Subjects were excluded if they did not have record of pre-procedure and post-procedure (within 1 month) post-void residual volume (PVR). The primary outcome was incidence of urinary retention within 1 month after BTX-A, defined as PVR&#x2009;>&#x2009;300&#x2009;mL and/or initiation of self-catheterization or indwelling catheter. We assessed the association of pre-procedure UDS parameters with urinary retention using Wilcoxon rank tests, Fisher's exact test, and chi-squared tests.<br><br>RESULTS: A total of 167 subjects (141 women, 26 men) were included. Ninety-nine subjects (59%) had urodynamic data. Thirty-seven subjects (22%) had urinary retention within 1 month of BTX-A. There were no significant differences in age, gender, race, or body mass index between the retention and non-retention groups. There was no statistically significant difference in median Qmax between those who did and did not have postprocedure retention (10.0 vs. 14.3&#x2009;mL/s respectively, p&#x2009;=&#x2009;0.06). Mean PVR at the start of UDS was not statistically significant when comparing the retention and non-retention groups (22.5 vs. 10.0&#x2009;mL respectively, p&#x2009;=&#x2009;0.70). Bladder outlet obstruction index (BOOI), bladder contractility index (BCI), and presence of detrusor overactivity (DO) were not found to be associated with posttreatment retention.<br><br>CONCLUSION: This retrospective multi-institutional cohort study revealed that of patients who receive UDS before BTX-A, there are no significant UDS parameters or baseline demographic factors associated with incomplete bladder emptying after intradetrusor BTX-A injections for iOAB. Future studies that focus on better defining objective evidence-based predictors of incomplete emptying after BTX are needed to optimize patient perception of efficacy and satisfaction with this therapy.}, }
@article {pmid40223534, year = {2025}, author = {Jung, M and Abu Shihada, J and Decke, S and Koschinski, L and Graff, PS and Maruri Pazmino, S and Höllig, A and Koch, H and Musall, S and Offenhäusser, A and Rincón Montes, V}, title = {Flexible 3D Kirigami Probes for In Vitro and In Vivo Neural Applications.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2418524}, doi = {10.1002/adma.202418524}, pmid = {40223534}, issn = {1521-4095}, support = {VH-NG-1611//Helmholtz Association/ ; GRK2610//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 424556709//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; GRK2416//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 368482240//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; }, abstract = {3D microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding 2D MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue, is presented. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. The 3D kirigami MEAs are successfully deployed in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.}, }
@article {pmid40223097, year = {2025}, author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S}, title = {A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3504}, pmid = {40223097}, issn = {2041-1723}, support = {62236009//National Science Foundation of China | Key Programme/ ; }, mesh = {Humans ; *Choroid Neoplasms/diagnosis/diagnostic imaging ; *Melanoma/diagnosis/diagnostic imaging ; Machine Learning ; Artificial Intelligence ; Female ; Uveal Melanoma ; Male ; *Uveal Neoplasms/diagnostic imaging/diagnosis ; Hemangioma/diagnosis/diagnostic imaging ; Middle Aged ; Diagnosis, Differential ; Multimodal Imaging/methods ; Adult ; }, abstract = {Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.}, }
@article {pmid40223012, year = {2025}, author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Calavia, D and Serra, V and Manrique, M}, title = {Optimization of surgical interventions in auditory rehabilitation for chronic otitis media: comparative between passive middle ear implants, bone conduction implants, and active middle ear systems.}, journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery}, volume = {}, number = {}, pages = {}, pmid = {40223012}, issn = {1434-4726}, abstract = {INTRODUCTION: In otology consultations, patients with chronic otitis media (COM) often present as candidates for various hearing rehabilitation options. Selecting the most suitable approach requires careful consideration of patient preferences and expectations, the risk of disease progression, and the integrity of the bone conduction pathway. This study aims to evaluate and compare postoperative hearing outcomes in COM patients undergoing tympanoplasty (with or without passive middle ear implants), bone conduction systems (BCI), or active middle ear implants (AMEI). The objective is to assess the effectiveness of each surgical approach in hearing rehabilitation, considering the type and severity of hearing loss as well as the duration of the disease.<br><br>METHODS: Retrospective data analysis in a tertiary referral center studying average PTA across six different frequencies, speech perception at 65&#xa0;dB, influence of Eustachian tube dysfunction, reintervention rate and adverse effects, and the influence of disease duration on functional outcomes via linear regression analysis.<br><br>RESULTS: 116 patients underwent surgery due to COM between 1998 and 2024. With a slight female predominance (54.31%). AMEIs and bone conduction devices provided the highest amplification in terms of PTA and speech discrimination, with a lower reintervention rate when comparing both groups with passive middle ear implants, OR in BCI group of 0.30 (0.10; 0.89, p&#x2009;=&#x2009;0.030), OR in VSB group of 0.15 (0.04; 0.56, p&#x2009;=&#x2009;0.005). It was also observed that a longer evolution time could be associated with greater auditory gain, with a p-value&#x2009;=&#x2009;0.033.<br><br>CONCLUSIONS: The selection of each treatment option primarily depends on bone conduction thresholds, along with surgical risk, patient preferences, and MRI compatibility. In our study, AMEIs demonstrated the highest functional gain in terms of speech discrimination and frequency-specific amplification, followed by BCI. These findings support the use of implantable hearing solutions as effective alternatives for auditory rehabilitation in COM patients.}, }
@article {pmid40222332, year = {2025}, author = {Choi, JY and Kim, YJ and Shin, JS and Choi, E and Kim, Y and Kim, MG and Kim, YT and Park, BS and Kim, JK and Kim, JG}, title = {Integrative metabolic profiling of hypothalamus and skeletal muscle in a mouse model of cancer cachexia.}, journal = {Biochemical and biophysical research communications}, volume = {763}, number = {}, pages = {151766}, doi = {10.1016/j.bbrc.2025.151766}, pmid = {40222332}, issn = {1090-2104}, abstract = {Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive weight loss, muscle wasting, and systemic inflammation. Despite its clinical significance, the underlying mechanisms linking central and peripheral metabolic changes remain incompletely understood. In this study, we employed a murine model of cancer cachexia induced by intraperitoneal injection of Lewis lung carcinoma (LLC1) cells to investigate tissue-specific metabolic adaptations. Cachectic mice exhibited reduced food intake, body weight loss, impaired thermoregulation, and decreased energy expenditure. Metabolomic profiling of serum, skeletal muscle, and hypothalamus revealed distinct metabolic shifts, with increased fatty acid and ketone body utilization and altered amino acid metabolism. Notably, hypothalamic metabolite changes diverged from peripheral tissues, showing decreased neurotransmitter-related metabolites and enhanced lipid-based energy signatures. Gene expression analysis further confirmed upregulation of glycolysis- and lipid oxidation-related genes in both hypothalamus and muscle. These findings highlight coordinated yet compartmentalized metabolic remodeling in cancer cachexia and suggest that hypothalamic adaptations may play a central role in the systemic energy imbalance associated with cachexia progression.}, }
@article {pmid40221457, year = {2025}, author = {Rybář, M and Poli, R and Daly, I}, title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {613}, pmid = {40221457}, issn = {2052-4463}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; *Semantics ; *Imagination ; Animals ; Male ; Female ; Adult ; Brain/physiology ; }, abstract = {Semantic neural decoding aims to identify which semantic concepts an individual focuses on at a given moment based on recordings of their brain activity. We investigated the feasibility of semantic neural decoding to develop a new type of brain-computer interface (BCI) that allows direct communication of semantic concepts, bypassing the character-by-character spelling used in current BCI systems. We provide data from our study to differentiate between two semantic categories of animals and tools during a silent naming task and three intuitive sensory-based imagery tasks using visual, auditory, and tactile perception. Participants were instructed to visualize an object (animal or tool) in their minds, imagine the sounds produced by the object, and imagine the feeling of touching the object. Simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals were recorded from 12 participants. Additionally, EEG signals were recorded from 7 other participants in a follow-up experiment focusing solely on the auditory imagery task. These datasets can serve as a valuable resource for researchers investigating semantic neural decoding, brain-computer interfaces, and mental imagery.}, }
@article {pmid40219565, year = {2025}, author = {Liu, N and Man, L and He, F and Huang, G and Zhai, J}, title = {[Correlation between urination intermittences and urodynamic parameters in benign prostatic hyperplasia patients].}, journal = {Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences}, volume = {57}, number = {2}, pages = {328-333}, pmid = {40219565}, issn = {1671-167X}, mesh = {*Prostatic Hyperplasia/complications/physiopathology ; Humans ; Male ; *Urodynamics ; *Urination ; Retrospective Studies ; Middle Aged ; Aged ; Aged, 80 and over ; *Urinary Bladder/physiopathology ; *Urination Disorders/etiology ; }, abstract = {OBJECTIVE: To explore the impact factors and the clinical significance of the urination intermittences in benign prostatic hyperplasia (BPH) patients.<br><br>METHODS: A retrospective study was performed in BPH patients who underwent urodynamic studies in Beijing Jishuitan Hospital form January 2016 to June 2021. The patients were aged 45 to 84 years with a median age of 63 years, and all the patients had no previous history of neurological disease and had no positive findings in neurological examinations. All the patients had free uroflometry followed by urethral catheterization and urodynamic tests. The voiding work of bladder was calculated using the detrusor power curve method, and the voiding power of bladder and the voiding energy consumption were also calculated. The frequency of urination intermittences generated in uroflometry was also recorded and the patients were divided into different groups according to it. The detrusor pressure at maximal flow rate (PdetQmax), the maximal flow rate (Qmax), the bladder contractile index (BCI), the bladder outlet obstruction index (BOOI), the voiding work, the voiding power, and the voiding energy consumption were compared among the different groups. Multiva-riate analyses associated with presence of urination intermittences were performed using step-wise Logistic regressions.<br><br>RESULTS: There were 272 patients included in this study, of whom, 179 had no urination intermittence (group A), 46 had urination intermittence for only one time (group B), 22 had urination intermittence for two times (group C), and 25 had urination intermittence for three times and more (group D). The BCI were 113.4&#xb1;28.2, 101.0&#xb1;30.2, 83.3&#xb1;30.2, 81.0&#xb1;30.5 in groups A, B, C, and D, respectively; The voiding power were (29.2&#xb1;14.8) mW, (16.4&#xb1;9.6) mW, (14.5&#xb1;7.1) mW, (8.5&#xb1;5.0) mW in groups A, B, C, and D, respectively, and the differences were significant (P < 0.05). The BOOI were 41.6&#xb1;29.3, 46.4&#xb1;31.0, 41.4&#xb1;29.0, 42.7&#xb1;22.8 in groups A, B, C, and D, respectively; The voiding energy consumption were (5.41&#xb1;2.21) J/L, (4.83&#xb1;2.31) J/L, (5.02&#xb1;2.54) J/L, (4.39&#xb1;2.03) J/L in groups A, B, C, and D, respectively, and the differences were insignificant (P>0.05). Among the patients, 179 cases were negative in presence of urination intermittences and 93 cases were positive. Step-wise Logistic regression analysis showed that bladder power (OR=0.814, 95%CI: 0.765-0.866, P < 0.001), BCI (OR=1.023, 95%CI: 1.008-1.038, P=0.003), and bladder work (OR=2.232, 95%CI: 1.191-4.184, P=0.012) were independent risk factors for urination intermittences in the BPH patients.<br><br>CONCLUSION: The presence of urination intermittences in the BPH patients was mainly influenced by bladder contractile functions, and was irrelevant to the degree of bladder outlet obstruction. The increase of frequency of urination intermittences seemed to be a sign of the decrease of the bladder contractile functions in the BPH patients.}, }
@article {pmid40218833, year = {2025}, author = {Ranjbar Koleibi, E and Lemaire, W and Koua, K and Benhouria, M and Bostani, R and Serri Mazandarani, M and Gauthier, LP and Besrour, M and Ménard, J and Majdoub, M and Gosselin, B and Roy, S and Fontaine, R}, title = {Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm[2] and an Ultra-High Input Impedance.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {7}, pages = {}, pmid = {40218833}, issn = {1424-8220}, abstract = {Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.}, }
@article {pmid40218817, year = {2025}, author = {Andreev, A and Cattan, G and Congedo, M}, title = {The Riemannian Means Field Classifier for EEG-Based BCI Data.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {7}, pages = {}, pmid = {40218817}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {: A substantial amount of research has demonstrated the robustness and accuracy of the Riemannian minimum distance to mean (MDM) classifier for all kinds of EEG-based brain-computer interfaces (BCIs). This classifier is simple, fully deterministic, robust to noise, computationally efficient, and prone to transfer learning. Its training is very simple, requiring just the computation of a geometric mean of a symmetric positive-definite (SPD) matrix per class. We propose an improvement of the MDM involving a number of power means of SPD matrices instead of the sole geometric mean. By the analysis of 20 public databases, 10 for the motor-imagery BCI paradigm and 10 for the P300 BCI paradigm, comprising 587 individuals in total, we show that the proposed classifier clearly outperforms the MDM, approaching the state-of-the art in terms of performance while retaining the simplicity and the deterministic behavior. In order to promote reproducible research, our code will be released as open source.}, }
@article {pmid40218770, year = {2025}, author = {Gómez-Morales, &#xd3;W and Collazos-Huertas, DF and Álvarez-Meza, AM and Castellanos-Dominguez, CG}, title = {EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {7}, pages = {}, pmid = {40218770}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; Male ; Adult ; *Brain/physiology ; Female ; }, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only does this entail long preparation times and high costs, but it also carries the risk of losing valuable information when an electrode is damaged, further limiting its practical applicability. In this study, a signal prediction-based method is proposed to achieve high accuracy in MI classification using EEG signals recorded from only a small number of electrodes. The signal prediction model was constructed using the elastic net regression technique, allowing for the estimation of EEG signals from 22 complete channels based on just 8 centrally located channels. The predicted EEG signals from the complete channels were used for feature extraction and MI classification. The results obtained indicate a notable efficacy of the proposed prediction method, showing an average performance of 78.16% in classification accuracy. The proposed method demonstrated superior performance compared to the traditional approach that used few-channel EEG and also achieved better results than the traditional method based on full-channel EEG. Although accuracy varies among subjects, from 62.30% to an impressive 95.24%, these data indicate the capability of the method to provide accurate estimates from a reduced set of electrodes. This performance highlights its potential to be implemented in practical MI-based BCI applications, thereby mitigating the time and cost constraints associated with systems that require a high density of electrodes.}, }
@article {pmid40218647, year = {2025}, author = {Xu, H and Hassan, SA and Haider, W and Sun, Y and Yu, X}, title = {A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {7}, pages = {}, pmid = {40218647}, issn = {1424-8220}, support = {U2033202, U1333119//National Natural Science Foundation of China and Civil Aviation Administration of China/ ; 52172387//National Natural Science Foundation of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; }, abstract = {Electroencephalogram (EEG) signal analysis is crucial for understanding neural activity and advancing diagnostics in neurology. However, traditional signal decomposition (SD) techniques are hindered by two critical issues, mode mixing and mode aliasing, that compromise the quality of the decomposed signal. These challenges result in poor signal integrity, which significantly affects the accuracy of subsequent EEG interpretations and classifications. As EEG analysis is widely used in diagnosing conditions such as epilepsy, brain injuries, and sleep disorders, the impact of these shortcomings can be far-reaching, leading to misdiagnoses or delayed treatments. Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. By integrating advanced feature selection techniques and classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and feature-weighted k-nearest neighbors (FWKNN), this approach offers a significant improvement in classification accuracy, with SVM achieving up to 99.99% accuracy in the 18-channel EEG setup with a standard deviation of 0.25. The results demonstrate that FS-VMD can address the critical issues of mode mixing and aliasing, providing a more accurate and efficient solution for EEG signal analysis and diagnostics.}, }
@article {pmid40215960, year = {2025}, author = {Chen, LN and Zhou, H and Xi, K and Cheng, S and Liu, Y and Fu, Y and Ma, X and Xu, P and Ji, SY and Wang, WW and Shen, DD and Zhang, H and Shen, Q and Chai, R and Zhang, M and Yang, L and Han, F and Mao, C and Cai, X and Zhang, Y}, title = {Proton perception and activation of a proton-sensing GPCR.}, journal = {Molecular cell}, volume = {85}, number = {8}, pages = {1640-1657.e8}, doi = {10.1016/j.molcel.2025.02.030}, pmid = {40215960}, issn = {1097-4164}, mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/chemistry/genetics/ultrastructure ; *Protons ; Cryoelectron Microscopy ; HEK293 Cells ; Hydrophobic and Hydrophilic Interactions ; Histidine/metabolism/chemistry ; Hydrogen Bonding ; Protein Binding ; Models, Molecular ; Protein Conformation ; Hydrogen-Ion Concentration ; }, abstract = {Maintaining pH at cellular, tissular, and systemic levels is essential for human health. Proton-sensing GPCRs regulate physiological and pathological processes by sensing the extracellular acidity. However, the molecular mechanism of proton sensing and activation of these receptors remains elusive. Here, we present cryoelectron microscopy (cryo-EM) structures of human GPR4, a prototypical proton-sensing GPCR, in its inactive and active states. Our studies reveal that three extracellular histidine residues are crucial for proton sensing of human GPR4. The binding of protons induces substantial conformational changes in GPR4's ECLs, particularly in ECL2, which transforms from a helix-loop to a β-turn-β configuration. This transformation leads to the rearrangements of H-bond network and hydrophobic packing, relayed by non-canonical motifs to accommodate G proteins. Furthermore, the antagonist NE52-QQ57 hinders human GPR4 activation by preventing hydrophobic stacking rearrangement. Our findings provide a molecular framework for understanding the activation mechanism of a human proton-sensing GPCR, aiding future drug discovery.}, }
@article {pmid40213917, year = {2025}, author = {Wang, N and Wang, Y and Guo, M and Wang, L and Wang, X and Zhu, N and Yang, J and Wang, L and Zheng, C and Ming, D}, title = {Dynamic gamma modulation of hippocampal place cells predominates development of theta sequences.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {40213917}, issn = {2050-084X}, support = {2022ZD0205000//National Science and Technology Innovation 2030 Major Project of China/ ; T2322021//National Natural Science Foundation of China/ ; 82271218//National Natural Science Foundation of China/ ; 12271272//National Natural Science Foundation of China/ ; 81925020//National Natural Science Foundation of China/ ; 82371886//National Natural Science Foundation of China/ ; 82202797//National Natural Science Foundation of China/ ; LG-TKN-202204-01//Space Brain Project from Lingang Laboratory/ ; 2022M712365//China Postdoctoral Science Foundation/ ; }, mesh = {Animals ; *Theta Rhythm/physiology ; Rats ; *Gamma Rhythm/physiology ; *Hippocampus/physiology/cytology ; *Place Cells/physiology ; Male ; Rats, Long-Evans ; }, abstract = {The experience-dependent spatial cognitive process requires sequential organization of hippocampal neural activities by theta rhythm, which develops to represent highly compressed information for rapid learning. However, how the theta sequences were developed in a finer timescale within theta cycles remains unclear. In this study, we found in rats that sweep-ahead structure of theta sequences developing with exploration was predominantly dependent on a relatively large proportion of FG-cells, that is a subset of place cells dominantly phase-locked to fast gamma rhythms. These ensembles integrated compressed spatial information by cells consistently firing at precessing slow gamma phases within the theta cycle. Accordingly, the sweep-ahead structure of FG-cell sequences was positively correlated with the intensity of slow gamma phase precession, in particular during early development of theta sequences. These findings highlight the dynamic network modulation by fast and slow gamma in the development of theta sequences which may further facilitate memory encoding and retrieval.}, }
@article {pmid40212471, year = {2025}, author = {Feng, J and Li, Y and Huang, Z and Chen, Y and Lu, S and Hu, R and Hu, Q and Chen, Y and Wang, X and Fan, Y and He, J}, title = {Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1555690}, pmid = {40212471}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study proposes a Cross-Subject Heterogeneous Transfer Learning Model (CHTLM) to enhance the generalization of MI-fNIRS signal classification in stroke patients.<br><br>METHODS: CHTLM leverages labeled electroencephalogram (EEG) data from healthy individuals as the source domain. An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. Multi-scale fNIRS features are extracted, and a sparse Bayesian extreme learning machine classifies the fused deep learning features.<br><br>RESULTS: Experiments utilized two MI-fNIRS datasets from eight stroke patients pre- and post-rehabilitation. CHTLM achieved average accuracies of 0.831 (pre-rehabilitation) and 0.913 (post-rehabilitation), with mean AUCs of 0.887 and 0.930, respectively. Compared to five baselines, CHTLM improved accuracy by 8.6-10.5% pre-rehabilitation and 11.3-15.7% post-rehabilitation.<br><br>DISCUSSION: The model demonstrates robust cross-subject generalization by transferring task-specific knowledge from heterogeneous EEG data while addressing domain discrepancies. Its performance gains post-rehabilitation suggest clinical potential for monitoring recovery progress. CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.}, }
@article {pmid40210930, year = {2025}, author = {Qi, W and Zhang, Y and Su, Y and Hui, Z and Li, S and Wang, H and Zhang, J and Shi, K and Wang, M and Zhou, L and Zhu, D}, title = {Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {12285}, pmid = {40210930}, issn = {2045-2322}, mesh = {Humans ; *Cerebral Palsy/physiopathology/rehabilitation ; Child ; Male ; Female ; *Brain-Computer Interfaces ; Child, Preschool ; Electroencephalography ; *Robotics/methods ; *Lower Extremity/physiopathology ; }, abstract = {This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretical basis for applying MI-BCI in the rehabilitation of children with cerebral palsy (CP). This study included 30 subjects aged 4-6 years with GMFCS II-III grade, diagnosed with CP and classified as spastic diplegia. They sequentially completed EEG signal acquisition under REST, MI, and MI-BCI conditions. Clustering analysis was used to analyze EEG microstates and extract EEG microstate temporal parameters. Additionally, the strength of brain FC in different frequency bands was analyzed to compare the differences under various conditions. Four microstate classes (A-D) were identified to best explain the datasets of three groups. Compared to REST, the average duration and coverage rate of microstate D under MI and MI-BCI significantly increased (P < 0.05), while their frequency and the coverage rate and frequency of microstate A decreased. Compared to MI, the average duration of microstate C under MI-BCI significantly decreased (P < 0.05), while the frequency of microstate B significantly increased (P < 0.05). Additionally, the transition probability results showed that other microstates under REST had a higher transition probability to microstate A, while under MI and MI-BCI, other microstates had a higher transition probability to microstate D. The brain network results revealed significant differences in brain network connectivity among REST, MI, and MI-BCI across different frequency bands. No FC differences were found between REST, MI, and MI-BCI in the α2 frequency band. In the δ and γ frequency bands, MI and MI-BCI both had greater inter-electrode connectivity strength than REST. In the θ frequency band, REST had greater inter-electrode connectivity strength than MI-BCI, while MI-BCI had greater inter-electrode connectivity strength than both REST and MI. In the α1 frequency band, MI-BCI had greater inter-electrode connectivity strength than REST, and in the β frequency band, MI-BCI had greater inter-electrode connectivity strength than MI. MI-BCI can significantly alter the brain activity patterns of children with CP, particularly by enhancing the activity intensity of EEG microstates related to attention, motor planning, and execution, as well as the brain FC strength in different frequency bands. It holds high application value in the lower limb motor rehabilitation of children with CP.}, }
@article {pmid40210429, year = {2025}, author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Kapageridou, E and Ahanov, O and Kontoninas, Z and Savopoulos, C and Didangelos, T}, title = {Psychometric Properties and Dimensionality of the Greek Version of the Hypoglycemic Confidence Scale.}, journal = {Journal of nursing measurement}, volume = {}, number = {}, pages = {}, doi = {10.1891/JNM-2024-0108}, pmid = {40210429}, issn = {1945-7049}, abstract = {Background and purpose: The prevalence of type 1 diabetes mellitus (T1D) is rising at an alarming rate and is projected to continue increasing in the coming years. The primary approach to preventing diabetes-related complications in individuals with T1D is the exogenous administration of insulin. However, this method can sometimes lead to hypoglycemia, a condition with a wide range of symptoms, including loss of consciousness, seizures, coma, and, in severe cases, death. This study aims to present the psychometric properties of the Greek translation of the Hypoglycemic Confidence Scale (HCS). The HCS measures an individual's sense of personal strength and comfort based on the belief that they possess the necessary resources to manage and prevent hypoglycemia-related complications. Methods: We conducted a forward and backward translation, along with a cultural adaptation, of the HCS into Greek. The psychometric properties of the scale were evaluated through confirmatory factor analysis. To assess the reliability, we calculated the intraclass correlation coefficient, while internal consistency was measured using Cronbach's coefficient α. Construct validity was evaluated through convergent and divergent validity, comparing the HCS-Gr with the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and hemoglobin A1C levels. Differential validity was assessed using the known-groups method. Results: Ninety-seven adults with T1D, aged between 18 and 57 years (mean age: 38.6 ± 11.7), completed the HCS-Gr. The two structures of the HCS-Gr demonstrated strong internal consistency, with Cronbach's coefficient α values of 0.87 for the eight-item version and 0.86 for the nine-item version. Convergent validity was supported by moderate negative correlations between both HCS-Gr versions and the DQoL-BCI subscales and total score. The HCS-Gr also showed satisfactory test-retest reliability and differential validity, confirming its robustness as a psychometric tool. Conclusion: The HCS-Gr is a valid and reliable tool for assessing confidence (or self-efficacy) in managing hypoglycemic situations among individuals with T1D in Greece.}, }
@article {pmid40209829, year = {2025}, author = {Ruiz Ibán, MA and García Navlet, M and Marco, SM and Diaz Heredia, J and Hernando, A and Ruiz Díaz, R and Vaquero Comino, C and Alvarez Villar, S and Ávila Lafuente, JL}, title = {AUGMENTATION WITH A BOVINE BIOINDUCTIVE COLLAGEN IMPLANT OF A POSTEROSUPERIOR CUFF REPAIR SHOWS LOWER RETEAR RATES BUT SIMILAR OUTCOMES COMPARED TO NO AUGMENTATION: 2-YEAR RESULTS OF A RANDOMIZED CONTROLLED TRIAL.}, journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.arthro.2025.03.057}, pmid = {40209829}, issn = {1526-3231}, abstract = {PURPOSE: To assess the clinical and radiological outcomes of the addition of a bioinductive collagen implant (BCI) over the repair of medium-to-large posterosuperior rotator cuff tears at 24-month follow-up.<br><br>METHODS: This is an update of a randomized controlled trial that was extended from one to two-year follow-up. 124 subjects with symptomatic full-thickness posterosuperior rotator cuff tears, with fatty infiltration Goutalier grade &#x2264;2 were randomized to two groups in which a transosseous equivalent repair was performed alone (Control group) or with BCI applied over the repair (BCI group). The outcomes reassessed at 2-year follow-up were: Sugaya grade, retear rate and tendon thickness in MRI; and the clinical outcomes (pain levels, EQ-5D-5L, American Shoulder and Elbow Society[ASES] and Constant-Murley scores[CMS]).<br><br>RESULTS: There were no relevant differences in preoperative characteristics. There were no additional complications or reinterventions in the second year of follow-up. 114 (59 males-55 males, age=58.1[SD:7.35] years) of 124 randomized patients (91.9%), underwent MRI evaluation 25.4[1.95] months after surgery. There was a lower retear rate (12.3%[7/57]) in the BCI group compared to the Control group (35.1%[20/57]) (p=0.004; relative risk of retear 0.35[CI-95%:0.16 to 0.76]). Sugaya grade was also better in the BCI group (2.58[1.07] vs 3.14[1.19]; p=0.020). Two-year Clinical follow-up at 25.8[2.75] months performed in 114 of 124 patients(91.9%) showed improvements in both groups (p<0.001), with 87% improving more than the MCID for CMS and 90% for ASES, but there were no differences between groups. In subjects with both MRI and clinical assessment (n=112), those with an intact tendon presented better CMS(p=0.035), ASES(p=0.015) and pain(p=0.006) scores than those with a failed repair.<br><br>CONCLUSION: Augmentation with a BCI of a TOE repair in posterosuperior rotator cuff tears clearly reduces the retear rate at two-year follow-up without increased complication rates and similar clinical outcomes. Subjects with failed repairs had poorer clinical outcomes.<br><br>LEVEL OF EVIDENCE: Level 1, Randomized controlled trial.}, }
@article {pmid40209163, year = {2025}, author = {Kurmanavičiūtė, D and Kataja, H and Parkkonen, L}, title = {Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention.}, journal = {PloS one}, volume = {20}, number = {4}, pages = {e0319328}, pmid = {40209163}, issn = {1932-6203}, mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Attention/physiology ; Young Adult ; Evoked Potentials, Auditory/physiology ; *Auditory Perception/physiology ; Support Vector Machine ; Acoustic Stimulation ; Algorithms ; }, abstract = {Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.}, }
@article {pmid40206150, year = {2025}, author = {Zhang, H and Wang, X and Chen, G and Zhang, Y and Jian, X and He, F and Xu, M and Ming, D}, title = {Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {6}, number = {}, pages = {0206}, pmid = {40206150}, issn = {2692-7632}, abstract = {High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.}, }
@article {pmid40205860, year = {2025}, author = {Sun, Y and Yu, N and Chen, G and Liu, T and Wen, S and Chen, W}, title = {What Else Is Happening to the Mirror Neurons?-A Bibliometric Analysis of Mirror Neuron Research Trends and Future Directions (1996-2024).}, journal = {Brain and behavior}, volume = {15}, number = {4}, pages = {e70486}, pmid = {40205860}, issn = {2162-3279}, support = {21BZX005//National Social Science Fund of China/ ; 21NDQN281YB//Philosophy and Social Sciences Project of Zhejiang Province/ ; 23QNYC19ZD//Special Project for Cultivating Leading Talents in Philosophy and Social Sciences of Zhejiang Province (Cultivation of Young Talents)/ ; }, mesh = {Animals ; Humans ; *Bibliometrics ; Brain/physiology ; *Mirror Neurons/physiology ; Neurosciences/trends ; }, abstract = {BACKGROUND: Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research.<br><br>METHODS: We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer.<br><br>RESULTS: Publication output on mirror neurons peaked in 2013 and remained active. High-impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain-computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression.<br><br>CONCLUSIONS: This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio-cognitive issues and their applications in both basic science and clinical practice.}, }
@article {pmid40205038, year = {2025}, author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y}, title = {Stress dynamically modulates neuronal autophagy to gate depression onset.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {40205038}, issn = {1476-4687}, abstract = {Chronic stress remodels brain homeostasis, in which persistent change leads to depressive disorders[1]. As a key modulator of brain homeostasis[2], it remains elusive whether and how brain autophagy is engaged in stress dynamics. Here we discover that acute stress activates, whereas chronic stress suppresses, autophagy mainly in the lateral habenula (LHb). Systemic administration of distinct antidepressant drugs similarly restores autophagy function in the LHb, suggesting LHb autophagy as a common antidepressant target. Genetic ablation of LHb neuronal autophagy promotes stress susceptibility, whereas enhancing LHb autophagy exerts rapid antidepressant-like effects. LHb autophagy controls neuronal excitability, synaptic transmission and plasticity by means of on-demand degradation of glutamate receptors. Collectively, this study shows a causal role of LHb autophagy in maintaining emotional homeostasis against stress. Disrupted LHb autophagy is implicated in the maladaptation to chronic stress, and its reversal by autophagy enhancers provides a new antidepressant strategy.}, }
@article {pmid40204716, year = {2025}, author = {Amann, LK and Casasnovas, V and Gail, A}, title = {Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3372}, pmid = {40204716}, issn = {2041-1723}, support = {H2020-FETPROACT-16 732266 WP1//European Commission (EC)/ ; ZN3422//Nieders&#xe4;chsische Ministerium f&#xfc;r Wissenschaft und Kultur (Lower Saxony Ministry of Science and Culture)/ ; SFB-889 C4//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SFB 1690 B09//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; }, mesh = {Animals ; Macaca mulatta ; Male ; *Motor Cortex/physiology ; Uncertainty ; *Feedback, Sensory/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; Brain-Computer Interfaces ; Hand/physiology ; Visual Perception/physiology ; }, abstract = {Sensory uncertainty jeopardizes accurate movement. During reaching, visual uncertainty can affect the estimation of hand position (feedback) and the desired movement endpoint (target). While impairing motor learning, it is unclear how either form of uncertainty affects cortical reach goal encoding. We show that reach trajectories vary more with higher visual uncertainty of the target, but not the feedback. Accordingly, cortical motor goal activities in male rhesus monkeys are less accurate during planning and movement initiation under target but not feedback uncertainty. Yet, when monkeys critically depend on visual feedback to conduct reaches via a brain-computer interface, then visual feedback uncertainty impairs reach accuracy and neural motor goal encoding around movement initiation. Neural state space analyses reveal a dimension that separates population activity by uncertainty level in all tested conditions. Our findings demonstrate that while both target and feedback uncertainty always reflect in neural activity, uncertain feedback only deteriorates neural reach goal information and behavior when it is task-critical, i.e., when having to rely on the sensory feedback and no other more reliable sensory modalities are available. Further, uncertain target and feedback impair reach goal encoding in a time-dependent manner, suggesting that they are integrated during different stages of reach planning.}, }
@article {pmid40204228, year = {2025}, author = {Hasegawa, R and Poulin, R}, title = {Effect of parasite infections on fish body condition: a systematic review and meta-analysis.}, journal = {International journal for parasitology}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.ijpara.2025.03.002}, pmid = {40204228}, issn = {1879-0135}, abstract = {Using host body condition indices (BCIs) based on the relationship between host body mass and length is a general and pervasive approach to assess the negative effects of parasites on host health. Although many researchers, especially fish biologists and fisheries managers, commonly utilize BCIs, the overall general patterns among BCI - infection relationships remain unclear. Here, we first systematically reviewed 985 fish BCI - infection relationships from 216 publications and investigated the factors affecting the strength and directionality of effects in BCI - infection relationships. We specifically predicted that the BCI measure used, parasite taxonomic group, and the infection measure used would influence the observed effect size and directionality of BCI - infection relationships. We found that most studies were heavily biased towards specific BCI measures such as Fulton's BCI and Relative BCI. Furthermore, studies using Fulton's BCI were more likely to report significant results compared with those using other BCI measures, suggesting that index choice could lead to an overestimation of the negative effects of parasites. Our meta-regressions uncovered that the use of parasite intensity as an infection measure and studies based on experimental rather than natural infections were more likely to report significant negative effects, however there were no differences among parasite taxonomic groups. Surprisingly, many studies, especially field studies, did not report significant negative correlations between BCI and infection, contrary to widespread expectations among researchers that parasites would negatively affect fish health. We discuss potential mechanisms underlying these results. Finally, we make several recommendations for the use of BCI - infection relationships in future studies.}, }
@article {pmid40204168, year = {2025}, author = {Tan, H and Hu, YT and Goudswaard, A and Li, YJ and Balesar, R and Swaab, D and Bao, AM}, title = {Increased oxytocin/vasopressin ratio in bipolar disorder in a cohort of human postmortem adults.}, journal = {Neurobiology of disease}, volume = {209}, number = {}, pages = {106904}, doi = {10.1016/j.nbd.2025.106904}, pmid = {40204168}, issn = {1095-953X}, mesh = {Humans ; *Oxytocin/metabolism ; Female ; Male ; *Bipolar Disorder/metabolism/pathology ; Middle Aged ; Adult ; *Supraoptic Nucleus/metabolism ; *Vasopressins/metabolism ; *Paraventricular Hypothalamic Nucleus/metabolism ; Cohort Studies ; Depressive Disorder, Major/metabolism ; Aged ; *Arginine Vasopressin/metabolism ; }, abstract = {Bipolar disorder (BD) and major depressive disorder (MDD) share some common characteristics in stress-related brain circuits, but they also exhibit distinct symptoms. Our previous postmortem research on the immunoreactivity (ir) levels of neuropeptide oxytocin (OT) in the hypothalamic paraventricular nucleus (OT[PVN]) and some clinical research on plasma OT levels suggested that increased levels of OT is a potential trait marker for BD. However, dysregulation of the related neuropeptide arginine vasopressin (AVP), that often shows opposite effects for stress responses compared to OT has not been investigated in BD. Moreover, it remains so far unknown what the contribution may be of OT produced in the hypothalamic supraoptic nucleus (SON), another major source of OT (OT[SON]). Therefore, in the present postmortem study, alterations in levels of OT-ir and for the first time in AVP-ir were determined in the SON and PVN among patients with BD, MDD, and matched controls. We observed a significantly increased OT[PVN]-ir but relatively stable AVP[PVN]-ir in male BD, and a significantly decreased AVP[PVN]-ir but relatively stable OT[PVN]-ir in female BD patients. A significantly increased ratio of OT-ir/AVP-ir was observed only in BD patients in both, the PVN and SON. No significant changes in OT-ir or AVP-ir were found in MDD patients compared with controls. Our data illustrate a clear disease- and sex-specificity of the OT and AVP changes in BD. In addition, since increased AVP-ir was observed in female BD patients with lithium nephropathy, increased AVP may have a direct effect on symptoms of BD.}, }
@article {pmid40203859, year = {2025}, author = {Pang, Y and Wang, X and Zhao, Z and Han, C and Gao, N}, title = {Multi-view collaborative ensemble classification for EEG signals based on 3D second-order difference plot and CSP.}, journal = {Physics in medicine and biology}, volume = {70}, number = {8}, pages = {}, doi = {10.1088/1361-6560/adcafa}, pmid = {40203859}, issn = {1361-6560}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imaging, Three-Dimensional/methods ; Brain-Computer Interfaces ; }, abstract = {Objective.EEG signal analysis methods based on electrical source imaging (ESI) technique have significantly improved classification accuracy and response time. However, for the refined and informative source signals, the current studies have not fully considered their dynamic variability in feature extraction and lacked an effective integration of their dynamic variability and spatial characteristics. Additionally, the adaptability and complementarity of classifiers have not been considered comprehensively. These two aspects lead to the issue of insufficient decoding of source signals, which still limits the application of brain-computer interface (BCI). To address these challenges, this paper proposes a multi-view collaborative ensemble classification method for EEG signals based on three-dimensional second-order difference plot (3D SODP) and common spatial pattern.Approach.First, EEG signals are mapped to the source domain using the ESI technique, and then the source signals in the region of interest are obtained. Next, features from three viewpoints of the source signals are extracted, including 3D SODP features, spatial features, and the weighted fusion of both. Finally, the extracted multi-view features are integrated with subject-specific sub-classifier combination, and a voting mechanism is used to determine the final classification.Main results.The results show that the proposed method achieves classification accuracy of 81.3% and 82.6% respectively in two sessions of the OpenBMI dataset, which is nearly 5% higher than the state-of-the-art method, and maintains the analysis response time required for online BCI.Significance.This paper employs multi-view feature extraction to fully capture the characteristics of the source signals and enhances feature utilization through collaborative ensemble classification. The results demonstrate high accuracy and robust performance, providing a novel approach for online BCI.}, }
@article {pmid40203855, year = {2025}, author = {Yin, S and Yue, Z and Qu, H and Wang, J and Shi, B and Zhang, J}, title = {Enhancing lower-limb motor imagery using a paradigm with visual and spatiotemporal tactile synchronized stimulation.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adcaec}, pmid = {40203855}, issn = {1741-2552}, mesh = {Humans ; Male ; Female ; *Imagination/physiology ; Adult ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Young Adult ; *Photic Stimulation/methods ; *Touch/physiology ; *Motor Cortex/physiology ; Physical Stimulation/methods ; Electroencephalography/methods ; Psychomotor Performance/physiology ; }, abstract = {Objective.Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most VS induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects.Approach.This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface (MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI. Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS.Main results.The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to NVS and VS for poor performers, respectively.Significance.The proposed paradigm could enhance motor cortical activation during MI and improve classification performance by providing vivid MI guidance for subjects, offering a promise for the application of lower-limb MI-BCI in stroke rehabilitation in the future.}, }
@article {pmid40203854, year = {2025}, author = {Collinger, J and Vansteensel, MJ and Mrachacz-Kersting, N and Mattia, D and Valeriani, D and Vaughan, TM}, title = {Special Issue on Brain-Computer Interfaces: Highlighting Research from the 10th International Brain-Computer Interface Meeting.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adcaed}, pmid = {40203854}, issn = {1741-2552}, abstract = {N/A.}, }
@article {pmid40203098, year = {2025}, author = {Sîmpetru, RC and Braun, DI and Simon, AU and März, M and Cnejevici, V and de Oliveira, DS and Weber, N and Walter, J and Franke, J and Höglinger, D and Prahm, C and Ponfick, M and Del Vecchio, A}, title = {MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions.}, journal = {Science advances}, volume = {11}, number = {15}, pages = {eads9150}, pmid = {40203098}, issn = {2375-2548}, mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; *Electromyography/instrumentation ; *Spinal Cord Injuries/rehabilitation ; *Stroke Rehabilitation/instrumentation ; *Amputation, Surgical/rehabilitation ; *Brain-Computer Interfaces ; Machine Learning ; Psychomotor Performance ; Intention ; Software Validation ; }, abstract = {Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared motor neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed a wireless, high-density EMG bracelet and a software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users' needs, allowing real-time decoding of spared motor dimensions. In our study, we successfully decoded motor intent from two participants with traumatic SCI, two with spinal stroke, and three with amputations in real time, achieving multiple controllable motor dimensions within minutes. The decoded neural signals could control a digitally rendered hand, an orthosis, a prosthesis, or a two-dimensional cursor. MyoGestic's participant-centered approach allows a collaborative and iterative development of myocontrol algorithms, bridging the gap between researcher and participant, to advance intuitive EMG interfaces for neural lesions.}, }
@article {pmid40199879, year = {2025}, author = {Zhao, Y and Wu, JT and Feng, JB and Cai, XY and Wang, XT and Wang, L and Xie, W and Gu, Y and Liu, J and Chen, W and Zhou, L and Shen, Y}, title = {Dual and plasticity-dependent regulation of cerebello-zona incerta circuits on anxiety-like behaviors.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3339}, pmid = {40199879}, issn = {2041-1723}, support = {2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2023YFE0206800//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 81625006//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200620//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32225021//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170976//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY21C090003//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Anxiety/physiopathology/metabolism ; Male ; *Neuronal Plasticity/physiology ; Mice ; *Zona Incerta/physiology/physiopathology ; *Cerebellum/physiology ; Mice, Inbred C57BL ; *Cerebellar Nuclei/physiology ; Neurons/physiology/metabolism ; Behavior, Animal/physiology ; Glutamic Acid/metabolism ; Neural Pathways ; Synaptic Transmission/physiology ; }, abstract = {Clinical observation has identified cerebellar cognitive affective syndrome, which is characterized by various non-motor dysfunctions such as social disorders and anxiety. Increasing evidence has revealed reciprocal mono-/poly-synaptic connections of cerebello-cerebral circuits, forming the concept of the cerebellar connectome. In this study, we demonstrate that neurons in the cerebellar nuclei (CN) of male mice project to a subset of zona incerta (ZI) neurons through long-range glutamatergic and GABAergic transmissions, both capable of encoding acute stress. Furthermore, activating or inhibiting glutamatergic and GABAergic transmissions in the CN → ZI pathway can positively or negatively regulate anxiety and place preference through presynaptic plasticity-dependent mechanisms, as well as mediate motor-induced alleviation of anxiety. Our data support the close relationship between the cerebellum and emotional processes and suggest that targeting cerebellar outputs may be an effective approach for treating anxiety.}, }
@article {pmid40199863, year = {2025}, author = {Guttmann-Flury, E and Sheng, X and Zhu, X}, title = {Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {587}, pmid = {40199863}, issn = {2052-4463}, support = {91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Eye-Tracking Technology ; *Eye Movements ; Blinking ; Video Recording ; }, abstract = {In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potential features, providing valuable insights into users' behavior and interaction patterns. We introduce a large dataset capturing electroencephalogram (EEG) signals, eye-tracking, high-speed camera recordings, as well as subjects' mental states and characteristics, to provide a multifactor analysis of eye-related movements. Four paradigms - motor imagery, motor execution, steady-state visually evoked potentials, and P300 spellers - are selected due to their capacity to evoke various sensory-motor responses and potential influence on ocular activity. This online-available dataset contains over 46 hours of data from 31 subjects across 63 sessions, totaling 2520 trials for each of the first three paradigms, and 5670 for P300. This multimodal and multi-paradigms dataset is expected to allow the development of algorithms capable of efficiently handling eye-induced artifacts and enhancing task-specific classification. Furthermore, it offers the opportunity to evaluate the cross-paradigm robustness involving the same participants.}, }
@article {pmid40198632, year = {2025}, author = {Ueda, M and Ueno, K and Inoue, T and Sakiyama, M and Shiroma, C and Ishii, R and Naito, Y}, title = {Detection of motor-related mu rhythm desynchronization by ear EEG.}, journal = {PloS one}, volume = {20}, number = {4}, pages = {e0321107}, pmid = {40198632}, issn = {1932-6203}, mesh = {Humans ; Male ; *Electroencephalography/methods/instrumentation ; Female ; Adult ; Young Adult ; Hand/physiology ; Movement/physiology ; *Ear/physiology ; Brain-Computer Interfaces ; }, abstract = {Event-related desynchronization (ERD) of the mu rhythm (8-13 Hz) is an important indicator of motor execution, neurofeedback, and brain-computer interface in EEG. This study investigated the feasibility of an ear electroencephalography (EEG) device monitoring mu-ERD during hand grasp and release movements. The EEG data of the right hand movement and the eye opened resting condition were measured with an ear EEG device. We calculated and compared mu rhythm power and time-frequency data from 20 healthy participants during right hand movement and eye opened resting. Our results showed a significant difference of mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. These results support the utility of ear EEG in detecting motor activity-related mu-ERD. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.}, }
@article {pmid40198304, year = {2025}, author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H}, title = {BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3557499}, pmid = {40198304}, issn = {2168-2208}, abstract = {In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.}, }
@article {pmid40197656, year = {2025}, author = {Won, C and Cho, S and Jang, KI and Park, JU and Cho, JH and Lee, T}, title = {Emerging fiber-based neural interfaces with conductive composites.}, journal = {Materials horizons}, volume = {}, number = {}, pages = {}, doi = {10.1039/d4mh01854k}, pmid = {40197656}, issn = {2051-6355}, abstract = {Neural interfaces that enable bidirectional communication between neural systems and external devices are crucial for treating neurological disorders and advancing brain-machine interfaces. Key requirements for these neural interfaces are the ability to modulate electrophysiological activity without causing tissue damage in the nerve system and long-term usability. Recent advances in biomedical neural electrodes aim to reduce mechanical mismatch between devices and surrounding tissues/organs while maintaining their electrical conductivity. Among these, fiber electrodes stand out as essential candidates for future neural interfaces owing to their remarkable flexibility, controllable scalability, and facile integration with systems. Herein, we introduce fiber-based devices with conductive composites, along with their fabrication technologies, and integration strategies for future neural interfaces. Compared to conventional neural electrodes, fiber electrodes readily combine with conductive materials such as metal nanoparticles, carbon-based nanomaterials, and conductive polymers. Their fabrication technologies enable high electrical performance without sacrificing mechanical properties. In addition, the neural modulation techniques of fiber electrodes; electrical, optical, and chemical, and their applications in central and peripheral nervous systems are carefully discussed. Finally, current limitations and potential advancements in fiber-based neural interfaces are highlighted for future innovations.}, }
@article {pmid40196469, year = {2025}, author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P and , }, title = {Material Damage to Multielectrode Arrays after Electrolytic Lesioning is in the Noise.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2025.03.26.645429}, pmid = {40196469}, issn = {2692-8205}, abstract = {1The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. Each electrode was scored in six damage categories, identified from the literature: abnormal debris, metal coating cracks, silicon tip breakage, parylene C delamination, parylene C cracks, and shank fracture. This analysis confirms previous results that observed damage on explanted arrays is more severe on the outer-edge electrodes versus inner electrodes. These findings also indicate that are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning. This work provides evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays and can be a useful tool in understanding perturbations to neural systems. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.}, }
@article {pmid40196347, year = {2025}, author = {Yu, H and Mu, Q and Wang, Z and Guo, Y and Zhao, J and Wang, G and Wang, Q and Meng, X and Dong, X and Wang, S and Sun, J}, title = {A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters.}, journal = {Frontiers in medicine}, volume = {12}, number = {}, pages = {1547588}, pmid = {40196347}, issn = {2296-858X}, abstract = {BACKGROUND: Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.<br><br>METHODS: Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.<br><br>RESULTS: A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences (p < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.<br><br>CONCLUSION: This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.}, }
@article {pmid40196232, year = {2025}, author = {Yang, Y and Zhao, H and Hao, Z and Shi, C and Zhou, L and Yao, X}, title = {Recognition of brain activities via graph-based long short-term memory-convolutional neural network.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1546559}, pmid = {40196232}, issn = {1662-4548}, abstract = {INTRODUCTION: Human brain activities are always difficult to recognize due to its diversity and susceptibility to disturbance. With its unique capability of measuring brain activities, magnetoencephalography (MEG), as a high temporal and spatial resolution neuroimaging technique, has been used to identify multi-task brain activities. Accurately and robustly classifying motor imagery (MI) and cognitive imagery (CI) from MEG signals is a significant challenge in the field of brain-computer interface (BCI).<br><br>METHODS: In this study, a graph-based long short-term memory-convolutional neural network (GLCNet) is proposed to classify the brain activities in MI and CI tasks. It was characterized by implementing three modules of graph convolutional network (GCN), spatial convolution and long short-term memory (LSTM) to effectively extract time-frequency-spatial features simultaneously. For performance evaluation, our method was compared with six benchmark algorithms of FBCSP, FBCNet, EEGNet, DeepConvNets, Shallow ConvNet and MEGNet on two public datasets of MEG-BCI and BCI competition IV dataset 3.<br><br>RESULTS: The results demonstrated that the proposed GLCNet outperformed other models with the average accuracies of 78.65% and 65.8% for two classification and four classification on the MEG-BCI dataset, respectively.<br><br>DISCUSSION: It was concluded that the GLCNet enhanced the model's adaptability in handling individual variability with robust performance. This would contribute to the exploration of brain activates in neuroscience.}, }
@article {pmid40195935, year = {2025}, author = {Hasegawa, R and Poulin, R}, title = {Cause or consequence? Exploring authors' interpretations of correlations between fish body condition and parasite infection.}, journal = {Journal of fish biology}, volume = {}, number = {}, pages = {}, doi = {10.1111/jfb.70048}, pmid = {40195935}, issn = {1095-8649}, support = {202460294//Japan Society for the Promotion of Science/ ; JP22KJ0086//Japan Society for the Promotion of Science/ ; }, abstract = {We reviewed 194 publications that reported relationships between fish body condition indices (BCIs) and parasite infections, and examined the authors' intention behind this cross-sectional analysis, that is, whether authors interpreted the negative correlations as the negative effects of parasites or as fish with poor BCIs being more susceptible to infections. While 89% of studies only considered parasite infections as causes of poor BCI, studies acknowledging the opposite or bidirectional causal links were rare. We recommend considering both possibilities in any given fish host and parasite association.}, }
@article {pmid40195900, year = {2025}, author = {Shin, H and Kim, K and Lee, J and Nam, J and Baeg, E and You, C and Choi, H and Kim, M and Chung, CK and Kim, JG and Ahn, JH and Han, M and Kim, J and Yang, S and Lee, SQ and Yang, S}, title = {A Wireless Cortical Surface Implant for Diagnosing and Alleviating Parkinson's Disease Symptoms in Freely Moving Animals.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e2405179}, doi = {10.1002/adhm.202405179}, pmid = {40195900}, issn = {2192-2659}, support = {//High Risk, High Return Research Program/ ; //ETRI grant (23YB1210, Collective Behavioral Modelling in Socially Interacting Group)/ ; }, abstract = {Parkinson's disease (PD), one of the most common neurodegenerative diseases, is involved in motor abnormality, primarily arising from the degeneration of dopaminergic neurons. Previous studies have examined the electrotherapeutic effects of PD using various methodological contexts, including live conditions, wireless control, diagnostic/therapeutic aspects, removable interfaces, or biocompatible materials, each of which is separately utilized for testing the diagnosis or alleviation of various brain diseases. Here, a cortical surface implant designed to improve motor function in freely moving PD animals is presented. This implant, a minimally invasive system equipped with a graphene electrode array, is the first integrated system to exhibit biocompatibility, wearability, removability, target specificity, and wireless control. The implant positioned at the motor cortical surface activates the motor cortex to maximize therapeutic effects and minimize off-target effects while monitoring motor activities. In PD animals, cortical motor surface stimulation restores motor function and brain waves, which corresponds to potentiated synaptic responses. Furthermore, these changes are associated with the upregulation of metabotropic glutamate receptor 5 (mGluR5, Grm5) and D5 dopamine receptor (D5R, Drd5) genes in the glutamatergic synapse. The newly designed wireless neural implant demonstrates capabilities in both real-time diagnostics and targeted therapeutics, suggesting its potential as a wireless system for biomedical devices for patients with PD and other neurodegenerative diseases.}, }
@article {pmid40195429, year = {2025}, author = {Ming, Z and Yu, W and Fan, J and Ling, G and Fengming, C and Wei, T}, title = {Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {11829}, pmid = {40195429}, issn = {2045-2322}, support = {XWRCHT20220045//the Xuzhou Key Medical Talents Project/ ; No.52375224//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; *Upper Extremity/physiopathology ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy ; Aged ; Electroencephalography ; *Kinesthesis/physiology ; Treatment Outcome ; Adult ; }, abstract = {This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.}, }
@article {pmid40194524, year = {2025}, author = {Johnson, TR and Haddix, CA and Ajiboye, AB and Taylor, DM}, title = {Simplified control of neuromuscular stimulation systems for restoration of reach with limb stiffness as a modifiable degree of freedom.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adc9e3}, pmid = {40194524}, issn = {1741-2552}, abstract = {Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: 1) optimizing stimulation across muscles with overlap in function, 2) coordinating stimulation across joints, and 3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated. Approach: We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected in silico, and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time. Main Results: By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g., 20 degree angular intervals) still produced good results with median endpoint position errors of less than 1 cm-a range that should be easy to correct for with real-time visual feedback. Significance: Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality. .}, }
@article {pmid40193612, year = {2025}, author = {Kim, H and Kim, JH and Lee, YJ and Lee, J and Han, H and Yi, H and Kim, H and Kim, H and Kang, TW and Chung, S and Ban, S and Lee, B and Lee, H and Im, CH and Cho, SJ and Sohn, JW and Yu, KJ and Kang, TJ and Yeo, WH}, title = {Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {15}, pages = {e2419304122}, pmid = {40193612}, issn = {1091-6490}, support = {ECCS-2025462//NSF (NSF)/ ; P0017303//Korea Institute for Advancement of Technology (KIAT)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Hair Follicle/physiology ; Electroencephalography/methods/instrumentation ; *Augmented Reality ; Artifacts ; *Brain/physiology ; Motion ; Algorithms ; Electrodes ; Evoked Potentials, Visual/physiology ; }, abstract = {Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm[-2]) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.}, }
@article {pmid40193313, year = {2025}, author = {Estivalet, KM and Pettenuzzo, TSA and Mazzilli, NL and Ferreira, LF and Cechetti, F}, title = {The use of brain-machine interface, motor imagery, and action observation in the rehabilitation of individuals with Parkinson's disease: A protocol study for a randomized clinical trial.}, journal = {PloS one}, volume = {20}, number = {4}, pages = {e0315148}, pmid = {40193313}, issn = {1932-6203}, mesh = {Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Cognition ; *Imagery, Psychotherapy/methods ; *Parkinson Disease/rehabilitation/physiopathology ; Single-Blind Method ; Upper Extremity/physiopathology ; Randomized Controlled Trials as Topic ; }, abstract = {BACKGROUND: Parkinson's disease (PD) is a neurodegenerative condition that impacts motor planning and control of the upper limbs (UL) and leads to cognitive impairments. Rehabilitation approaches, including motor imagery (MI) and action observation (AO), along with the use of brain-machine interfaces (BMI), are essential in the PD population to enhance neuroplasticity and mitigate symptoms.<br><br>OBJECTIVE: To provide a description of a rehabilitation protocol for evaluating the effects of isolated and combined applications of MI and action observation (AO), along with BMI, on upper limb (UL) motor changes and cognitive function in PD.<br><br>METHODS: This study provides a detailed protocol for a single-blinded, randomized clinical trial. After selection, participants will be randomly assigned to one of five experimental groups. Each participant will be assessed at three points: pre-intervention, post-intervention, and at a follow-up four weeks after the intervention ends. The intervention consists of 10 sessions, each lasting approximately 60 minutes.<br><br>EXPECTED RESULTS: The primary outcome expected is an improvement in the Test d'&#xc9;valuation des Membres Sup&#xe9;rieurs de Personnes &#xc2;g&#xe9;es score, accompanied by a reduction in task execution time. Secondary outcomes include motor symptoms in the upper limbs, assessed via the Unified Parkinson's Disease Rating Scale - Part III and the 9-Hole Peg Test; cognitive function, assessed with the PD Cognitive Rating Scale; and occupational performance, assessed with the Canadian Occupational Performance Measure.<br><br>DISCUSSION: This study protocol is notable for its intensive daily sessions. Both MI and AO are low-cost, enabling personalized interventions that physiotherapists and occupational therapists can readily replicate in practice. While BMI use does require professionals to acquire an exoskeleton, the protocol ensures the distinctiveness of the interventions and, to our knowledge, is the first to involve individuals with PD.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05696925.}, }
@article {pmid40191683, year = {2025}, author = {Miri, M and Abootalebi, V and Saeedi-Sourck, H and Van De Ville, D and Behjat, H}, title = {Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks.}, journal = {Journal of medical signals and sensors}, volume = {15}, number = {}, pages = {7}, pmid = {40191683}, issn = {2228-7477}, abstract = {BACKGROUND: Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.<br><br>METHODS: In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.<br><br>RESULTS: The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.<br><br>CONCLUSIONS: Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.}, }
@article {pmid40189874, year = {2025}, author = {Li, X and Zhang, J and Shi, B and Li, Y and Wang, Y and Shuai, K and Li, Y and Ming, G and Song, T and Pei, W and Sun, B}, title = {Freestanding Transparent Organic-Inorganic Mesh E-Tattoo for Breathable Bioelectrical Membranes with Enhanced Capillary-Driven Adhesion.}, journal = {ACS applied materials &amp; interfaces}, volume = {17}, number = {15}, pages = {22337-22351}, doi = {10.1021/acsami.5c00565}, pmid = {40189874}, issn = {1944-8252}, mesh = {Humans ; *Wearable Electronic Devices ; *Tattooing ; Brain-Computer Interfaces ; }, abstract = {The electronic tattoo (e-tattoo), a cutting-edge wearable sensor technology adhered to human skin, has garnered significant attention for its potential in brain-computer interfaces (BCIs) and routine health monitoring. Conventionally, flexible substrates with adhesion force on dewy surfaces pursue seamless contact with skin, employing compact airtight substrates, hindering air circulation between skin and the surrounding environment, and compromising long-term wearing comfort. To address these challenges, we have developed a freestanding transparent e-tattoo featuring flexible serpentine mesh bridges with a unique full-breathable multilayer structure. The mesh e-tattoo demonstrates remarkable ductility and air permeability while maintaining robust electronic properties, even after significant mechanical deformation. Furthermore, it exhibits an impressive visible-light transmittance of up to 95%, coupled with a low sheet resistance of 0.268 Ω sq[-1], ensuring both optical clarity and electrical efficiency. By increasing the number of menisci between the mesh e-tattoo and the skin, the total adhesion force increases due to the cumulative capillary-driven effect. We also successfully demonstrated high-quality bioelectric signal collections. In particular, the controlling virtual reality (VR) objects using electrooculogram (EOG) signals collected by mesh e-tattoos were achieved to demonstrate their potential for human-computer interactions (HCIs). This freestanding transparent e-tattoo with a fully breathable mesh structure represents a significant advancement in flexible electrodes for bioelectrical signal monitoring applications.}, }
@article {pmid40189123, year = {2025}, author = {Wang, F and Ren, J and Cai, Q and Liang, R and Wang, L and Yang, Q and Tian, Y and Zheng, C and Yang, J and Ming, D}, title = {Theta-gamma phase-amplitude coupling as a promising neurophysiological biomarker for evaluating the efficacy of low-intensity focused ultrasound stimulation on vascular dementia treatment.}, journal = {Experimental neurology}, volume = {389}, number = {}, pages = {115237}, doi = {10.1016/j.expneurol.2025.115237}, pmid = {40189123}, issn = {1090-2430}, abstract = {Low-intensity focused ultrasound stimulation (LIFUS) has garnered attention for its potential in vascular dementia (VD) treatment. However, the lack of sufficient data supporting its efficacy and elucidating its mechanisms of action limits its further clinical translation and application. Considerable researches support the idea that LIFUS can improve the disturbance of neural oscillation modes caused by a variety of neurological diseases. However, the effect of LIFUS on neural oscillation modes in VD remains unclear. Therefore, this study aims to investigate the therapeutic effects of LIFUS on neural oscillation modes in VD. To achieve this purpose, the VD model was established via the bilateral common carotid artery occlusion, followed by two weeks of LIFUS treatment targeting the bilateral hippocampus. The therapeutic effects of LIFUS were evaluated by behavioral tests and cerebral blood flow measurement. Electrophysiological signals were recorded from the hippocampal CA1 and CA3 and medial prefrontal cortex (mPFC). The results indicated LIFUS could effectively improve cognitive dysfunction in VD rats. The underlying electrophysiological mechanisms involved the restoration of phase-amplitude coupling (PAC) of theta-gamma oscillations within both the CA3-CA1 local circuit and the hippocampus-mPFC cross-brain circuit. Classification results based on PAC characteristics suggested that PAC metrics are effective for evaluating the efficacy of LIFUS in treating VD, with optimal recognition performance observed in the hippocampus-mPFC cross-brain circuit. Our findings provide neuroelectrophysiological insights into the mechanisms of LIFUS in VD treatment and propose a promising diagnostic biomarker for evaluating LIFUS efficacy in future applications.}, }
@article {pmid40187178, year = {2025}, author = {S, P and M, S}, title = {Design of asynchronous low-complexity SSVEP-based brain control interface speller.}, journal = {Computers in biology and medicine}, volume = {190}, number = {}, pages = {110062}, doi = {10.1016/j.compbiomed.2025.110062}, pmid = {40187178}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; Wireless Technology ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) provide a transformative solution, addressing communication challenges for individuals with speech impairments or neuromuscular disorders. The real-time wireless asynchronous BCI speller system utilizes electroencephalography (EEG) signals, tapping the brain's electrical activity for effective communication.<br><br>METHODS: Users interact with a screen featuring flickering stimuli, each representing cursor movement and character selection. The system includes cursor movements, displays selected characters, and produces an audio output of the complete word. Users generate real-time SSVEP responses captured wirelessly through an EEG acquisition system by directing attention to the stimulus. The single-channel EEG signal is wirelessly transmitted to a Raspberry Pi processing module through Wi-Fi. The EEG signals are decoded using modified power spectral density (PSD) analysis to identify the user's focus, maneuvering the cursor for character selection.<br><br>RESULTS: In experiments with ten subjects, the single-channel asynchronous low-complexity BCI speller system achieved 95.2% SSVEP identification accuracy with a detection time of 1.05 s for selecting each character/target and an information transfer rate (ITR) of 119.82 bits/min.<br><br>CONCLUSION: This underscores its efficacy in enabling individuals to spell words and communicate efficiently. The proposed real-time wireless BCI speller system is an effective tool for communication-challenged individuals, enhancing communication efficiency through brain signals.}, }
@article {pmid40183071, year = {2025}, author = {Muthukrishnan, SP and Atyabi, A}, title = {Editorial: Neural mechanisms of motor planning in assisted voluntary movement.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1582214}, pmid = {40183071}, issn = {1662-5161}, }
@article {pmid40182217, year = {2025}, author = {Wirawan, IMA and Paramarta, K}, title = {Acquisition Of Balinese Imagined Spelling using Electroencephalogram (BISE) Dataset.}, journal = {Data in brief}, volume = {60}, number = {}, pages = {111454}, pmid = {40182217}, issn = {2352-3409}, abstract = {One of the main goals of today's technology is to create a connected environment between humans and technological devices to perform daily physical activities. However, users with speech disorders cannot use this application. Loss of verbal communication can be caused by injuries and neurodegenerative diseases that affect motor production, speech articulation, and language comprehension. To overcome this problem, Brain-Computer Interfaces (BCI) use EEG signals as assistive technology to provide a new communication channel for individuals who cannot communicate due to loss of motor control. Of the several BCI studies that use EEG signals, no studies have studied Balinese characters. As a first step, this study examines the acquisition of EEG signal data for Balinese character recognition. There are several stages in obtaining EEG signal data for Balinese character spelling imagination in this study: preparation of research documents, preparation of stimulus media, submission of ethical permits, determination of participants, recording process, data presentation, and publication of datasets. The result datasets from this study are in the form of raw data, and data was analyzed for 18 Balinese and 6 vowel characters, both spelling and imagined.}, }
@article {pmid40182177, year = {2025}, author = {Paillard, J and Hipp, JF and Engemann, DA}, title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.}, journal = {Patterns (New York, N.Y.)}, volume = {6}, number = {3}, pages = {101182}, pmid = {40182177}, issn = {2666-3899}, abstract = {Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.}, }
@article {pmid40181137, year = {2025}, author = {Wang, G and Wang, W and Wang, Z and Huang, S and Liu, Y and Ming, D}, title = {The sixth finger illusion induced by palm outside stroking shows stable ownership and independence.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {11447}, pmid = {40181137}, issn = {2045-2322}, support = {2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; }, mesh = {Humans ; *Illusions/physiology ; Male ; Female ; *Fingers/physiology ; Adult ; Young Adult ; *Hand/physiology ; *Touch Perception/physiology ; }, abstract = {Recently, the sixth finger illusion has been widely studied for body representation. It remains unclear how the stroking area, visual effects and the number of trials affect the illusion. We recruited 80 participants to conduct five trials by stroking the palm outside or little finger outside in conditions with and without wearing supernumerary rubber finger. The results show the stroking area has a greater impact on the intensity and independence of the illusion. And the palm outside can induce a stronger and more independent illusion. In addition, the sixth finger illusion induced by these four conditions was significantly influenced by the number of trials, and there is a significant enhancement in the intensity of the illusion induced by the palm outside as the number of trials increases. These indicate that stroking the outer lateral side of the palm can induce a relatively stronger and more independent sixth finger illusion, and the intensity of it reaches a steady state after three trials when wearing a supernumerary rubber finger and five trials when not wearing a supernumerary rubber finger. This study adds evidence to the research on multisensory integration and sensory feedback of the supernumerary robotic fingers.}, }
@article {pmid40181122, year = {2025}, author = {Rawat, K and Sharma, T}, title = {An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {11379}, pmid = {40181122}, issn = {2045-2322}, mesh = {Humans ; Electroencephalography/methods ; Deep Learning ; *Nervous System Diseases/diagnosis/physiopathology ; Electrocardiography ; *Neural Networks, Computer ; *Mental Disorders/diagnosis/physiopathology ; Adult ; Male ; Female ; }, abstract = {Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative methodology for predicting mental illnesses such as epilepsy, sleep disorders, bipolar disorder, eating disorders, and depression using a multimodal deep learning framework that integrates neurocardiac data fusion. The proposed framework combines MEG, EEG, and ECG signals to create a more comprehensive understanding of brain and cardiac function in individuals with mental disorders. The multimodal deep learning approach uses an integrated CNN-Bi-Transformer, i.e., CardioNeuroFusionNet, which can process multiple types of inputs simultaneously, allowing for the fusion of various modalities and improving the performance of the predictive representation. The proposed framework has undergone testing on data from the Deep BCI Scalp Database and was further validated on the Kymata Atlas dataset to assess its generalizability. The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. The neurocardiac data fusion has been found to provide additional insights into the relationship between brain and cardiac function in neurological conditions, which could potentially lead to more accurate diagnosis and personalized treatment options. The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.}, }
@article {pmid40180157, year = {2025}, author = {Liang, W and Xu, R and Wang, X and Cichocki, A and Jin, J}, title = {Enhancing robustness of spatial filters in motor imagery based brain-computer interface via temporal learning.}, journal = {Journal of neuroscience methods}, volume = {418}, number = {}, pages = {110441}, doi = {10.1016/j.jneumeth.2025.110441}, pmid = {40180157}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; *Motor Activity/physiology ; }, abstract = {BACKGROUND: In motor imagery-based brain-computer interface (MI-BCI) EEG decoding, spatial filtering play a crucial role in feature extraction. Recent studies have emphasized the importance of temporal filtering for extracting discriminative features in MI tasks. While many efforts have been made to optimize feature extraction externally, stabilizing features from spatial filtering remains underexplored.<br><br>NEW METHOD: To address this problem, we propose an approach to improve the robustness of temporal features by minimizing instability in the temporal domain. Specifically, we utilize Jensen-Shannon divergence to quantify temporal instability and integrate decision variables to construct an objective function that minimizes this instability. Our method enhances the stability of variance and mean values in the extracted features, improving the identification of discriminative features and reducing the effects of instability.<br><br>RESULTS: The proposed method was applied to spatial filtering models, and tested on two publicly datasets as well as a self-collected dataset. Results demonstrate that the proposed method significantly boosts classification accuracy, confirming its effectiveness in enhancing temporal feature stability.<br><br>We compared our method with spatial filtering methods, and the-state-of-the-art models. The proposed approach achieves the highest accuracy, with 92.43&#x202f;% on BCI competition III IVa dataset, 84.45&#x202f;% on BCI competition IV 2a dataset, and 73.18&#x202f;% on self-collected dataset.<br><br>CONCLUSIONS: Enhancing the instability of temporal features contributes to improved MI-BCI performance. This not only improves classification performance but also provides a stable foundation for future advancements. The proposed method shows great potential for EEG decoding.}, }
@article {pmid40179638, year = {2025}, author = {Thielen, J and Tangermann, M and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ}, title = {Towards an sEEG-based BCI using code-modulated VEP: A case study showing the influence of electrode location on decoding efficiency.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {173}, number = {}, pages = {213-215}, doi = {10.1016/j.clinph.2025.03.034}, pmid = {40179638}, issn = {1872-8952}, }
@article {pmid40177878, year = {2025}, author = {Bhamidipaty, V and Botchu, B and Bhamidipaty, DL and Guntoory, I and Iyengar, KP}, title = {ChatGPT for speech-impaired assistance.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-3}, doi = {10.1080/17483107.2025.2483300}, pmid = {40177878}, issn = {1748-3115}, abstract = {Background: Speech and language impairments, though often used interchangeably, are two very distinct types of challenges. A speech impairment may lead to impaired ability to produce speech sounds whilst communication may be affected due to lack of fluency or articulation of words. Consequently this may affect a person's ability to articulate may affect academic achievement, social development and progress in life. ChatGPT (Generative Pretrained Transformer) is an open access AI (Artificial Intelligence) tool developed by Open AI® based on Large language models (LLMs) with the ability to respond to human prompts to generate texts using Supervised and Unsupervised Machine Learning (ML) Algorithms. This article explores the current role and future perspectives of ChatGPT AI Tool for Speech-Impaired Assistance. Methods: A cumulative search strategy using databases of PubMed, Google Scholar, Scopus and grey literature was conducted to generate this narrative review. Results: A spectrum of Enabling Technologies for Speech &amp; Language Impairment have been explored. Augmentative and Alternative Communication technology (AAC), Integration with Neuroprosthesis technology and Speech therapy applications offer considerable potential to aid speech and language impaired individuals. Conclusion: Current applications of AI, ChatGPT and other LLM's offer promising solutions in enhancing communication in people affected by Speech and Language impairment. However, further research and development is required to ensure affordability, accessibility and authenticity of these AI Tools in clinical Practice.}, }
@article {pmid40175961, year = {2025}, author = {Guo, X and Deng, R and Lai, J and Hu, S}, title = {Is muscarinic receptor agonist effective and tolerant for schizophrenia?.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {323}, pmid = {40175961}, issn = {1471-244X}, mesh = {Humans ; *Schizophrenia/drug therapy ; *Muscarinic Agonists/adverse effects/therapeutic use ; Randomized Controlled Trials as Topic ; *Antipsychotic Agents/therapeutic use/adverse effects ; }, abstract = {BACKGROUND: Several randomized clinical trials (RCTs) have recently examined the efficacy and tolerability of muscarinic receptor agonists in schizophrenia. However, whether therapeutics targeting muscarinic receptors improve symptom management and reduce side effects remains systemically unexplored.<br><br>METHODS: Embase, PubMed, and Web of Science were searched from inception until Jan 9, 2025. Altogether, the efficacy and safety outcomes of four RCTs (397 individuals in the muscarinic receptor agonists group, and 374 in the placebo control group) were meta-analyzed. To compare scores of positive and negative syndrome scale (PANSS), response rate, discontinuation rate, and adverse events with muscarinic receptor agonists vs. placebo in patients with schizophrenia, scale changes were pooled as mean difference (MD) for continuous outcomes and risk ratio (RR) for categorical outcomes.<br><br>RESULTS: It revealed that muscarinic receptor agonists were superior to placebo in terms of decrease in the total PANSS score (MD, -&#x2009;9.92; 95% CI, -12.46 to -7.37; I[2]&#x2009;=&#x2009;0%), PANSS positive symptom subscore (MD, -&#x2009;3.21; 95% CI, -4.02 to -2.40; I[2]&#x2009;=&#x2009;0%), and PANSS negative symptom subscore (MD, -1.79; 95% CI, -2.47 to -1.11; I[2]&#x2009;=&#x2009;48%). According to the study-defined response rate, the pooled muscarinic receptor agonists vs. placebo RR was 2.08 (95% CI, 1.59 to 2.72; I[2]&#x2009;=&#x2009;0%). No significance was found in the discontinuation rate. Muscarinic receptor agonists were associated with a higher risk of nausea (RR&#x2009;=&#x2009;4.61, 95% CI, 2.65 to 8.02; I[2]&#x2009;=&#x2009;3%), and in particular, xanomeline-trospium was associated with risks of dyspepsia, vomiting, and constipation.<br><br>CONCLUSIONS: The findings highlighted an efficacy advantage with tolerated adverse event profiles for muscarinic receptor agonists in schizophrenia.}, }
@article {pmid40175631, year = {2025}, author = {Qi, Y and Zhu, X and Xiong, X and Yang, X and Ding, N and Wu, H and Xu, K and Zhu, J and Zhang, J and Wang, Y}, title = {Human motor cortex encodes complex handwriting through a sequence of stable neural states.}, journal = {Nature human behaviour}, volume = {}, number = {}, pages = {}, pmid = {40175631}, issn = {2397-3374}, support = {62276228//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62336007//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 ± 2.0 strokes). We find that MC activity evolves through a sequence of states corresponding to the writing of stroke fragments during complicated handwriting. The directional tuning curve of MC neurons remains stable within states, but its gain or preferred direction strongly varies across states. By building models that can automatically infer the neural states and implement state-dependent directional tuning, we can significantly better explain the firing pattern of individual neurons and reconstruct recognizable handwriting trajectories with 69% improvement compared with baseline models. Our findings unveil that skilled and sophisticated movements are encoded through state-specific neural configurations.}, }
@article {pmid40175376, year = {2025}, author = {Dong, L and Ke, Y and Zhu, X and Liu, S and Ming, D}, title = {Long-term cognitive and neurophysiological effects of mental rotation training.}, journal = {NPJ science of learning}, volume = {10}, number = {1}, pages = {16}, pmid = {40175376}, issn = {2056-7936}, support = {81741139//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Mental rotation, a crucial aspect of spatial cognition, can be improved through repeated practice. However, the long-term effects of combining training with non-invasive brain stimulation and its neurophysiological correlates are not well understood. This study examined the lasting effects of a 10-day mental rotation training with high-definition transcranial direct current stimulation (HD-tDCS) on behavioral and neural outcomes in 34 healthy participants. Participants were randomly assigned to the Active and Shan groups, with equal group sizes. Mental rotation tests and EEG recordings were conducted at baseline, 1 day, 20 days, and 90 days post-training. Although HD-tDCS showed no significant effect, training led to improved accuracy, faster response times, and enhanced task-evoked EEG responses, with benefits lasting up to 90 days. Notably, task-evoked EEG responses remained elevated 20 days post-training. Individual differences, such as gender and baseline performance, influenced the outcomes. These results emphasize the potential of mental rotation training for cognitive enhancement and suggest a need for further investigation into cognition-related neuroplasticity.}, }
@article {pmid40174604, year = {2025}, author = {Kojima, S and Kortenbach, BE and Aalberts, C and Miloševska, S and de Wit, K and Zheng, R and Kanoh, S and Musso, M and Tangermann, M}, title = {Influence of pitch modulation on event-related potentials elicited by Dutch word stimuli in a brain-computer interface language rehabilitation task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adc83d}, pmid = {40174604}, issn = {1741-2552}, abstract = {OBJECTIVE: Recently, a novel language training using an auditory brain-computer interface (BCI) based on electroencephalogram recordings has been proposed for chronic stroke patients with aphasia. Tested with native German patients, it has shown significant and medium to large effect sizes in improving multiple aspects of language. During the training, the auditory BCI system delivers word stimuli using six spatially arranged loudspeakers. As delivering the word stimuli via headphones reduces spatial cues and makes the attention to target words more difficult, we investigate the influence of added pitch information. While pitch modulations have shown benefits for tone stimuli, they have not yet been investigated in the context of language stimuli.<br><br>APPROACH: The study translated the German experimental setup into Dutch. Seventeen native Dutch speakers participated in a single session of an exploratory study. An incomplete Dutch sentence cued them to listen to a target word embedded into a sequence of comparable non-target words while an electroencephalogram was recorded. Four conditions were compared within-subject to investigate the influence of pitch modulation: presenting the words spatially from six loudspeakers without (6D) and with pitch modulation (6D-Pitch), via stereo headphones with simulated spatial cues and pitch modulation (Stereo-Pitch), and via headphones without spatial cues or pitch modulation (Mono).<br><br>MAIN RESULTS: Comparing the 6D conditions of both language setups, the Dutch setup could be validated. For the Dutch setup, the binary AUC classification score in the 6D and the 6D-Pitch condition were 0.75 and 0.76, respectively, and adding pitch information did not significantly alter the binary classification accuracy of the event-related potential responses. The classification scores in the 6D condition and the Stereo-Pitch condition were on the same level.<br><br>SIGNIFICANCE: The competitive performance of pitch-modulated word stimuli suggests that the complex hardware setup of the 6D condition could be replaced by a headphone condition. If future studies with aphasia patients confirm the effectiveness and higher usability of a headphone-based language rehabilitation training, a simplified setup could be implemented more easily outside of clinics to deliver frequent training sessions to patients in need.}, }
@article {pmid40174326, year = {2025}, author = {Wan, P and Xue, H and Zhang, S and Kong, W and Shao, W and Wen, B and Zhang, D}, title = {Image by co-reasoning: A collaborative reasoning-based implicit data augmentation method for dual-view CEUS classification.}, journal = {Medical image analysis}, volume = {102}, number = {}, pages = {103557}, doi = {10.1016/j.media.2025.103557}, pmid = {40174326}, issn = {1361-8423}, mesh = {Humans ; Ultrasonography/methods ; *Liver Neoplasms/diagnostic imaging ; *Breast Neoplasms/diagnostic imaging ; *Contrast Media ; Machine Learning ; Female ; Retrospective Studies ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; }, abstract = {Dual-view contrast-enhanced ultrasound (CEUS) data are often insufficient to train reliable machine learning models in typical clinical scenarios. A key issue is that limited clinical CEUS data fail to cover the underlying texture variations for specific diseases. Implicit data augmentation offers a flexible way to enrich sample diversity, however, inter-view semantic consistency has not been considered in previous studies. To address this issue, we propose a novel implicit data augmentation method for dual-view CEUS classification, which performs a sample-adaptive data augmentation with collaborative semantic reasoning across views. Specifically, the method constructs a feature augmentation distribution for each ultrasound view of an individual sample, accounting for intra-class variance. To maintain semantic consistency between the augmented views, plausible semantic changes in one view are transferred from similar instances in the other view. In this retrospective study, we validate the proposed method on the dual-view CEUS datasets of breast cancer and liver cancer, obtaining the superior mean diagnostic accuracy of 89.25% and 95.57%, respectively. Experimental results demonstrate its effectiveness in improving model performance with limited clinical CEUS data. Code: https://github.com/wanpeng16/CRIDA.}, }
@article {pmid40173067, year = {2025}, author = {Tian, M and Li, S and Xu, R and Cichocki, A and Jin, J}, title = {An Interpretable Regression Method for Upper Limb Motion Trajectories Detection with EEG Signals.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3557255}, pmid = {40173067}, issn = {1558-2531}, abstract = {OBJECTIVE: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.<br><br>METHODS: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.<br><br>RESULTS: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.<br><br>CONCLUSION: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.<br><br>SIGNIFICANCE: This work provides a novel perspective on the comprehensive study of movement disorders.}, }
@article {pmid40172963, year = {2025}, author = {Yang, Z and Zheng, Y and Ma, D and Wang, L and Zhang, J and Song, T and Wang, Y and Zhang, Y and Nan, F and Su, N and Gao, Z and Guo, J}, title = {Phosphatidylinositol 4,5-bisphosphate activation mechanism of human KCNQ5.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {14}, pages = {e2416738122}, pmid = {40172963}, issn = {1091-6490}, support = {2020YFA0908501//MOST | National Key Research and Development Program of China (NKPs)/ ; 32371204//MOST | National Natural Science Foundation of China (NSFC)/ ; }, mesh = {Humans ; *Phosphatidylinositol 4,5-Diphosphate/metabolism ; *KCNQ Potassium Channels/metabolism/chemistry/genetics ; *Cryoelectron Microscopy ; Calmodulin/metabolism/chemistry ; Protein Binding ; Ion Channel Gating ; Models, Molecular ; Protein Conformation ; }, abstract = {The human voltage-gated potassium channels KCNQ2, KCNQ3, and KCNQ5 can form homo- and heterotetrameric channels that are responsible for generating the neuronal M current and maintaining the membrane potential stable. Activation of KCNQ channels requires both the depolarization of membrane potential and phosphatidylinositol 4,5-bisphosphate (PIP2). Here, we report cryoelectron microscopy structures of the human KCNQ5-calmodulin (CaM) complex in the apo, PIP2-bound, and both PIP2- and the activator HN37-bound states in either a closed or an open conformation. In the closed conformation, a PIP2 molecule binds in the middle of the groove between two adjacent voltage-sensing domains (VSDs), whereas in the open conformation, one additional PIP2 binds to the interface of VSD and the pore domain, accompanying structural rearrangement of the cytosolic domain of KCNQ and CaM. The structures, along with electrophysiology analyses, reveal the two different binding modes of PIP2 and elucidate the PIP2 activation mechanism of KCNQ5.}, }
@article {pmid40172828, year = {2025}, author = {Li, M and Zhao, Q and Zhang, T and Ge, J and Wang, J and Xu, G}, title = {A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40172828}, issn = {1995-8218}, abstract = {A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.}, }
@article {pmid40172544, year = {2025}, author = {Li, H and Li, C and Zhao, H and Li, Q and Zhao, Y and Gong, J and Li, G and Yu, H and Tian, Q and Liu, Z and Han, F}, title = {Flexible fibrous electrodes for implantable biosensing.}, journal = {Nanoscale}, volume = {17}, number = {16}, pages = {9870-9894}, doi = {10.1039/d4nr04542d}, pmid = {40172544}, issn = {2040-3372}, mesh = {*Biosensing Techniques/instrumentation ; Humans ; *Electrodes, Implanted ; Animals ; Electrochemical Techniques ; }, abstract = {Flexible fibrous electrodes have emerged as a promising technology for implantable biosensing applications, offering significant advancements in the monitoring and manipulation of biological signals. This review systematically explores the key aspects of flexible fibrous electrodes, including the materials, structural designs, and fabrication methods. A detailed discussion of electrode performance metrics is provided, covering factors such as conductivity, stretchability, axial channel count, and implantation duration. The diverse applications of these electrodes in electrophysiological signal monitoring, electrochemical sensing, tissue strain monitoring, and in vivo electrical stimulation are reviewed, highlighting their potential in biomedical settings. Finally, the review discusses the eight major challenges currently faced by implantable fibrous electrodes and explores future development directions, providing critical technical analysis and potential solutions for the advancement of next-generation flexible implantable fiber-based biosensors.}, }
@article {pmid40172075, year = {2025}, author = {Wu, X and Liang, C and Bustillo, J and Kochunov, P and Wen, X and Sui, J and Jiang, R and Yang, X and Fu, Z and Zhang, D and Calhoun, VD and Qi, S}, title = {The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.}, journal = {Human brain mapping}, volume = {46}, number = {5}, pages = {e70206}, pmid = {40172075}, issn = {1097-0193}, support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; BE2023668//Jiangsu Provincial Key Research and Development Program/ ; RF1 NS114628/NS/NINDS NIH HHS/United States ; U01 MH108148/MH/NIMH NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; BK20220889//Natural Science Foundation of Jiangsu Province/ ; 62376124//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Atlases as Topic ; Male ; Female ; *Connectome/methods ; *Bipolar Disorder/diagnostic imaging/physiopathology/pathology ; *Schizophrenia/diagnostic imaging/physiopathology/pathology ; *Depressive Disorder, Major/diagnostic imaging/physiopathology ; *Mental Disorders/diagnostic imaging/physiopathology ; Autism Spectrum Disorder/diagnostic imaging/physiopathology ; Young Adult ; Nerve Net/diagnostic imaging/physiopathology ; Attention Deficit Disorder with Hyperactivity/diagnostic imaging/physiopathology ; Psychotic Disorders/diagnostic imaging/physiopathology/pathology ; Middle Aged ; Adolescent ; }, abstract = {Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.}, }
@article {pmid40171943, year = {2025}, author = {Lin, C and Zhou, X and Li, M and Zhang, C and Zhai, H and Li, H and Wang, H and Wang, X}, title = {S-ketamine Alleviates Neuroinflammation and Attenuates Lipopolysaccharide-Induced Depression Via Targeting SIRT2.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2416481}, doi = {10.1002/advs.202416481}, pmid = {40171943}, issn = {2198-3844}, support = {2021ZD0203000//STI2030-Major Projects/ ; 2021ZD0203003//STI2030-Major Projects/ ; T2341003//National Natural Science Foundation of China/ ; 22207105//National Natural Science Foundation of China/ ; 2023C038-3//Jilin Provincial Development and Reform Commission/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; }, abstract = {Depression, a pervasive mental health condition, has increasingly been linked to neuroinflammation, as evidenced by elevated levels of pro-inflammatory markers such as TNF-α and IL-1β observed in patients, which underscores the role of inflammation in its pathophysiology. This study investigates the differential effects of S-ketamine (S-KET) and R-ketamine (R-KET) on inflammation-induced depression using a lipopolysaccharide (LPS)-induced mouse model. Results showed that S-KET, but not R-KET, significantly alleviated depressive-like behaviors and reduced levels of pro-inflammatory factors in the medial prefrontal cortex (mPFC). Activity-based protein profiling identified SIRT2 as a key intracellular target of S-KET, with direct binding observed at the Q167 residue, whereas R-KET showed no such binding. S-KET enhanced SIRT2 interaction with NF-κB subunit p65, reducing its acetylation and suppressing pro-inflammatory gene expression, effects not seen with R-KET. In vitro studies with RNA interference and the SIRT2 inhibitor AK-7, along with in vivo pharmacological blockade, confirmed that SIRT2 is crucial for the anti-inflammatory and antidepressant actions of S-KET. These findings suggest that SIRT2 mediates the therapeutic effects of S-KET, highlighting its potential as a target for treating inflammation-associated depression. This study provides novel insights into the stereospecific actions of ketamine enantiomers and the promise of targeting SIRT2 for neuroinflammatory depression.}, }
@article {pmid40170996, year = {2025}, author = {Ping, A and Wang, J and Ángel García-Cabezas, M and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW}, title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.}, journal = {National science review}, volume = {12}, number = {4}, pages = {nwae473}, pmid = {40170996}, issn = {2053-714X}, support = {R01 MH121706/MH/NIMH NIH HHS/United States ; }, abstract = {The primate amygdala serves to evaluate the emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using infrared neural stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7-T functional magnetic resonance imaging, we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a 'one-to-many' (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.}, }
@article {pmid40169544, year = {2025}, author = {Zhu, M and Peng, J and Wang, M and Lin, S and Zhang, H and Zhou, Y and Dai, X and Zhao, H and Yu, YQ and Shen, L and Li, XM and Chen, J}, title = {Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {3107}, pmid = {40169544}, issn = {2041-1723}, support = {81870898//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *GABAergic Neurons/metabolism ; *Zona Incerta/metabolism ; Mice ; *Transcriptome ; Male ; Behavior, Animal/physiology ; Grooming ; Optogenetics ; Mice, Inbred C57BL ; Sleep/physiology/genetics ; Wakefulness/physiology/genetics ; Periaqueductal Gray/metabolism/cytology/physiology ; Female ; Mice, Transgenic ; }, abstract = {Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIr[Ecel1] and ZIm[Pde11a]), respectively. The GABAergic projection from ZIr[Ecel1] to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZIm[Pde11a] to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.}, }
@article {pmid40168986, year = {2025}, author = {Wang, P and Han, L and Wang, L and Tao, Q and Guo, Z and Luo, T and He, Y and Xu, Z and Yu, J and Liu, Y and Wu, Z and Xu, B and Jin, B and Wei, Y and Yang, Y and Cheng, M and Jiang, Y and Tian, C and Zheng, H and Fan, Z and Jiang, P and Gao, Y and Wu, J and Wang, S and Sun, B and Fang, Z and Lei, J and Luo, B and Wen, H and Peng, G and Tang, Y and Yang, T and Chen, J and Zhuang, Z and Su, X and Pan, C and Zhu, K and Shen, Y and Liu, S and Bao, A and Yao, J and Wang, J and Xu, X and Li, XM and Liu, L and Duan, S and Zhang, J}, title = {Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2025.03.002}, pmid = {40168986}, issn = {1097-4199}, abstract = {We employed Stereo-seq combined with single-nucleus RNA sequencing (snRNA-seq) to investigate the gene expression and cell composition changes in human hippocampus with or without Alzheimer's disease (AD). The transcriptomic map, with single-cell precision, unveiled AD-associated alterations with spatial specificity, which include the following: (1) elevated synapse pruning gene expression in the fimbria of AD, with disrupted microglia-astrocyte communication likely leading to disorganized synaptic structure; (2) a globally increased energy generation in the cornu ammonis (CA) region, with varying degrees across its subregions; (3) a significant reduction in the number of CA1 neurons in AD, while CA4 neurons remained largely unaffected, potentially due to gene alterations in CA4 conferring resilience to AD; and (4) aggravated amyloid-beta (Aβ) plaques in CA1 and stratum lucidum, radiatum, and moleculare (SLRM), and integration of Stereo-seq map with Aβ staining revealed a sequential enrichment of microglia and astrocytes around Aβ plaques. Finally, reduced brain-derived extracellular vesicles carrying cholecystokinin (CCK) and peripheral myelin protein 2 (PMP2) in AD plasma highlighted their diagnostic potential for clinical applications.}, }
@article {pmid40168808, year = {2025}, author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J}, title = {Validity of purulent vaginal discharge, esterase, luminometry, and three bacteriological tests for diagnosing uterine infection in dairy cows using Bayesian latent class analysis.}, journal = {Preventive veterinary medicine}, volume = {239}, number = {}, pages = {106521}, doi = {10.1016/j.prevetmed.2025.106521}, pmid = {40168808}, issn = {1873-1716}, abstract = {This prospective cross-sectional study aimed to evaluate the ability of laboratory bacterial culture, Petrifilm, Tri-Plate, luminometry, purulent vaginal discharge (PVD), and esterase to correctly identify uterine infection in dairy cows, and to assess these tests' usefulness in different situations. We sampled dairy cows between 29 and 43 days in milk in seven farms. We considered all six tests imperfect to identify uterine infection and used Bayesian latent class analyses to estimate their sensitivity and specificity. We created ten scenarios, including tests alone, in series, or in parallel, and we calculated predictive values and misclassification cost terms (MCTs). All estimates are presented with 95 % Bayesian credibility intervals (BCI). A total of 326 uterine samples were collected. The laboratory culture had the best validity (sensitivity = 0.87, 95 % BCI = 0.77-0.97; specificity = 0.71, 95 % BCI = 0.58-0.86). The other tests had similar specificity but lower sensitivity, with PVD having the lowest sensitivity (0.05, 95 % BCI = 0.01-0.10). If treating a healthy cow was considered worse than leaving a cow with a uterine infection untreated, luminometry yielded an MCT similar to the laboratory culture. These findings highlight that the on-farm tools currently used to identify cows that could benefit from intrauterine antimicrobial treatment do not identify uterine infection accurately. While the laboratory culture was the most accurate test, it cannot easily be implemented on farms. Luminometry's validity was good, but additional research is necessary to understand how it can be implemented to improve judicious intrauterine antimicrobial use.}, }
@article {pmid40166115, year = {2025}, author = {Yu, Z and Yang, B and Wei, P and Xu, H and Shan, Y and Fan, X and Zhang, H and Wang, C and Wang, J and Yu, S and Zhao, G}, title = {Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field.}, journal = {Fundamental research}, volume = {5}, number = {1}, pages = {103-114}, pmid = {40166115}, issn = {2667-3258}, abstract = {To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.}, }
@article {pmid40166113, year = {2025}, author = {Sun, Y and Chen, X and Liu, B and Liang, L and Wang, Y and Gao, S and Gao, X}, title = {Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.}, journal = {Fundamental research}, volume = {5}, number = {1}, pages = {3-16}, pmid = {40166113}, issn = {2667-3258}, abstract = {Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.}, }
@article {pmid40164913, year = {2025}, author = {Cheng, YA and Sanayei, M and Chen, X and Jia, K and Li, S and Fang, F and Watanabe, T and Thiele, A and Zhang, RY}, title = {A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.}, journal = {Nature human behaviour}, volume = {}, number = {}, pages = {}, pmid = {40164913}, issn = {2397-3374}, support = {3230085//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2421004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32441102//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100901//National Natural Science Foundation of China (National Science Foundation of China)/ ; R01EY019466//U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (NEI)/ ; BCS-2241417//NSF | Directorate for Social, Behavioral &amp; Economic Sciences | Division of Behavioral and Cognitive Sciences (Behavioral &amp; Cognitive Sciences)/ ; G0700976//RCUK | Medical Research Council (MRC)/ ; }, abstract = {Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.}, }
@article {pmid40164740, year = {2025}, author = {Littlejohn, KT and Cho, CJ and Liu, JR and Silva, AB and Yu, B and Anderson, VR and Kurtz-Miott, CM and Brosler, S and Kashyap, AP and Hallinan, IP and Shah, A and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF and Anumanchipalli, GK}, title = {A streaming brain-to-voice neuroprosthesis to restore naturalistic communication.}, journal = {Nature neuroscience}, volume = {28}, number = {4}, pages = {902-912}, pmid = {40164740}, issn = {1546-1726}, support = {5U01DC018671//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30DC021872//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Voice/physiology ; Adult ; Female ; Communication Devices for People with Disabilities ; *Sensorimotor Cortex/physiology ; Communication ; Paralysis/physiopathology/rehabilitation ; }, abstract = {Natural spoken communication happens instantaneously. Speech delays longer than a few seconds can disrupt the natural flow of conversation. This makes it difficult for individuals with paralysis to participate in meaningful dialogue, potentially leading to feelings of isolation and frustration. Here we used high-density surface recordings of the speech sensorimotor cortex in a clinical trial participant with severe paralysis and anarthria to drive a continuously streaming naturalistic speech synthesizer. We designed and used deep learning recurrent neural network transducer models to achieve online large-vocabulary intelligible fluent speech synthesis personalized to the participant's preinjury voice with neural decoding in 80-ms increments. Offline, the models demonstrated implicit speech detection capabilities and could continuously decode speech indefinitely, enabling uninterrupted use of the decoder and further increasing speed. Our framework also successfully generalized to other silent-speech interfaces, including single-unit recordings and electromyography. Our findings introduce a speech-neuroprosthetic paradigm to restore naturalistic spoken communication to people with paralysis.}, }
@article {pmid40163319, year = {2025}, author = {van Balen, B}, title = {Somatosensory Feedback in BCIs: Why Aiming for Naturalness Raises Ethical Concerns.}, journal = {AJOB neuroscience}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/21507740.2025.2478427}, pmid = {40163319}, issn = {2150-7759}, abstract = {Recent developments in the domain of bi-directional Brain-Computer Interface (BCI) technology are directed at generating naturalistic sensory perceptual experiences for disabled people. I argue that conceptualizing and operationalizing "naturalness" in this context has profound impact on disabled people and their experiences. I ask (1) what does it mean to have a "natural" perceptual experience and (2) should the bi-directional BCI-community strive for naturalness in this context? Inspired by phenomenological and 4E-cognition approaches to perception, I argue that the terms "natural" and "naturalness" should not be used in this context because of (1) polysemicity and (2) an implicit bias favoring able-bodied perception over disabled perception. I offer the phenomenological concept of transparency as a positive alternative to denote the underlying goal of embodiment and effortless use. I cash out methodological ramifications of my argument for research in bi-directional BCIs and plea for a transdisciplinary dialogue between end-users, phenomenologists and neuroscientists.}, }
@article {pmid40162212, year = {2025}, author = {Wang, D and Ramesh, R and Azgomi, HF and Louie, K and Balakid, J and Marks, J}, title = {At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {40162212}, issn = {2693-5015}, support = {R01 NS130183/NS/NINDS NIH HHS/United States ; }, abstract = {Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.}, }
@article {pmid40162168, year = {2025}, author = {Yang, H and Jiang, L}, title = {Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches.}, journal = {Digital health}, volume = {11}, number = {}, pages = {20552076251326123}, pmid = {40162168}, issn = {2055-2076}, abstract = {Brain-computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. In furtherance of human right protection such as mental privacy, data laws provide more detailed and enforceable rules for processing neural data which may balance the tension between privacy protection and need of the public for wellness promotion and scientific progress through data sharing. This article notes that most of the current data laws like GDPR have not covered neural data clearly, incapable of providing full protection in response to its specialty. The new legislative reforms in the U.S. states of Colorado and California made pioneering advances to incorporate neural data into data privacy laws. Yet regulatory gaps remain as such reforms have not provided special additional rules for neural data processing. Potential problems such as static consent, vague research exceptions, and loopholes in regulating non-personal neural data need to be further addressed. We recommend relevant improved measures taken through amending data laws or making special data acts.}, }
@article {pmid40161643, year = {2025}, author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ}, title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {40161643}, issn = {2692-8205}, support = {T32 DC000038/DC/NIDCD NIH HHS/United States ; }, abstract = {OBJECTIVE: There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.<br><br>APPROACH: This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.<br><br>RESULTS: In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.<br><br>SIGNIFICANCE: These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.}, }
@article {pmid40161457, year = {2025}, author = {Ren, X and Wang, Y and Li, X and Wang, X and Liu, Z and Yang, J and Wang, L and Zheng, C}, title = {Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {56}, pmid = {40161457}, issn = {1871-4080}, abstract = {Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.}, }
@article {pmid40159374, year = {2025}, author = {Phang, CR and Hirata, A}, title = {Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.}, journal = {Annals of the New York Academy of Sciences}, volume = {1546}, number = {1}, pages = {157-172}, doi = {10.1111/nyas.15322}, pmid = {40159374}, issn = {1749-6632}, support = {KAKENHI 21H04956//Japan Society for the Promotion of Science/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Deep Learning ; *Reinforcement, Psychology ; Algorithms ; Brain/physiology ; Reinforcement Machine Learning ; }, abstract = {Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given complex environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target-approaching score, lower failure rate, and lower human workload than the EEG-NB model. We also proposed a disparity d $d$ -index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors.}, }
@article {pmid40153908, year = {2025}, author = {Liu, J and Yang, X and Musmar, B and Hasan, DM}, title = {Trans-arterial approach for neural recording and stimulation: Present and future.}, journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia}, volume = {135}, number = {}, pages = {111180}, doi = {10.1016/j.jocn.2025.111180}, pmid = {40153908}, issn = {1532-2653}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted ; *Endovascular Procedures/methods ; *Brain/physiology ; Electroencephalography/methods ; }, abstract = {Neural recording and stimulation are fundamental techniques used for brain computer interfaces (BCIs). BCIs have significant potential for use in a range of brain disorders. However, for most BCIs, electrode implantation requires invasive craniotomy procedures, which have a risk of infection, hematoma, and immune responses. Such drawbacks may limit the extensive application of BCIs. There has been a rapid increase in the development of endovascular technologies and devices. Indeed, in a clinical trial, stent electrodes have been endovascularly implanted via a venous approach and provided an effective endovascular BCI to help disabled patients. Several authors have reviewed the use of endovascular recordings or endovascular BCIs. However, there is limited information on the use of trans-arterial BCIs. Herein, we reviewed the literature on the use of trans-arterial neural recording and stimulation for BCIs, and discuss their potential in terms of anatomical features, device innovations, and clinical applications. Although the use of trans-arterial recording and stimulation in the brain remains challenging, we believe it has high potential for both scientists and physicians.}, }
@article {pmid40152578, year = {2025}, author = {Li, K and Cui, Y}, title = {The Emerging Role of Astrocytes in Learning and Memory Recall.}, journal = {Journal of integrative neuroscience}, volume = {24}, number = {3}, pages = {38721}, doi = {10.31083/JIN38721}, pmid = {40152578}, issn = {0219-6352}, support = {2022ZD0211700//STI2030-Major projects/ ; 32371057//National Natural Science Foundation of China/ ; }, }
@article {pmid40151645, year = {2025}, author = {Iwama, S and Ueno, T and Fujimaki, T and Ushiba, J}, title = {Enhanced human sensorimotor integration via self-modulation of the somatosensory activity.}, journal = {iScience}, volume = {28}, number = {4}, pages = {112145}, pmid = {40151645}, issn = {2589-0042}, abstract = {Motor performance improvement through self-modulation of brain activity has been demonstrated through neurofeedback. However, the sensorimotor plasticity induced through the training remains unclear. Here, we combined individually tailored closed-loop neurofeedback, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The real-time neurofeedback of human electroencephalogram (EEG) signals enhanced participants' self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 min. Further, the short-term reorganization in S1 was corroborated by the post-training changes in somatosensory evoked potential (SEP) amplitude of the early component from S1. Meanwhile those derived from peripheral and spinal sensory fibers were maintained (N9 and N13 components), suggesting that the training manipulated S1 activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved speed-accuracy trade-off. Collectively, our results provide evidence that neurofeedback training induces functional reorganization of S1 and sensorimotor function.}, }
@article {pmid40149743, year = {2025}, author = {Zheng, J and Li, Y and Chen, L and Wang, F and Gu, B and Sun, Q and Gao, X and Zhou, F}, title = {Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission.}, journal = {Brain sciences}, volume = {15}, number = {3}, pages = {}, pmid = {40149743}, issn = {2076-3425}, support = {2021ZD0200405//National Key R&amp;D Program of China/ ; 2024M752811//China Postdoctoral Science Foundation under Grant Number/ ; 202204A09//Key Agricultural and Social Development Projects of Hangzhou/ ; }, abstract = {BACKGROUND: In brain-computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as "packet loss". Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing.<br><br>METHODS: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios.<br><br>RESULTS: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes.<br><br>CONCLUSIONS: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss.}, }
@article {pmid40149575, year = {2025}, author = {Calderone, A and Manuli, A and Arcadi, FA and Militi, A and Cammaroto, S and Maggio, MG and Pizzocaro, S and Quartarone, A and De Nunzio, AM and Calabrò, RS}, title = {The Impact of Visualization on Stroke Rehabilitation in Adults: A Systematic Review of Randomized Controlled Trials on Guided and Motor Imagery.}, journal = {Biomedicines}, volume = {13}, number = {3}, pages = {}, pmid = {40149575}, issn = {2227-9059}, abstract = {Background/Objectives: Guided imagery techniques, which include mentally picturing motions or activities to help motor recovery, are an important part of neuroplasticity-based motor therapy in stroke patients. Motor imagery (MI) is a kind of guided imagery in neurorehabilitation that focuses on mentally rehearsing certain motor actions in order to improve performance. This systematic review aims to evaluate the current evidence on guided imagery techniques and identify their therapeutic potential in stroke motor rehabilitation. Methods: Randomized controlled trials (RCTs) published in the English language were identified from an online search of PubMed, Web of Science, Embase, EBSCOhost, and Scopus databases without a specific search time frame. The inclusion criteria take into account guided imagery interventions and evaluate their impact on motor recovery through validated clinical, neurophysiological, or functional assessments. This review has been registered on Open OSF with the following number: DOI 10.17605/OSF.IO/3D7MF. Results: This review synthesized 41 RCTs on MI in stroke rehabilitation, with 996 participants in the intervention group and 757 in the control group (average age 50-70, 35% female). MI showed advantages for gait, balance, and upper limb function; however, the RoB 2 evaluation revealed 'some concerns' related to allocation concealment, blinding, and selective reporting issues. Integrating MI with gait training or action observation (AO) seems to improve motor recovery, especially in balance and walking. Technological methods like brain-computer interfaces (BCIs) and hybrid models that combine MI with circuit training hold potential for enhancing functional mobility and motor results. Conclusions: Guided imagery shows promise as a beneficial adjunct in stroke rehabilitation, with the potential to improve motor recovery across several domains such as gait, upper limb function, and balance.}, }
@article {pmid40148520, year = {2025}, author = {Li, J and Shi, J and Yu, P and Yan, X and Lin, Y}, title = {Feature-aware domain invariant representation learning for EEG motor imagery decoding.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {10664}, pmid = {40148520}, issn = {2045-2322}, support = {2023QN06002//Inner Mongolia Natural Science Foundation project of China/ ; JJ230408//Inner Mongolia University high-level talent research project/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; Machine Learning ; Movement/physiology ; }, abstract = {Electroencephalography (EEG)-based motor imagery (MI) is extensively utilized in clinical rehabilitation and virtual reality-based movement control. Decoding EEG-based MI signals is challenging because of the inherent spatio-temporal variability of the original signal representation, coupled with a low signal-to-noise ratio (SNR), which impedes the extraction of clean and robust features. To address this issue, we propose a multi-scale spatio-temporal domain-invariant representation learning method, termed MSDI. By decomposing the original signal into spatial and temporal components, the proposed method extracts invariant features at multiple scales from both components. To further constrain the representation to invariant domains, we introduce a feature-aware shift operation that resamples the representation based on its feature statistics and feature measure, thereby projecting the features into a domain-invariant space. We evaluate our proposed method via two publicly available datasets, BNCI2014-001 and BNCI2014-004, demonstrating state-of-the-art performance on both datasets. Furthermore, our method exhibits superior time efficiency and noise resistance.}, }
@article {pmid40147158, year = {2025}, author = {Tang, J and Xi, X and Wang, T and Li, L and Yang, J}, title = {Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network.}, journal = {Computer methods and programs in biomedicine}, volume = {265}, number = {}, pages = {108735}, doi = {10.1016/j.cmpb.2025.108735}, pmid = {40147158}, issn = {1872-7565}, mesh = {Humans ; Electromyography ; *Muscle, Skeletal/physiology/physiopathology ; Male ; *Electric Stimulation ; Female ; Middle Aged ; Stroke/physiopathology ; *Motor Cortex/physiopathology ; *Electric Stimulation Therapy ; Adult ; Stroke Rehabilitation ; *Cerebral Cortex ; Nerve Net ; }, abstract = {BACKGROUND AND OBJECTIVE: Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.<br><br>METHODS: In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.<br><br>RESULTS: The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p < 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the &#x3b3; frequency band (30-45 Hz), with marked improvements in the clustering coefficient and shortest path length (p < 0.001).<br><br>CONCLUSIONS: As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.}, }
@article {pmid40145943, year = {2025}, author = {Jilderda, MF and Zhang, Y and Rebattu, V and Salunga, R and Mesker, W and Wong, J and de Munck, L and Fornander, T and Nordenskjöld, B and Stål, O and Anderson, AKL and Bastiaannet, E and Treuner, K and Liefers, GJ}, title = {Identification of Early-Stage Breast Cancer with Minimal Risk of Recurrence by Breast Cancer Index.}, journal = {Clinical cancer research : an official journal of the American Association for Cancer Research}, volume = {}, number = {}, pages = {}, doi = {10.1158/1078-0432.CCR-24-3836}, pmid = {40145943}, issn = {1557-3265}, abstract = {PURPOSE: This study assessed the prognostic ability of Breast Cancer Index (BCI) to identify patients at minimal risk (<5%) of 10-year distant recurrence (DR) who are unlikely to benefit from adjuvant endocrine therapy.<br><br>EXPERIMENTAL DESIGN: This prospective translational study included postmenopausal patients with early-stage, HR+ N0 breast cancer from the Stockholm (STO-3) trial who underwent surgery alone ("untreated") or surgery plus adjuvant tamoxifen ("treated") and the Netherlands Cancer Registry (NCR; surgery alone). The primary endpoint was time to DR. An adjusted BCI model with an additional cut-point was developed that stratified patients into 4 prognostic risk groups.<br><br>RESULTS: Across cohorts, 16%-22% of patients were classified as minimal risk of 10-year DR. In the Stockholm untreated cohort (n = 283), risks in the minimal, low, intermediate, and high risk groups were 2.3%, 15.5% (hazard ratio, 4.71 [95% CI, 1.09-20.29] versus minimal risk), 19.8% (6.97 [1.61-30.18]), and 35.9% (13.21 [3.07-56.76]), respectively (P < .001). In the Stockholm treated cohort (n = 317), risks were 4.3%, 5.0% (1.16 [0.35-3.85]), 11.7% (2.45 [0.74-8.14]), and 21.1% (5.27 [1.72-16.16]; P < .001). In the NCR cohort (n = 1245), risks were 4.5%, 7.5% (sub-distribution hazard ratio, 1.67 [95% CI, 0.81-3.45]), 10.3% (2.40 [1.14-5.03]), and 13.1% (3.13 [1.50-6.55]; P = .005). BCI risk scores provided additional independent information over standard prognostic factors (likelihood ratio, c2 = 7.98; P = .004).<br><br>CONCLUSIONS: The adjusted BCI model identified women with early-stage, HR+ N0 breast cancer at minimal risk of DR who may consider de-escalating adjuvant endocrine therapy.}, }
@article {pmid40144587, year = {2025}, author = {Marzulli, M and Bleuzé, A and Saad, J and Martel, F and Ciuciu, P and Aksenova, T and Struber, L}, title = {Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1521491}, pmid = {40144587}, issn = {1662-5161}, abstract = {INTRODUCTION: Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.<br><br>METHODS: This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.<br><br>RESULTS: The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.<br><br>DISCUSSION: These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.}, }
@article {pmid40144585, year = {2025}, author = {Saad, J and Evans, A and Jaoui, I and Roux-Sibillon, V and Hardy, E and Anghel, L}, title = {Comparison metrics and power trade-offs for BCI motor decoding circuit design.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1547074}, pmid = {40144585}, issn = {1662-5161}, abstract = {Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.}, }
@article {pmid40143846, year = {2025}, author = {Andronache, C and Curǎvale, D and Nicolae, IE and Neacşu, AA and Nicolae, G and Ivanovici, M}, title = {Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1487175}, pmid = {40143846}, issn = {1662-4548}, abstract = {In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.}, }
@article {pmid40141951, year = {2025}, author = {Aydin, S and Melek, M and Gökrem, L}, title = {A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.}, journal = {Micromachines}, volume = {16}, number = {3}, pages = {}, pmid = {40141951}, issn = {2072-666X}, support = {2023/86//Tokat Gaziosmanpa&#x15f;a &#xdc;niversitesi/ ; }, abstract = {Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.}, }
@article {pmid40140571, year = {2025}, author = {Gazit Shimoni, N and Tose, AJ and Seng, C and Jin, Y and Lukacsovich, T and Yang, H and Verharen, JPH and Liu, C and Tanios, M and Hu, E and Read, J and Tang, LW and Lim, BK and Tian, L and Földy, C and Lammel, S}, title = {Changes in neurotensin signalling drive hedonic devaluation in obesity.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {40140571}, issn = {1476-4687}, abstract = {Calorie-rich foods, particularly those that are high in fat and sugar, evoke pleasure in both humans and animals[1]. However, prolonged consumption of such foods may reduce their hedonic value, potentially contributing to obesity[2-4]. Here we investigated this phenomenon in mice on a chronic high-fat diet (HFD). Although these mice preferred high-fat food over regular chow in their home cages, they showed reduced interest in calorie-rich foods in a no-effort setting. This paradoxical decrease in hedonic feeding has been reported previously[3-7], but its neurobiological basis remains unclear. We found that in mice on regular diet, neurons in the lateral nucleus accumbens (NAcLat) projecting to the ventral tegmental area (VTA) encoded hedonic feeding behaviours. In HFD mice, this behaviour was reduced and uncoupled from neural activity. Optogenetic stimulation of the NAcLat→VTA pathway increased hedonic feeding in mice on regular diet but not in HFD mice, though this behaviour was restored when HFD mice returned to a regular diet. HFD mice exhibited reduced neurotensin expression and release in the NAcLat→VTA pathway. Furthermore, neurotensin knockout in the NAcLat and neurotensin receptor blockade in the VTA each abolished optogenetically induced hedonic feeding behaviour. Enhancing neurotensin signalling via overexpression normalized aspects of diet-induced obesity, including weight gain and hedonic feeding. Together, our findings identify a neural circuit mechanism that links the devaluation of hedonic foods with obesity.}, }
@article {pmid40139011, year = {2025}, author = {Luo, C and Zhu, X and Zhang, Y and Wen, Y and Wan, L and Qian, Z}, title = {Competitive electrochemical immunosensor for trace phosphorylated Tau181 analysis in plasma: Toward point-of-care technologies of Alzheimer's disease.}, journal = {Talanta}, volume = {292}, number = {}, pages = {128009}, doi = {10.1016/j.talanta.2025.128009}, pmid = {40139011}, issn = {1873-3573}, mesh = {*tau Proteins/blood/immunology ; *Alzheimer Disease/blood/diagnosis ; Humans ; Phosphorylation ; *Electrochemical Techniques/methods ; Immunoassay/methods ; *Biosensing Techniques/methods ; *Point-of-Care Systems ; Electrodes ; Limit of Detection ; Biomarkers/blood ; Horseradish Peroxidase/chemistry ; }, abstract = {Accurate detection of core Alzheimer's disease (AD) biomarkers in biofluids is crucial for identifying preclinical AD and predicting disease progression. Phosphorylated tau 181 (p-tau181), a key biomarker, holds promise for early diagnosis. This work presents a sensitive and rapid electrochemical immunosensor (EC-iSensor) based on screen-printed electrodes (SPEs) for p-tau181 quantification. Employing a competitive immunoassay format, the EC-iSensor utilizes biotinylated p-tau181 as a competitor against the target analyte for binding to immobilized capture antibodies. Signal transduction is achieved via horseradish peroxidase (HRP) and tetramethylbenzidine (TMB) substrate. The EC-iSensor exhibits a low detection limit of 1.91 fg/mL and a wide dynamic range spanning 6.97 fg/mL to 100 ng/mL in PBS. Furthermore, successful detection of p-tau181 in blood samples from AD patients demonstrated its practical applicability. This cost-effective SPE-based EC-iSensor offers a simple and highly sensitive platform for p-tau181 detection, presenting potential for point-of-care technologies (POCT) of AD.}, }
@article {pmid40138736, year = {2025}, author = {Yang, W and Wang, X and Qi, W and Wang, W}, title = {LGFormer: integrating local and global representations for EEG decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc5a3}, pmid = {40138736}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain/physiology ; Brain-Computer Interfaces ; }, abstract = {Objective.Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.Approach.In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.Main results.LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.Significance.In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.}, }
@article {pmid40136841, year = {2025}, author = {Mohamed, AK and Aharonson, V}, title = {Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {3}, pages = {}, pmid = {40136841}, issn = {2313-7673}, support = {99207 and 117965//National Research Foundation of South Africa/ ; }, abstract = {Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.}, }
@article {pmid40136836, year = {2025}, author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Sauter-Starace, F and Koprinkova-Hristova, P and Kasabov, N}, title = {Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {3}, pages = {}, pmid = {40136836}, issn = {2313-7673}, support = {101070891/01.10.2022//European Commission under the HORIZON-EIC action/ ; }, abstract = {Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.}, }
@article {pmid40136825, year = {2025}, author = {Li, H and Wang, Y and Fu, P}, title = {A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {3}, pages = {}, pmid = {40136825}, issn = {2313-7673}, support = {2024JC-YBON-0659//Natural Science Basic Research Program of Shaanxi Province/ ; TC2023JC16//Basic Research Programs of Taicang/ ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.}, }
@article {pmid40135785, year = {2025}, author = {Chen, J and Liu, B and Peng, G and Zhou, L and Tan, C and Qin, J and Li, J and Hong, Z and Wu, Y and Lu, M and Cai, F and Huang, Y}, title = {Achieving High-Performance Transcranial Ultrasound Transmission Through Mie and Fano Resonance in Flexible Metamaterials.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2500170}, doi = {10.1002/advs.202500170}, pmid = {40135785}, issn = {2198-3844}, support = {2021YFB3801802//National Key R&amp;D Program of China/ ; 2022YFB3204300//National Key R&amp;D Program of China/ ; 226-2024-00120//Fundamental Research Funds for the Central Universities/ ; 2023R5231//Special Support Plan for High Level Talents in Zhejiang Province/ ; }, abstract = {Transcranial ultrasound holds great potential in medical applications. However, the effective transmission of ultrasound through the skull remains challenging due to the acoustic impedance mismatch, as well as the non-uniform thickness, and the curved surface. To overcome these challenges, this work introduces an innovative Mie-resonance flexible metamaterial (MRFM), which consists of periodically arranged low-speed micropillars embedded within a high-speed flexible substrate. The MRFM generates Mie-resonance, which couples with the skull to form Fano resonance, thereby enhancing ultrasound transmittance through the skull. Simulation results demonstrate that the proposed resonance solution significantly increases transcranial ultrasound transmittance from 33.7% to 75.2% at 0.309 MHz. For the fabrication of the MRFM, porous nickel foam is used as the Mie micropillars, and agarose hydrogel serves as the flexible substrate. Experimental results demonstrate enhanced ultrasound transmittance from 20.6% to 73.3% at 0.33 MHz with the MRFM, which shows good agreement with the simulation results, further validating the effectiveness of the design. The simplicity, tunability, and flexibility of the MRFM represent a significant breakthrough, addressing the limitations of conventional rigid metamaterials. This work lays a solid theoretical and experimental foundation for advancing the clinical application of transcranial ultrasound stimulation and neuromodulation.}, }
@article {pmid40134759, year = {2025}, author = {Sakel, M and Ozolins, CA and Saunders, K and Biswas, R}, title = {A home-based EEG neurofeedback treatment for chronic neuropathic pain-a pilot study.}, journal = {Frontiers in pain research (Lausanne, Switzerland)}, volume = {6}, number = {}, pages = {1479914}, pmid = {40134759}, issn = {2673-561X}, abstract = {OBJECTIVE: This study assessed the effect of an 8-week home-based neurofeedback intervention in chronic neuropathic pain patients.<br><br>SUBJECTS/PATIENTS: A cohort of eleven individuals with chronic neuropathic pain receiving treatment within the NHS framework.<br><br>METHODS: Participants were trained to operate a home-based neurofeedback system. Each received a portable Axon system for one week of electroencephalogram (EEG) baselines, followed by an 8-week neurofeedback intervention, and subsequent 12 weeks of follow-up EEG baselines. Primary outcome measures included changes in the Brief Pain Inventory and Visual Analogue Pain Scale at post-intervention, and follow-ups compared with the baseline. Secondary outcomes included changes in depression, anxiety, stress, pain catastrophizing, central sensitization, sleep quality, and quality of life. EEG activities were monitored throughout the trial.<br><br>RESULTS: Significant improvements were noted in pain scores, with all participants experiencing overall pain reduction. Clinically significant pain improvement (&#x2265;30%) was reported by 5 participants (56%). Mood scores showed a significant decrease in depression (p&#x2009;<&#x2009;0.05), and pain catastrophizing (p&#x2009;<&#x2009;0.05) scores improved significantly at post-intervention, with continued improvement at the first-month follow-up.<br><br>CONCLUSION: The findings indicate that an 8-week home-based neurofeedback intervention improved pain and psychological well-being in this sample of chronic neuropathic pain patients. A randomized controlled trial is required to replicate these results in a larger cohort. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT05464199, identifier: (NCT05464199).}, }
@article {pmid40133571, year = {2025}, author = {Xu, X and Sha, L and Basang, S and Peng, A and Zhou, X and Liu, Y and Li, Y and Chen, L}, title = {Mortality in patients with epilepsy: a systematic review.}, journal = {Journal of neurology}, volume = {272}, number = {4}, pages = {291}, pmid = {40133571}, issn = {1432-1459}, support = {Grant Recipient//Science and Technology Major Project of Sichuan Province/ ; }, mesh = {Humans ; *Epilepsy/mortality/epidemiology ; Sudden Unexpected Death in Epilepsy/epidemiology ; }, abstract = {BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.<br><br>METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.<br><br>RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.<br><br>CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.}, }
@article {pmid40131365, year = {2025}, author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM}, title = {Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X.}, journal = {JMIR formative research}, volume = {}, number = {}, pages = {}, doi = {10.2196/60859}, pmid = {40131365}, issn = {2561-326X}, abstract = {BACKGROUND: Given the recent evolution and achievements in Brain-Computer interface (BCI) technologies, understanding public perception and sentiments towards such novel technologies is important for guiding their communication strategies in marketing and education.<br><br>OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (Twitter), utilizing Natural Language Processing (NLP) methods.<br><br>METHODS: A mixed-methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,926 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We utilized the Sentiment.ai tool to infer users' demographics by matching pre-defined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.<br><br>RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% of posts were neutral, 32.75% were positive, and 7.85% were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic = 0.266, tau = 0.266, P<.001). Most posts were objective (77.81%), with a smaller proportion being subjective (22.02%). Biographic analysis showed that the 'Broadcasting' group contributed the most to BCI discussions (30.67%), but the 'Scientific' group, which contributed 27.58% of the discussions, had the highest overall engagement metrics. Emotional analysis identified anticipation (20.56%), trust (17.59%), and fear (13.98%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.<br><br>CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy-making, and communication strategies.}, }
@article {pmid40129720, year = {2025}, author = {Daly, I and Matran-Fernandez, A and Lebedev, MA and Kübler, A and Valeriani, D}, title = {Editorial: Datasets for brain-computer interface applications, volume II.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1569216}, pmid = {40129720}, issn = {1662-4548}, }
@article {pmid40129499, year = {2025}, author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S}, title = {How do voice acoustics affect the perceived trustworthiness of a speaker? A systematic review.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1495456}, pmid = {40129499}, issn = {1664-1078}, abstract = {Trust is a multidimensional and dynamic social and cognitive construct, considered the glue of society. Gauging someone's perceived trustworthiness is essential for forming and maintaining healthy relationships across various domains. Humans have become adept at inferring such traits from speech for survival and sustainability. This skill has extended to the technological space, giving rise to humanlike voice technologies. The inclination to assign personality traits to these technologies suggests that machines may be processed along similar social and vocal dimensions as human voices. Given the increasing prevalence of voice technology in everyday tasks, this systematic review examines the factors in the psychology of voice acoustics that influence listeners' trustworthiness perception of speakers, be they human or machine. Overall, this systematic review has revealed that voice acoustics impact perceptions of trustworthiness in both humans and machines. Specifically, combining multiple acoustic features through multivariate methods enhances interpretability and yields more balanced findings compared to univariate approaches. Focusing solely on isolated features like pitch often yields inconclusive results when viewed collectively across studies without considering other factors. Crucially, situational, or contextual factors should be utilised for enhanced interpretation as they tend to offer more balanced findings across studies. Moreover, this review has highlighted the significance of cross-examining speaker-listener demographic diversity, such as ethnicity and age groups; yet, the scarcity of such efforts accentuates the need for increased attention in this area. Lastly, future work should involve listeners' own trust predispositions and personality traits with ratings of trustworthiness perceptions.}, }
@article {pmid40128831, year = {2025}, author = {Liu, XY and Wang, WL and Liu, M and Chen, MY and Pereira, T and Doda, DY and Ke, YF and Wang, SY and Wen, D and Tong, XG and Li, WG and Yang, Y and Han, XD and Sun, YL and Song, X and Hao, CY and Zhang, ZH and Liu, XY and Li, CY and Peng, R and Song, XX and Yasi, A and Pang, MJ and Zhang, K and He, RN and Wu, L and Chen, SG and Chen, WJ and Chao, YG and Hu, CG and Zhang, H and Zhou, M and Wang, K and Liu, PF and Chen, C and Geng, XY and Qin, Y and Gao, DR and Song, EM and Cheng, LL and Chen, X and Ming, D}, title = {Recent applications of EEG-based brain-computer-interface in the medical field.}, journal = {Military Medical Research}, volume = {12}, number = {1}, pages = {14}, pmid = {40128831}, issn = {2054-9369}, support = {2021YFF1200602//The National Key R&amp;D Program of China/ ; 0401260011//The National Science Fund for Excellent Overseas Scholars/ ; c02022088//The National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 82472098//National Natural Science Foundation of China (General Program)/ ; 82202798//The National Natural Science Foundation of China/ ; 22YF1404200//The Shanghai Sailing Program/ ; }, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electroencephalography/methods/trends/instrumentation ; Epilepsy/physiopathology ; }, abstract = {Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.}, }
@article {pmid40127544, year = {2025}, author = {Davis, KC and Wyse-Sookoo, KR and Raza, F and Meschede-Krasa, B and Prins, NW and Fisher, L and Brown, EN and Cajigas, I and Ivan, ME and Jagid, JR and Prasad, A}, title = {5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc48c}, pmid = {40127544}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electrocorticography/methods/instrumentation/trends ; *Electrodes, Implanted/trends ; Follow-Up Studies ; *Spinal Cord Injuries/rehabilitation/physiopathology/diagnosis ; }, abstract = {Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.Approach.The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).Main results.The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.Significance.This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.}, }
@article {pmid40127541, year = {2025}, author = {Marissens Cueva, V and Bougrain, L and Lotte, F and Rimbert, S}, title = {Reliable predictor of BCI motor imagery performance using median nerve stimulation.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc48d}, pmid = {40127541}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Female ; Adult ; *Median Nerve/physiology ; Young Adult ; *Electroencephalography/methods ; Reproducibility of Results ; Psychomotor Performance/physiology ; Electric Stimulation/methods ; Movement/physiology ; }, abstract = {Objective.Predicting performance in brain-computer interfaces (BCIs) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.Approach.This study explores the use of median nerve stimulation (MNS) as a predictor of motor imagery (MI)-BCI performance. MNS induces event related (de)synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.Main results.Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71,p<0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0,p<0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.Significance.By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.}, }
@article {pmid40127535, year = {2025}, author = {Ofer, A and Ophir, A and Yoav, N and Roman, R and Oren, S}, title = {Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc48e}, pmid = {40127535}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagination/physiology ; Signal-To-Noise Ratio ; Algorithms ; Adult ; Male ; Autoencoder ; }, abstract = {Objective.Non-stationarity in electroencephalogram (EEG) signals poses significant challenges for the performance and implementation of brain-computer interfaces (BCIs).Approach.In this study, we propose a novel method for cross-session BCI tasks that employs a supervised autoencoder to reduce session-specific information while preserving task-related signals. Our approach compresses high-dimensional EEG inputs and reconstructs them, thereby mitigating non-stationary variability in the data. In addition to unsupervised minimization of the reconstruction error, the objective function of the network includes two supervised terms to ensure that the latent representations exclude session identity information and are optimized for subsequent classification.Main results.Evaluation across three different motor imagery datasets demonstrates that our approach effectively addresses domain adaptation challenges, outperforming both naïve cross-session and within-session methods.Significance.Our method eliminates the need for data from new sessions, making it fully unsupervised concerning new session data and reducing the necessity for recalibration with each session. Furthermore, the reduction of session-specific information in the reconstructed signals indicates that our approach effectively denoises non-stationary signals, thereby enhancing the accuracy of BCI models. Future applications could extend this model to a broader range of BCI tasks and explore the residual signals to investigate sources of non-stationary brain components and other cognitive processes.}, }
@article {pmid40125566, year = {2025}, author = {Hong, W and Mao, L and Lin, K and Huang, C and Su, Y and Zhang, S and Wang, C and Wang, D and Song, J and Chen, Z}, title = {Accurate and Noninvasive Dysphagia Assessment via a Soft High-Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2500472}, doi = {10.1002/advs.202500472}, pmid = {40125566}, issn = {2198-3844}, support = {12402202//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 2021C03050//Zhejiang Key Research and Development Program of China/ ; }, abstract = {Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large-area high-density sEMG (HD-sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high-fidelity HD-sEMG signals during swallowing. The electrode array features a 64-channel soft on-skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD-sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD-sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.}, }
@article {pmid40124055, year = {2025}, author = {Lee, H and Lee, S and Lee, S and Lee, J and Chou, N and Shin, H}, title = {A Highly Efficient Low-Cost Flexible Neural Probe for Scalable Mass Fabrication.}, journal = {ACS omega}, volume = {10}, number = {10}, pages = {10733-10740}, pmid = {40124055}, issn = {2470-1343}, abstract = {Neural probes capable of the precise recording and control of brain signals are essential tools for brain-computer interfaces and neuroscience research. However, conventional neural probes have not been widely adopted due to the high costs associated with semiconductor fabrication and complex packaging procedures. Herein, we present a breakthrough in this area in the form of a highly efficient flexible neural probe with a production cost of only 1.5 dollars per unit that can be mass-produced (1000 units within 3 days). The probe design is based on a standardized flexible printed circuit board (PCB) process that ensures large-scale producibility and minimizes device performance variation. The device features four independent neural probes that enable flexible targeting of multiple brain regions and a reusable interface PCB that minimizes packaging complexity. The neural signal recording performance of the fabricated probe is comparable to that of traditional silicon-based probes and is scalable with eight electrodes capable of simultaneous measurements. We anticipate that our innovative device will significantly improve the accessibility of neuroscience research.}, }
@article {pmid40122923, year = {2025}, author = {Yang, B and Rong, F and Xie, Y and Li, D and Zhang, J and Li, F and Shi, G and Gao, X}, title = {A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {488}, pmid = {40122923}, issn = {2052-4463}, support = {62376149//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Male ; Adult ; }, abstract = {A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.}, }
@article {pmid40122080, year = {2025}, author = {Branco, MP and Verberne, MSW and van Balen, BJ and Bekius, A and Leinders, S and Ketelaar, M and Geytenbeek, J and van Driel-Boerrigter, M and Willems-Op Het Veld, M and Rabbie-Baauw, K and Vansteensel, MJ}, title = {Stakeholder's perspective on brain-computer interfaces for children and young adults with cerebral palsy.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/17483107.2025.2481426}, pmid = {40122080}, issn = {1748-3115}, abstract = {Communication Brain-Computer Interfaces (cBCIs) are a promising tool for people with motor and speech impairment, in particular for children and young adults with communication impairments, for example due to cerebral palsy (CP). Here we aimed to create a solid basis for the user-centered design of cBCIs for children and young adults with severe CP by investigating the perspectives of their parents/caregivers and health care professionals on communication and cBCIs. We conducted an online survey on 1) current communication problems and usability of used aids, 2) interest in cBCIs, and 3) preference for specific types of cBCIs. A total of 19 parents/caregivers and 36 health care professionals who interacted directly with children and young adults (8-25 years old) with severe CP, corresponding to Gross Motor Function Classification System level IV or V, participated. Both groups of respondents indicated that motor impairment occurred the most frequently and had the greatest impact on communication. The currently used communication aids included mainly no/low-tech aids and high-tech aids. The majority of health care professionals and parents/caregivers reported an interest in cBCIs, with a slight preference for implanted electrodes over non-implanted ones, and no preference for either of the two proposed mental BCI control strategies. Results indicate that cBCIs should be considered for a subpopulation of children and young adults with severe CP, and that in the development of cBCIs for this group both visual stimuli and sensorimotor rhythms, as well as the use of implanted electrodes, should be considered.}, }
@article {pmid40121857, year = {2025}, author = {Zhou, H and Qiao, K and Rao, L and Zhai, HJ}, title = {Nanosilica cross-linked polyurethane hybrid hydrogels to stabilize the silicone rubber based invasive electrode-neural tissue interface.}, journal = {Colloids and surfaces. B, Biointerfaces}, volume = {251}, number = {}, pages = {114643}, doi = {10.1016/j.colsurfb.2025.114643}, pmid = {40121857}, issn = {1873-4367}, mesh = {*Hydrogels/chemistry/pharmacology ; Animals ; Rats ; PC12 Cells ; *Polyurethanes/chemistry ; *Silicon Dioxide/chemistry ; *Silicone Elastomers/chemistry ; Electrodes ; *Cross-Linking Reagents/chemistry ; Surface Properties ; Cell Adhesion/drug effects ; Particle Size ; }, abstract = {An unstable electrode-neural tissue interface induced by tissue inflammatory response hinders the application of invasive brain-computer interfaces (BCIs). In this work, nanosilica cross-linked polyurethane (SiO2/PU) hybrid hydrogels were prepared to serve as the coatings and to modify silicone rubber (SR), which is a commonly used encapsulation material of invasive electrodes for neural recording/stimulation. The hydrophilicity, swelling ratio, and bulk ionic conductivity of SiO2/PU hybrid hydrogels were tailored by incorporating different amount of SiO2 serving as the cross-linking agent. Correspondingly, the optimized SiO2/PU hybrid hydrogel coatings have less impact on the electrochemical properties of invasive electrodes relative to PU hydrogel. Cell affinity assays with rat pheochromocytoma cells reveal that coatings made of SiO2/PU hybrid hydrogels are more effective in enhancing their adhesion and neurite outgrowth than those made of PU hydrogels. The adsorption amount of non-specific proteins on SR is significantly reduced by 81.6 % and 92.6 % upon coating with PU hydrogels and SiO2/PU hybrid hydrogels, respectively. Histological assessment indicates that the SR implants with a SiO2/PU hybrid hydrogel coating provoke the mildest tissue response. Collectively, the SiO2/PU hybrid hydrogel is highly promising for the stabilization of electrode-neural tissue interface, which is crucial for the development of invasive BCIs.}, }
@article {pmid40119207, year = {2025}, author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X}, title = {Author Correction: Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.}, journal = {Nature neuroscience}, volume = {28}, number = {4}, pages = {913}, doi = {10.1038/s41593-025-01941-2}, pmid = {40119207}, issn = {1546-1726}, }
@article {pmid40118966, year = {2025}, author = {Hussain, SAH and Raza, I and Hussain, SA and Jamal, MH and Gulrez, T and Zia, A}, title = {A mental state aware brain computer interface for adaptive control of electric powered wheelchair.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {9880}, pmid = {40118966}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Humans ; *Wheelchairs ; *Electroencephalography ; Male ; Machine Learning ; Persons with Disabilities/psychology ; Adult ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively adjusts to the psychological effects of the patient could provide the foundation for refining BCI applications. This paper focuses on the collection and realization of human electroencephalogram (EEG) signals data, obtained as a response to different psychological effects of sound stimuli. Filtration and pre-processing of the data set are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26%. In addition, an automated BCI system is developed to control an electric wheelchair (EPW) while responding to the mental state of the user with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and responsive to emotions for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 s to generate the interpretable brain signal from the user.}, }
@article {pmid40118477, year = {2025}, author = {Ren, Y and Kang, YN and Cao, SY and Meng, F and Zhang, J and Liao, R and Li, X and Chen, Y and Wen, Y and Wu, J and Xia, W and Xu, L and Wen, S and Liu, H and Li, Y and Gu, J and Lv, Q}, title = {Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China.}, journal = {BMJ open}, volume = {15}, number = {3}, pages = {e097528}, pmid = {40118477}, issn = {2044-6055}, mesh = {Humans ; Cross-Sectional Studies ; *Spondylitis, Ankylosing ; China ; Male ; Single-Blind Method ; Female ; Adult ; Middle Aged ; Patient Education as Topic/methods ; Language ; Spondylarthritis ; Health Knowledge, Attitudes, Practice ; }, abstract = {OBJECTIVES: To evaluate the potential of large language models (LLMs) in health education for patients with ankylosing spondylitis (AS)/spondyloarthritis (SpA), focusing on the accuracy of information transmission, patient acceptance and performance differences between different models.<br><br>DESIGN: Cross-sectional, single-blind study.<br><br>SETTING: Multiple centres in China.<br><br>PARTICIPANTS: 182 volunteers, including 4 rheumatologists and 178 patients with AS/SpA.<br><br>Scientificity, precision and accessibility of the content of the answers provided by LLMs; patient acceptance of the answers.<br><br>RESULTS: LLMs performed well in terms of scientificity, precision and accessibility, with ChatGPT-4o and Kimi models outperforming traditional guidelines. Most patients with AS/SpA showed a higher level of understanding and acceptance of the responses from LLMs.<br><br>CONCLUSIONS: LLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.}, }
@article {pmid40117671, year = {2025}, author = {Revechkis, B and Aflalo, TN and Pouratian, N and Rosario, E and Ouellette, DS and Zhang, C and Pejsa, K}, title = {Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc3ca}, pmid = {40117671}, issn = {1741-2552}, mesh = {Humans ; *Parietal Lobe/physiology ; *Virtual Reality ; *Movement/physiology ; *Neurons/physiology ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology/rehabilitation ; Adult ; Online Systems ; Psychomotor Performance/physiology ; }, abstract = {Objective. Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex (PPC) to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex.Approach. A quadriplegic human subject was implanted with multi-electrode recording arrays in the PPC. Neural spiking and local field potentials were recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person movement generation task in addition to closed loop control performance.Main results. We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and 'attached' 3D arm relative to either a visually similar but 'detached' 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors.Significance. In human parietal cortex, single neuron activity and local field potentials responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, this tuning did not affect closed loop brain control in a virtual reality environment when preceded by a text-based decoder training paradigm.}, }
@article {pmid40117159, year = {2025}, author = {Xia, Y and Chen, J and Li, J and Gong, T and Vidal-Rosas, EE and Loureiro, R and Cooper, RJ and Zhao, H}, title = {A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1220-1230}, doi = {10.1109/TNSRE.2025.3553794}, pmid = {40117159}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Spectroscopy, Near-Infrared/methods ; *Neurofeedback/methods ; Male ; *Deep Learning ; *Algorithms ; *Tomography, Optical/methods ; Artifacts ; Female ; Adult ; Calibration ; Young Adult ; Computer Systems ; Motion ; }, abstract = {Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.}, }
@article {pmid40115973, year = {2025}, author = {Chen, W and Chen, H and Ruan, H and Jiang, W and Chen, C and Xu, M and Xu, Y and Chen, H and Yu, Z and Chen, S}, title = {Identification of Adolescents With Major Depressive Disorder Using Random Forest Based on Nocturnal Heart Rate Variability.}, journal = {Psychophysiology}, volume = {62}, number = {3}, pages = {e70049}, doi = {10.1111/psyp.70049}, pmid = {40115973}, issn = {1469-8986}, support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; }, mesh = {Humans ; *Heart Rate/physiology ; *Depressive Disorder, Major/physiopathology/diagnosis ; Adolescent ; Male ; Female ; *Electrocardiography ; Bayes Theorem ; Polysomnography ; Random Forest ; }, abstract = {Major depressive disorder (MDD) in adolescents is often underdiagnosed, with the current diagnosis predominantly relying on subjective assessment. Sleep disturbance and reduced heart rate variability (HRV) have been typically observed in adolescents with MDD. This study aimed to develop an automatic classification model based on nocturnal HRV features to identify adolescent MDD. Sixty-three subjects, including depressed adolescents and healthy controls, participated in the study and completed a three-night sleep electrocardiogram (ECG) monitoring, yielding 160 overnight RR interval time series and 7520 5-min short-term segments for analysis. Nineteen HRV features were extracted from the time domain, frequency domain, and nonlinear dynamics. The Bayesian-optimized random forest (BO-RF) algorithm was applied as the classifier, with performance evaluated using ten-fold cross-validation. The impact of data accumulation on the reliability of identification using short-term data and the importance of features were also examined. The BO-RF classifier based on long-term features achieved a noteworthy predictive accuracy of 80.6%, and the performance of the classifier using short-term data showed a significant improvement when more segment outcomes from the same night were included, ultimately achieving an accuracy of 75.0%. The Poincaré plot-derived features, especially heart rate asymmetry (HRA) features such as C1d, significantly contributed to distinguishing depressed adolescents from healthy subjects. Nocturnal HRV features can effectively differentiate adolescents with MDD from healthy controls. This study provides a promising diagnostic approach for adolescent MDD, with the potential to be integrated into wearable devices for broader application.}, }
@article {pmid40115889, year = {2025}, author = {Li, J and Hu, B and Guan, ZH}, title = {AM-MTEEG: multi-task EEG classification based on impulsive associative memory.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1557287}, pmid = {40115889}, issn = {1662-4548}, abstract = {Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.}, }
@article {pmid40115888, year = {2025}, author = {Liu, J and Xie, J and Zhang, H and Yang, H and Shao, Y and Chen, Y}, title = {Improvement of BCI performance with bimodal SSMVEPs: enhancing response intensity and reducing fatigue.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1506104}, pmid = {40115888}, issn = {1662-4548}, abstract = {Steady-state visual evoked potential (SSVEP) is a widely used brain-computer interface (BCI) paradigm, valued for its multi-target capability and limited EEG electrode requirements. Conventional SSVEP methods frequently lead to visual fatigue and decreased recognition accuracy because of the flickering light stimulation. To address these issues, we developed an innovative steady-state motion visual evoked potential (SSMVEP) paradigm that integrated motion and color stimuli, designed specifically for augmented reality (AR) glasses. Our study aimed to enhance SSMVEP response intensity and reduce visual fatigue. Experiments were conducted under controlled laboratory conditions. EEG data were analyzed using the deep learning algorithm of EEGNet and fast Fourier transform (FFT) to calculate the classification accuracy and assess the response intensity. Experimental results showed that the bimodal motion-color integrated paradigm significantly outperformed single-motion SSMVEP and single-color SSVEP paradigms, respectively, achieving the highest accuracy of 83.81% ± 6.52% under the medium brightness (M) and area ratio of C of 0.6. Enhanced signal-to-noise ratio (SNR) and reduced visual fatigue were also observed, as confirmed by objective measures and subjective reports. The findings verified the bimodal paradigm as a novel application in SSVEP-based BCIs, enhancing both brain response intensity and user comfort.}, }
@article {pmid40115887, year = {2025}, author = {Schreiner, L and Wipprecht, A and Olyanasab, A and Sieghartsleitner, S and Pretl, H and Guger, C}, title = {Brain-computer-interface-driven artistic expression: real-time cognitive visualization in the pangolin scales animatronic dress and screen dress.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1516776}, pmid = {40115887}, issn = {1662-5161}, abstract = {This paper explores the intersection of brain-computer interfaces (BCIs) and artistic expression, showcasing two innovative projects that merge neuroscience with interactive wearable technology. BCIs, traditionally applied in clinical settings, have expanded into creative domains, enabling real-time monitoring and representation of cognitive states. The first project showcases a low-channel BCI Screen Dress, utilizing a 4-channel electroencephalography (EEG) headband to extract an engagement biomarker. The engagement is visualized through animated eyes on small screens embedded in a 3D-printed dress, which dynamically responds to the wearer's cognitive state. This system offers an accessible approach to cognitive visualization, leveraging real-time engagement estimation and demonstrating the effectiveness of low-channel BCIs in artistic applications. In contrast, the second project involves an ultra-high-density EEG (uHD EEG) system integrated into an animatronic dress inspired by pangolin scales. The uHD EEG system drives physical movements and lighting, visually and kinetically expressing different EEG frequency bands. Results show that both projects have successfully transformed brain signals into interactive, wearable art, offering a multisensory experience for both wearers and audiences. These projects highlight the vast potential of BCIs beyond traditional clinical applications, extending into fields such as entertainment, fashion, and education. These innovative wearable systems underscore the ability of BCIs to expand the boundaries of creative expression, turning the wearer's cognitive processes into art. The combination of neuroscience and fashion tech, from simplified EEG headsets to uHD EEG systems, demonstrates the scalability of BCI applications in artistic domains.}, }
@article {pmid40115885, year = {2025}, author = {Liu, J and Li, Y and Zhao, D and Zhong, L and Wang, Y and Hao, M and Ma, J}, title = {Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1525293}, pmid = {40115885}, issn = {1662-5161}, abstract = {BACKGROUND: Stroke is a major global health challenge that significantly influences public health. In stroke rehabilitation, brain-computer interfaces (BCI) offer distinct advantages over traditional training programs, including improved motor recovery and greater neuroplasticity. Here, we provide a first re-evaluation of systematic reviews and meta-analyses to further explore the safety and clinical efficacy of BCI in stroke rehabilitation.<br><br>METHODS: A standardized search was conducted in major databases up to October 2024. We assessed the quality of the literature based on the following aspects: AMSTAR-2, PRISMA, publication year, study design, homogeneity, and publication bias. The data were subsequently visualized as radar plots, enabling a comprehensive and rigorous evaluation of the literature.<br><br>RESULTS: We initially identified 908 articles and, after removing duplicates, we screened titles and abstracts of 407 articles. A total of 18 studies satisfied inclusion criteria were included. The re-evaluation showed that the quality of systematic reviews and meta-analyses concerning stroke BCI training is moderate, which can provide relatively good evidence.<br><br>CONCLUSION: It has been proven that BCI-combined treatment can improve upper limb motor function and the quality of daily life for stroke patients, especially those in the subacute phase, demonstrating good safety. However, its effects on improving speech function, lower limb motor function, and long-term outcomes require further evidence. Multicenter, long-term follow-up studies are needed to increase the reliability of the results.<br><br>CLINICAL TRIAL REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024562114, CRD42023407720.}, }
@article {pmid40112995, year = {2025}, author = {Huang, T and Ma, Y and Chen, H and Zhang, S and Liu, L and Chen, M and Jia, R and Lin, L and Ullah, MW and Fan, Y}, title = {A silk nanofiber and hyaluronic acid composite hemostatic sponge for compressible hemostasis.}, journal = {International journal of biological macromolecules}, volume = {307}, number = {Pt 4}, pages = {142262}, doi = {10.1016/j.ijbiomac.2025.142262}, pmid = {40112995}, issn = {1879-0003}, abstract = {Uncontrolled traumatic blood loss is a leading cause of hemorrhagic shock and death, highlighting the critical need for compressible and rapid hemostatic first-aid materials. In this study, silk nanofibers (MA-SNFs) were prepared through maleic acid (MA) hydrolysis decorated with enriched carboxyl groups. The MA-SNFs were then combined with hyaluronic acid (HA) through EDC/NHS crosslinking to form a porous sponge (i.e., MA-SNF/HA) through freeze-drying. The fabricated MA-SNF/HA sponges demonstrated excellent blood compatibility (hemolysis ratio < 5 %), outstanding hemocompatibility (blood clotting index (BCI) < 35 % within 60 s), and good cytocompatibility (cell viability >85 %). Among the different sponges prepared, M4-H6 (MA-SNFs: HA = 4:6) exhibited the best liquid reabsorption capacity after 80 % compression, outperforming M6-H4 and M5-H5 sponges. Furthermore, M4-H6 sponge absorbed liquid rapidly (~30 s) while matching the liquid absorption capacity of commercial gelatin sponge (GS), which require over 5 min for similar absorption (2232.84 ± 141.69 %). These findings suggest that M4-H6 sponge is highly suitable for compressible hemostasis applications and provide further insights into its potential hemostatic mechanism.}, }
@article {pmid40112909, year = {2025}, author = {Wang, J and Guo, M and Zhang, J and Bai, Y and Ni, G}, title = {Early audiovisual integration in target processing under continuous noise: Behavioral and EEG evidence.}, journal = {Neuropsychologia}, volume = {211}, number = {}, pages = {109128}, doi = {10.1016/j.neuropsychologia.2025.109128}, pmid = {40112909}, issn = {1873-3514}, mesh = {Humans ; Electroencephalography ; Male ; *Visual Perception/physiology ; Female ; Young Adult ; *Auditory Perception/physiology ; Reaction Time/physiology ; Photic Stimulation ; Acoustic Stimulation ; Adult ; Evoked Potentials/physiology ; *Noise ; *Brain/physiology ; Brain Mapping ; }, abstract = {Multisensory integration is interconnected across various information reception. The stage and mechanism of brain response to audiovisual integration have not been fully understood. In this study, we designed audiovisual and unisensory experiments to investigate task performance and electrophysiological characteristics associated with audiovisual integration in a continuous background interference environment using materials collected from the underwater environment. Behavioral results showed that the reaction time (RT) was shorter, and the accuracy was higher in the audiovisual experiment. The cumulative distribution function (CDF) results of RT indicated that audiovisual integration supported the co-activation model. Event-related potential (ERP) results revealed shorter latency of the P1 and N1 components in the audiovisual experiment. Microstate analysis indicated that the parietal-occipital area may play a key role in audiovisual integration. Moreover, event-related spectral perturbation (ERSP) results demonstrated the critical role of low-frequency oscillation in audiovisual integration at the early stage. Our findings support the view that the beneficial effect of audiovisual integration is predominantly upon the early stage of neural information processing, including task-independent information.}, }
@article {pmid40112768, year = {2025}, author = {Yang, HR and Han, MR and Oh, EY and Choi, JY and Choi, JY and Kim, Y and Kim, YT and Kang, H and Kim, JG}, title = {Role of cold-inducible RNA-binding protein in hypothalamic regulation of feeding behavior during fasting and cold exposure.}, journal = {Biochemical and biophysical research communications}, volume = {757}, number = {}, pages = {151616}, doi = {10.1016/j.bbrc.2025.151616}, pmid = {40112768}, issn = {1090-2104}, mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; *Fasting/metabolism ; Male ; *Feeding Behavior/physiology ; *Hypothalamus/metabolism ; Mice ; *Cold Temperature ; *Agouti-Related Protein/metabolism/genetics ; *Mice, Inbred C57BL ; Neurons/metabolism ; Eating/physiology ; }, abstract = {Appetite regulation is a complex process that is critical for maintaining energy balance and is governed by intricate molecular and cellular mechanisms in the hypothalamus. RNA-binding proteins play vital roles in the post-transcriptional regulation of mRNA and influence feeding behavior and energy metabolism. This study explored the role of cold-inducible RNA-binding protein (Cirbp) in hypothalamic neurons under metabolic stress conditions, such as fasting and cold exposure. Next-generation sequencing (NGS) of the hypothalami from fasted mice identified 67 differentially expressed RNA-binding proteins, with Cirbp and RNA-binding motif protein 3 (Rbm3) being significantly upregulated. Immunohistochemical analysis confirmed increased Cirbp expression in the arcuate nucleus (ARC) and dorsomedial hypothalamus during fasting, indicating responsiveness to metabolic cues. Ribo-Tag analysis of agouti-related protein (AgRP) neurons demonstrated elevated Cirbp expression levels in response to fasting, linking it to hunger-regulating pathways. Intracerebroventricular injection of Cirbp antisense oligodeoxynucleotides (AS ODN) reduced Cirbp expression, leading to a decrease in food intake and a reduction in body weight, highlighting the functional role of Cirbp in appetite regulation. Cold exposure also induced Cirbp expression in the ARC, which correlated with an increase in food intake. Blockade of Cirbp by AS ODN treatment attenuated cold-induced food intake, indicating that Cirbp plays a specific role in regulating feeding behavior during cold stress. This suggests that Cirbp is a key mediator in hypothalamic responses to metabolic stress, influencing feeding behavior through its regulatory functions in AgRP neurons. Further exploration of Cirbp mechanisms may offer insights into therapeutic strategies for energy balance disorders, such as obesity and anorexia.}, }
@article {pmid40111769, year = {2025}, author = {Wang, X and Liu, A and Cui, H and Chen, X and Wang, K and Chen, X}, title = {GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3553204}, pmid = {40111769}, issn = {1558-2531}, abstract = {Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement. The code is available for reproducibility at https://github.com/xtwong111/GZSL-Lite-for-SSVEP-Based-BCIs.}, }
@article {pmid40111392, year = {2025}, author = {Tang, P and Jing, P and Luo, Z and Liu, K and Tan, W and Yao, Q and Qiu, Z and Liu, Y and Dou, Q and Yan, X}, title = {Modulating Ionic Hysteresis to Selective Interaction Mechanism toward Transition from Supercapacitor-Memristor to Supercapacitor-Diode.}, journal = {Nano letters}, volume = {25}, number = {13}, pages = {5415-5424}, doi = {10.1021/acs.nanolett.5c00596}, pmid = {40111392}, issn = {1530-6992}, abstract = {The emerging ion-confined transport supercapacitors, including supercapacitor-diodes (CAPodes) and supercapacitor-memristors (CAPistors), offer potential for neuromorphic computing, brain-computer interface, signal propagation, and logic operations. This study reports a novel transition from CAPistor to CAPode via electrochemical cycling of a ZIF-7 electrode. X-ray absorption fine structure (XAFS) and electrochemical analyses reveal a shift from "ionic hysteresis" to "ionic selective interaction" in an alkaline electrolyte, elucidating the evolution of ionic devices. The CAPodes exhibit high rectification ratios, long cycling stability, and effective current blocking in reverse bias. Additionally, they are demonstrated in ionic logic circuits ("AND" and "OR" gates), with comparisons to traditional electronic diodes. This work advances the development of functional supercapacitors and iontronic devices for future capacitive computing architectures.}, }
@article {pmid40110612, year = {2024}, author = {Jain, S and Srivastava, R}, title = {Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {}, number = {}, pages = {9287329241291334}, doi = {10.1177/09287329241291334}, pmid = {40110612}, issn = {1878-7401}, abstract = {BackgroundThe complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.ObjectivesA novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.MethodsWe determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.ResultsOur method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.ConclusionsThis innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.}, }
@article {pmid40110537, year = {2025}, author = {Zhou, Y and Xu, X and Zhang, D}, title = {Cognitive load recognition in simulated flight missions: an EEG study.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1542774}, pmid = {40110537}, issn = {1662-5161}, abstract = {Cognitive load recognition (CLR) utilizing EEG signals has experienced significant advancement in recent years. However, current load-eliciting paradigms often rely on simplistic cognitive tasks such as arithmetic calculations, failing to adequately replicate real-world scenarios and lacking applicability. This study explores simulated flight missions over time to better reflect operational environments and investigate temporal dynamics of multiple load states. Thirty-six participants were recruited to perform simulated flight tasks with varying cognitive load levels of low, medium, and high. Throughout the experiments, we collected EEG load data from three sessions, pre- and post-task resting-state EEG data, subjective ratings, and objective performance metrics. Then, we employed several deep convolutional neural network (CNN) models, utilizing raw EEG data as model input, to assess cognitive load levels with six classification designs. Key findings from the study include (1) a notable distinction between resting-state and post-fatigue EEG data; (2) superior performance of shallow CNN models compared to more complex ones; and (3) temporal dynamics decline in CLR as the missions progressed. This paper establishes a potential foundation for assessing cognitive states during intricate simulated tasks across different individuals.}, }
@article {pmid40109751, year = {2025}, author = {Wang, X and Lin, C and Wang, X}, title = {Psychedelics and Pro-Social Behaviors: A Perspective on Autism Spectrum Disorders.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {3}, pages = {903-906}, pmid = {40109751}, issn = {2575-9108}, abstract = {Autism Spectrum Disorders (ASD) are complex neurodevelopmental conditions characterized by deficits in social interaction, communication, and repetitive behaviors. This viewpoint explores the potential mechanisms through which psychedelics such as lysergic acid diethylamide (LSD), psilocybin, and 3,4-methylenedioxymethamphetamine (MDMA) may positively influence pro-social behaviors, focusing on their implications for individuals with ASD.}, }
@article {pmid40109747, year = {2025}, author = {Li, H and Wang, H and Wang, X}, title = {Psychedelics and the Autonomic Nervous System: A Perspective on Their Interplay and Therapeutic Potential.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {3}, pages = {899-902}, pmid = {40109747}, issn = {2575-9108}, abstract = {Psychedelics, known for their therapeutic potential in psychiatric disorders, interact with the autonomic nervous system in ways that are not well understood. This viewpoint examines the complex relationships between psychedelics and autonomic functions, focusing on sympathetic and parasympathetic modulation. We propose a research framework to elucidate how these interactions influence cardiovascular health and contribute to therapeutic outcomes.}, }
@article {pmid40109736, year = {2025}, author = {Li, H and Wang, X}, title = {Exploring End-of-Life Experiences and Consciousness through the Lens of Psychedelics.}, journal = {ACS pharmacology &amp; translational science}, volume = {8}, number = {3}, pages = {907-909}, pmid = {40109736}, issn = {2575-9108}, abstract = {Exploring dying through the lens of psychedelic experiences offers transformative perspectives on the end-of-life process, potentially alleviating existential distress and enriching the quality of life for those nearing death. Their potential in palliative care, therapy, and spiritual exploration is increasingly recognized, promising to revolutionize end-of-life understanding and care.}, }
@article {pmid40109381, year = {2025}, author = {Lloyd, S and Bonventre, C}, title = {Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future.}, journal = {Science, technology &amp; human values}, volume = {50}, number = {2}, pages = {336-363}, pmid = {40109381}, issn = {0162-2439}, abstract = {In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the "deafness of history and the present," as well as experiences of brain-computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.}, }
@article {pmid40109135, year = {2025}, author = {Sheng, T and Li, J and Zheng, L and Du, N and Xie, M and Wang, X and Gao, X and Huang, M and Wen, S and Liu, W and Guo, Y and Yao, Y and Shao, X and Liu, L and Xu, J and Wang, Y and Zhang, M}, title = {An Expandable Brain-Machine Interface Enabled by Origami Materials and Structures for Tracking Epileptic Traveling Waves.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e2404947}, doi = {10.1002/adhm.202404947}, pmid = {40109135}, issn = {2192-2659}, support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; 1S24080//Beijing Natural Science Foundation/ ; }, abstract = {Tracking neural activities across multiple brain regions remains a daunting challenge due to the non-negligible skull injuries during implantations of large-area electrocorticography (ECoG) grids and the limited spatial accessibility of conventional rectilinear depth probes. Here, a multiregion Brain-machine Interface (BMI) is proposed comprising an expandable bio-inspired origami ECoG electrode covering cortical areas larger than the cranial window, and an expandable origami depth probe capable of reaching multiple deep brain regions beyond a single implantation axis. Using the proposed BMI, it is observed that, in rat models of focal seizures, cortical multiband epileptiform activities mainly manifest as expanding traveling waves outward from a cortical source.}, }
@article {pmid40108144, year = {2025}, author = {Qi, Z and Liu, H and Jin, F and Wang, Y and Lu, X and Liu, L and Yang, Z and Fan, L and Song, M and Zuo, N and Jiang, T}, title = {A wearable repetitive transcranial magnetic stimulation device.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {2731}, pmid = {40108144}, issn = {2041-1723}, support = {2021ZD0200200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82202253//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31620103905//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Transcranial Magnetic Stimulation/instrumentation/methods ; *Wearable Electronic Devices ; Adult ; Male ; Female ; Walking/physiology ; Equipment Design ; Young Adult ; Motor Cortex/physiology ; }, abstract = {Repetitive transcranial magnetic stimulation (rTMS) is widely used to treat various neuropsychiatric disorders and to explore the brain, but its considerable power consumption and large size limit its potential for broader utility, such as applications in free behaviors and in home and community settings. We addressed this challenge through lightweight magnetic core coil designs and high-power-density, high-voltage pulse driving techniques and successfully developed a battery-powered wearable rTMS device. The combined weight of the stimulator and coil is only 3 kg. The power consumption was reduced to 10% of commercial rTMS devices even though the stimulus intensity and repetition frequency are comparable. We demonstrated the effectiveness of this device during free walking, showing that neural activity associated with the legs can enhance the cortex excitability associated with the arms. This advancement allows for high-frequency rTMS modulation during free behaviors and enables convenient home and community rTMS treatments.}, }
@article {pmid40107265, year = {2025}, author = {Jiang, R and Tian, Y and Yuan, X and Guo, F}, title = {Regulation of pre-dawn arousal in Drosophila by a pair of trissinergic descending neurons of the visual and circadian networks.}, journal = {Current biology : CB}, volume = {35}, number = {8}, pages = {1750-1764.e3}, doi = {10.1016/j.cub.2025.02.056}, pmid = {40107265}, issn = {1879-0445}, mesh = {Animals ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Drosophila melanogaster/physiology ; *Neurons/physiology ; *Arousal/physiology ; }, abstract = {Circadian neurons form a complex neural network that generates circadian oscillations. How the circadian neural network transmits circadian signals to other brain regions, thereby regulating the activity patterns in fruit flies, is not well known. Using the FlyWire database, we identified a cluster of descending neurons, DNp27, which is densely connected with key circadian neurons and the visual circuit, projecting extensively across the brain. DNp27 receives excitatory inputs from the circadian neurons DN3s at night and photo-inhibitory signals predominantly during the day, resulting in calcium oscillations that peak in the early morning and dip at dusk. Experimental manipulation of DNp27 revealed its role in activity regulation: artificial activation of DNp27 decreased flies' activity, while ablation or silencing led to an advance in the morning anticipatory peak. Similar alterations in the morning peak were observed following pan-neuronal knockdown of either Trissin or TrissinR, suggesting the involvement of this neuropeptide signaling pathway in DNp27 function. Moreover, neural circuitry and connectivity analyses indicate that DNp27 may regulate circadian neurons via extra-clock electrical oscillators (xCEOs). Lastly, we found that DNp27 modulates arousal thresholds by inhibiting light-responsive activity in the central brain, thereby promoting sleep stability, particularly in the pre-dawn period. Together, these findings suggest that DNp27 plays a crucial role in maintaining stable sleep patterns.}, }
@article {pmid40106898, year = {2025}, author = {Hobbs, TG and Greenspon, CM and Verbaarschot, C and Valle, G and Hughes, CL and Boninger, ML and Bensmaia, SJ and Gaunt, RA}, title = {Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adc2d4}, pmid = {40106898}, issn = {1741-2552}, abstract = {OBJECTIVE: Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts.<br><br>APPROACH: In this study, we investigated whether biomimetic stimulation patterns - ICMS patterns that reproduce essential features of natural neural activity - increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation.<br><br>MAIN RESULTS: We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation.<br><br>SIGNIFICANCE: We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.}, }
@article {pmid40106847, year = {2025}, author = {Wen, B and Su, L and Zhang, Y and Wang, A and Zhao, H and Wu, J and Gan, Z and Zhang, L and Kang, X}, title = {Fabrication of micro-wire stent electrode as a minimally invasive endovascular neural interface for vascular electrocorticography using laser ablation method.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {3}, pages = {}, doi = {10.1088/2057-1976/adc266}, pmid = {40106847}, issn = {2057-1976}, mesh = {Animals ; Rats ; *Stents ; *Laser Therapy/methods/instrumentation ; *Electrocorticography/instrumentation/methods ; Equipment Design ; Endovascular Procedures/instrumentation/methods ; Rats, Sprague-Dawley ; Electrodes ; Male ; Electrodes, Implanted ; }, abstract = {Objective. Minimally invasive endovascular stent electrode is a currently emerging technology in neural engineering with minimal damage to the neural tissue. However, the typical stent electrode still requires resistive welding and is relatively large, limiting its application mainly on the large animal or thick vessels. In this study, we investigated the feasibility of laser ablation of micro-wire stent electrode with a diameter as small as 25μmand verified it in the superior sagittal sinus (SSS) of a rat.Approach. We have developed a laser ablation technology to expose the electrode sites of the micro-wire on both sides without damaging the wire itself. During laser ablation, we applied a new method to fix and realign the micro-wires. The micro-wire stent electrode was fabricated by carefully assemble the micro-wire and stent. We tested the electrochemical performances of the electrodes as a neural interface. Finally, we deployed the stent electrode in a rat to verified the feasibility.Main result. Based on the proposed micro-wire stent electrode, we demonstrated that the stent electrode could be successfully deployed in a rat. With the benefit of the smaller design and laser fabrication technology, it can be fitted into a catheter with an inner diameter of 0.6mm. The vascular electrocorticography can be detected during the acute recording, making it promising in the application of small animals and thin vessels.Significance. The method we proposed combines the advantages of endovascular micro-wire electrode and stent, helping make the electrodes smaller. This study provided an alternative method for deploying micro-wire electrodes into thinner vessels as an endovascular neural interface.}, }
@article {pmid40106436, year = {2025}, author = {Wang, Y and Chen, Z and Liang, K and Wang, W and Hu, Z and Mao, Y and Liang, X and Jiang, L and Liu, Z and Ma, Z}, title = {AGO2 mediates immunotherapy failure via suppressing tumor IFN-gamma response-dependent CD8[+] T cell immunity.}, journal = {Cell reports}, volume = {44}, number = {4}, pages = {115445}, doi = {10.1016/j.celrep.2025.115445}, pmid = {40106436}, issn = {2211-1247}, abstract = {Interferon-gamma (IFN-γ), a cytokine essential for activating cellular immune responses, plays a crucial role in cancer immunosurveillance and the clinical success of immune checkpoint blockade therapy. In this study, we show that Argonaute 2 (AGO2), a key mediator in small RNA-guided gene regulation, inversely correlates with tumor responsiveness to IFN-γ and the efficacy of immunotherapy. Mechanistically, IFN-γ upregulates miR-1246 expression in tumor cells, enhancing its interaction with AGO2. This miR-1246-AGO2 complex disrupts IFN-γ-mediated signal transducer and activator of transcription 1 (STAT1) phosphorylation by stabilizing protein tyrosine phosphatase non-receptor 6 (PTPN6) mRNA, thereby suppressing the expression of downstream C-X-C motif chemokine ligands (CXCLs), IFN-stimulated genes (ISGs), and human leukocyte antigen (HLA) molecules, which collectively contribute to tumor immune evasion. In preclinical cancer models, inhibiting AGO2 with BCI-137 or targeting miR-1246 with its antagomir re-sensitizes tumor cells to IFN-γ, leading to the enhanced recruitment, activation, and cytotoxicity of CD8[+] T cells and ultimately improving immunotherapy efficacy.}, }
@article {pmid40104767, year = {2025}, author = {Memmott, T and Klee, D and Smedemark-Margulies, N and Oken, B}, title = {Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1551214}, pmid = {40104767}, issn = {1662-5161}, abstract = {A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults (n = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.}, }
@article {pmid40103837, year = {2024}, author = {Yektaeian Vaziri, A and Makkiabadi, B}, title = {Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1505017}, pmid = {40103837}, issn = {1662-4548}, abstract = {This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.}, }
@article {pmid40102969, year = {2025}, author = {Jeong, H and Song, M and Jang, SH and Kim, J}, title = {Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {61}, pmid = {40102969}, issn = {1743-0003}, support = {2022 R1 A2 C1008150//National Research Foundation of Korea/ ; NRCTR-EX23004//Translational Research Program for Rehabilitation Robots/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke Rehabilitation/methods ; Female ; Adult ; *Electroencephalography/methods ; *Learning/physiology ; *Imagination/physiology ; Middle Aged ; Neuronal Plasticity/physiology ; Young Adult ; Motor Cortex/physiology ; Spectroscopy, Near-Infrared/methods ; Neurological Rehabilitation/methods ; }, abstract = {BACKGROUND: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.<br><br>OBJECTIVE: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).<br><br>METHODS: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.<br><br>RESULTS: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.021 for healthy people; p&#x2009;=&#x2009;0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.099 for healthy people, p&#x2009;=&#x2009;0.084 for people with stroke).<br><br>CONCLUSIONS: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.}, }
@article {pmid40102931, year = {2025}, author = {Wu, X and Hu, Z and Yue, H and Wang, C and Li, J and Yang, Y and Luan, Z and Wang, L and Shen, Y and Gu, Y}, title = {Enhancing myelinogenesis through LIN28A rescues impaired cognition in PWMI mice.}, journal = {Stem cell research &amp; therapy}, volume = {16}, number = {1}, pages = {141}, pmid = {40102931}, issn = {1757-6512}, support = {2017YFA0104200//National Key Research and Development Program of China/ ; 32071021//National Natural Science Foundation of China/ ; 32225021//China National Funds for Distinguished Young Scientists/ ; }, mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; Mice ; *Myelin Sheath/metabolism ; Cell Differentiation ; White Matter/metabolism/pathology ; Oligodendrocyte Precursor Cells/metabolism ; Mice, Knockout ; Cognition ; Oligodendroglia/metabolism ; Disease Models, Animal ; }, abstract = {BACKGROUND: In premature newborn infants, preterm white matter injury (PWMI) causes motor and cognitive disabilities. Accumulating evidence suggests that PWMI may result from defected differentiation of oligodendrocyte precursor cells (OPCs) and impaired maturation of oligodendrocytes. However, the underlying mechanisms remain unclear.<br><br>METHODS: Using RNAscope, we analyzed the expression level of RNA-binding protein LIN28A in individual OPCs. Knockout of one or both alleles of Lin28a in OPCs was achieved by administrating tamoxifen to NG2[CreER]::Ai14::Lin28a[flox/+] or NG2[CreER]::Ai14::Lin28a[flox/flox] mice. Lentivirus expressing FLEX-Lin28a was used in NG2[CreER] mice to overexpress LIN28A in OPCs. A series of behavioral tests were performed to assess the cognitive functions of mice. Two-tailed unpaired t-tests was carried out for statistical analysis between groups.<br><br>RESULTS: We found that the expression of Lin28a was decreased in OPCs in a PWMI mouse model. Knockout of one or both alleles of Lin28a in OPCs postnatally resulted in reduced OPC differentiation, decreased myelinogenesis and impaired cognitive functions. Supplementing LIN28A in OPCs postnatally was able to promote OPC differentiation and enhance myelinogenesis, thus rescuing the cognitive functions in PWMI mice.<br><br>CONCLUSION: Our study reveals that LIN28A is critical in regulating postnatal myelinogenesis. Overexpression of LIN28A in OPCs rescues cognitive deficits in PWMI mice by promoting myelinogenesis, thus providing a potential strategy for the treatment of PWMI.}, }
@article {pmid40101709, year = {2025}, author = {Chen, Q and Zhu, L and Zhang, S and Qiao, S and Ding, ZJ and Zheng, SJ and Guo, J and Su, N}, title = {Structures and mechanisms of the ABC transporter ABCB1 from Arabidopsis thaliana.}, journal = {Structure (London, England : 1993)}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.str.2025.02.008}, pmid = {40101709}, issn = {1878-4186}, abstract = {The Arabidopsis thaliana auxin transporter ABCB1 plays a fundamental role in the regulation of plant growth and development. While its homolog ABCB19 was previously shown to transport brassinosteroids (BR), another class of essential hormones, the ability of ABCB1 to mediate BR transport has remained unexplored. In this study we show that ABCB1 also transports brassinosteroids with an in vitro brassinolide (BL) transport assay. Using single-particle cryo-electron microscopy, we determined ABCB1 structures in multiple inward-facing conformations in the apo state, ANP-bound state, BL-bound state, and the both BL- and ANP-bound state. BL binds to the large cavity of two transmembrane domains, inducing a slight conformational change. Additionally, we obtained the structure of ABCB1 in an outward-facing conformation. By comparing these different conformations, we elucidated the possible mechanism of hormone transport by ABCB1. These high-resolution structures help us to understand the structural basis for hormone recognition and transport mechanisms of ABCB1.}, }
@article {pmid40101581, year = {2025}, author = {Abdelaty, MM and Rushdi, MA and Rasmy, ME and Annaby, MH}, title = {Graph vertex and spectral features for EEG-based motor imagery classification.}, journal = {Computers in biology and medicine}, volume = {189}, number = {}, pages = {109944}, doi = {10.1016/j.compbiomed.2025.109944}, pmid = {40101581}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Support Vector Machine ; Algorithms ; }, abstract = {Motor imagery (MI) patterns play a vital role in brain-computer interface (BCI) systems, enabling control of external devices without relying on peripheral nerves or muscles. These patterns are typically classified by analyzing the associated electroencephalogram (EEG) signals. In this work, we introduce a novel MI classification approach based on multilevel graph-theoretic modeling of multichannel EEG signals. Multivariate autoregressive modeling and coherence analysis are firstly employed to construct directed graph signals to represent the relationships among EEG channels and capture the complex correlations inherent in MI patterns. Spatial graph vertex features are thus extracted as well as graph Fourier transform coefficients. Moreover, multilevel generalizations of vertex-domain features are thus defined where edges of graph signals are pruned according to different thresholds, vertex features are extracted for each threshold level, and then all features are combined into a multilevel hierarchical graph descriptor. These graph-theoretic descriptors could be fused with different variants of common spatial patterns for improved discriminability on MI classification tasks. Different feature combinations are used to train k-nearest neighbor classifiers, support vector machines, and random forests for MI pattern classification. The proposed method demonstrates competitive performance compared to the FWCSP and SCSP methods on Dataset 2a of the BCI Competition IV, as well as robust results on Dataset 1 from the same competition. Overall, the findings highlight the potential of multilevel spatial and spectral graph features in leveraging the correlation among EEG channels towards enhanced MI classification performance.}, }
@article {pmid40101362, year = {2025}, author = {Arpaia, P and Esposito, A and Galasso, E and Galdieri, F and Natalizio, A}, title = {A wearable brain-computer interface to play an endless runner game by self-paced motor imagery.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adc205}, pmid = {40101362}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Wearable Electronic Devices ; Female ; Adult ; Young Adult ; Electroencephalography/methods/instrumentation ; Video Games ; Running/physiology/psychology ; Movement/physiology ; Psychomotor Performance/physiology ; }, abstract = {Objective.A wearable brain-computer interface is proposed and validated experimentally in relation to the real-time control of an endless runner game by self-paced motor imagery(MI).Approach.Electroencephalographic signals were recorded via eight wet electrodes. The processing pipeline involved a filter-bank common spatial pattern approach and the combination of three binary classifiers exploiting linear discriminant analysis. This enabled the discrimination between imagining left-hand, right-hand, and no movement. Each mental task corresponded to an avatar horizontal motion within the game. Twenty-three healthy subjects participated to the experiments and their data are made publicly available. A custom metric was proposed to assess avatar control performance during the gaming phase. The game consisted of two levels, and after each, participants completed a questionnaire to self-assess their engagement and gaming experience.Main results.The mean classification accuracies resulted 73%, 73%, and 67% for left-rest, right-rest, and left-right discrimination, respectively. In the gaming phase, subjects with higher accuracies for left-rest and right-rest pair exhibited higher performance in terms of the custom metric. Correlation of the offline and real-time performance was investigated. The left-right MI did not correlate to the gaming phase performance due to the poor mean accuracy of the calibration. Finally, the engagement questionnaires revealed that level 1 and level 2 were not perceived as frustrating, despite the increasing difficulty.Significance.The work contributes to the development of wearable and self-paced interfaces for real-time control. These enhance user experience by guaranteeing a more natural interaction with respect to synchronous neural interfaces. Moving beyond benchmark datasets, the work paves the way to future applications on mobile devices for everyday use.}, }
@article {pmid40101262, year = {2025}, author = {Choubey, C and Dhanalakshmi, M and Karunakaran, S and Londhe, GV and Vimal, V and Kirubakaran, MK}, title = {Optimizing Bioimaging: Quantum Computing-Inspired Bald Eagle Search Optimization for Motor Imaging EEG Feature Selection.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594251325273}, doi = {10.1177/15500594251325273}, pmid = {40101262}, issn = {2169-5202}, abstract = {One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.}, }
@article {pmid40100843, year = {2025}, author = {Russo, JS and Mahoney, T and Kokorin, K and Reynolds, A and Lin, CS and John, SE and Grayden, DB}, title = {Towards developing brain-computer interfaces for people with Multiple Sclerosis.}, journal = {PloS one}, volume = {20}, number = {3}, pages = {e0319811}, pmid = {40100843}, issn = {1932-6203}, mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/psychology ; Female ; Male ; Adult ; Middle Aged ; Surveys and Questionnaires ; Electroencephalography ; }, abstract = {BACKGROUND: Multiple Sclerosis (MS) can be a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. Present BCI designs have also overlooked the unique pathological changes associated with MS and have not considered needs of users within their home environments. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We hypothesised that (i) people with MS would be interested in adopting BCI technology and (ii) those with reduced independence would prefer a higher-performing invasive BCI.<br><br>METHODS: We conducted an online survey of people with MS to describe user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest in BCI applications, bionic applications, device preferences, and development considerations and related these to symptoms and assistance needs.<br><br>RESULTS: We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Descriptive analysis indicated that level of independence did not influence preference towards the higher performing but highly invasive BCI.<br><br>CONCLUSIONS: The needs of end users reported in this study are crucial for efficient development of BCI systems that can be effectively translated into the home environment. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.}, }
@article {pmid40100695, year = {2025}, author = {Guo, Z and Xu, L and Tan, W and Chen, F}, title = {Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1180-1190}, doi = {10.1109/TNSRE.2025.3552606}, pmid = {40100695}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; Male ; *Imagination/physiology ; Female ; Adult ; *Speech/physiology ; Young Adult ; Algorithms ; Healthy Volunteers ; Stroke Rehabilitation/methods ; }, abstract = {Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the "yes" and "no" speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.}, }
@article {pmid40100694, year = {2025}, author = {Yan, W and Lin, Y and Chen, YF and Wang, Y and Wang, J and Zhang, M}, title = {Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1156-1168}, doi = {10.1109/TNSRE.2025.3551753}, pmid = {40100694}, issn = {1558-0210}, mesh = {*Neuronal Plasticity/physiology ; Humans ; *Stroke Rehabilitation/methods ; *Recovery of Function ; *Brain-Computer Interfaces ; Stroke/physiopathology ; Robotics ; Electric Stimulation Therapy/methods ; Postural Balance/physiology ; Transcranial Magnetic Stimulation/methods ; }, abstract = {Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.}, }
@article {pmid40100553, year = {2025}, author = {Zhou, H and Yan, ZN and Gao, WH and Lv, XX and Luo, R and Hoellwarth, JS and He, L and Yang, JM and Zhang, JY and Wang, HL and Xie, Y and Chen, XL and Xue, MD and Fang, Y and Duan, YY and Li, RY and Wang, XD and Wang, RL and Xie, M and Huang, L and Liu, PR and Ye, ZW}, title = {Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain-Computer Interface Based on Mixed Reality.}, journal = {Current medical science}, volume = {}, number = {}, pages = {}, pmid = {40100553}, issn = {2523-899X}, support = {No.82172524//the National Natural Science Foundation of China/ ; No.81974355//the National Natural Science Foundation of China/ ; No.2020021105012440//National Innovation Platform Development Program/ ; 2021BEA161//Major Program of Hubei Province/ ; JD2023BAA005//Major Key Project of Hubei Province/ ; No.2024XHYN047//Wuhan Union Hospital Free Innovation Preliminary Research Fund/ ; }, abstract = {OBJECTIVE: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.<br><br>METHODS: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0&#xa0;T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR visualization.<br><br>RESULTS: This study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical model.<br><br>CONCLUSIONS: This multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.}, }
@article {pmid40100543, year = {2025}, author = {Gao, J and Tang, H and Wang, Z and Li, Y and Luo, N and Song, M and Xie, S and Shi, W and Yan, H and Lu, L and Yan, J and Li, P and Song, Y and Chen, J and Chen, Y and Wang, H and Liu, W and Li, Z and Guo, H and Wan, P and Lv, L and Yang, Y and Wang, H and Zhang, H and Wu, H and Ning, Y and Zhang, D and Jiang, T}, title = {Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {40100543}, issn = {1995-8218}, abstract = {Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.}, }
@article {pmid40099835, year = {2025}, author = {Jin, J and Xiao, Q and Liu, Y and Xu, T and Shen, Q}, title = {Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {3}, pages = {}, doi = {10.1093/cercor/bhaf058}, pmid = {40099835}, issn = {1460-2199}, support = {2022KFKT005//Open Research Fund/ ; 22dz2261100//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; 23NDJC124YB//Zhejiang Province Philosophy and Social Science Planning Project/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 72371165//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; Young Adult ; *Gambling/psychology/physiopathology ; *Electroencephalography ; *Risk-Taking ; *Evoked Potentials/physiology ; *Decision Making/physiology ; Reproducibility of Results ; Adult ; *Reward ; Event-Related Potentials, P300/physiology ; Brain/physiology ; }, abstract = {The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.}, }
@article {pmid40096442, year = {2025}, author = {Shi, X and Zhai, X and Wang, R and Le, Y and Fu, S and Liu, N}, title = {Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096442}, issn = {1424-8220}, abstract = {With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs' distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations.}, }
@article {pmid40096214, year = {2025}, author = {Bouchane, M and Guo, W and Yang, S}, title = {Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096214}, issn = {1424-8220}, support = {236Z0105G//Hebei Central Leading Local Science and Technology Development Foundation/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). Evaluated using the publicly available PhysioNet dataset, the CNN-GRU classifier achieved peak mean accuracy rates of 99.71%, 99.73%, 99.61%, and 99.86% for tasks involving left fist (LF), right fist (RF), both fists (LRF), and both feet (BF), respectively. The experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency on small-scale EEG datasets. The CNN-GRU and CNN-Bi-GRU architectures exhibit superior predictive reliability, offering a faster, cost-effective solution for user-adaptable MI-BCI applications.}, }
@article {pmid40096116, year = {2025}, author = {González-España, JJ and Sánchez-Rodríguez, L and Pacheco-Ramírez, MA and Feng, J and Nedley, K and Chang, SH and Francisco, GE and Contreras-Vidal, JL}, title = {At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096116}, issn = {1424-8220}, support = {1827769//National Science Foundation/ ; 2137255//NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Female ; Middle Aged ; *Neurological Rehabilitation/methods/instrumentation ; *Exoskeleton Device ; Aged ; Stroke/physiopathology ; Adult ; Robotics/methods ; Electroencephalography/methods ; }, abstract = {BACKGROUND: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians.<br><br>METHODS: This paper describes the early findings of the NeuroExo brain-machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users' compliance and system performance.<br><br>RESULTS: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02).<br><br>CONCLUSIONS: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.}, }
@article {pmid40096108, year = {2025}, author = {Alexopoulou, A and Pergantis, P and Koutsojannis, C and Triantafillou, V and Drigas, A}, title = {Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096108}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Autism Spectrum Disorder/therapy ; Adolescent ; Electroencephalography/methods ; Virtual Reality ; User-Computer Interface ; Cognition/physiology ; }, abstract = {This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD.}, }
@article {pmid40096020, year = {2025}, author = {Vafaei, E and Hosseini, M}, title = {Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096020}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Seizures/physiopathology/diagnosis ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.}, }
@article {pmid40096019, year = {2025}, author = {Koo, BH and Siu, HC and Newman, DJ and Roche, ET and Petersen, LG}, title = {Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {5}, pages = {}, pmid = {40096019}, issn = {1424-8220}, mesh = {Humans ; *Electromyography/methods ; *Algorithms ; Male ; Adult ; Movement/physiology ; Neural Networks, Computer ; Muscle, Skeletal/physiology ; Female ; Young Adult ; Deep Learning ; Motion ; Signal Processing, Computer-Assisted ; Machine Learning ; }, abstract = {This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader-follower paradigms seen in today's systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application.}, }
@article {pmid40095842, year = {2025}, author = {Tian, B and Zhang, S and Xue, D and Chen, S and Zhang, Y and Peng, K and Wang, D}, title = {Decoding Intrinsic Fluctuations of Engagement From EEG Signals During Fingertip Motor Tasks.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1271-1283}, doi = {10.1109/TNSRE.2025.3551819}, pmid = {40095842}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Fingers/physiology ; Adult ; Female ; Young Adult ; Machine Learning ; Algorithms ; Virtual Reality ; Brain-Computer Interfaces ; Psychomotor Performance/physiology ; Reproducibility of Results ; Motor Skills/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; }, abstract = {Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.}, }
@article {pmid40095346, year = {2025}, author = {Li, J and Shao, N and Zhang, Y and Liu, X and Zhang, H and Tian, L and Piatkevich, KD and Zhang, D and Lee, HJ}, title = {Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.}, journal = {Small methods}, volume = {}, number = {}, pages = {e2402124}, doi = {10.1002/smtd.202402124}, pmid = {40095346}, issn = {2366-9608}, support = {82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 32050410298//National Natural Science Foundation of China/ ; 32171093//National Natural Science Foundation of China/ ; LZ25H180001//Natural Science Foundation of Zhejiang Province/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024YFA1408900//National Key Research and Development Program of China/ ; 28961//Brain and Behavior Research Foundation/ ; }, abstract = {Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm[-1] red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.}, }
@article {pmid40093990, year = {2025}, author = {Yang, KC and Xu, Y and Lin, Q and Tang, LL and Zhong, JW and An, HN and Zeng, YQ and Jia, K and Jin, Y and Yu, G and Gao, F and Zhao, L and Tong, LS}, title = {Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.}, journal = {EClinicalMedicine}, volume = {81}, number = {}, pages = {103128}, pmid = {40093990}, issn = {2589-5370}, abstract = {BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).<br><br>METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023&#xa0;at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.<br><br>FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0&#xb7;94, sensitivity 0&#xb7;96, and specificity 0&#xb7;8 for the internal testing subset; AUC 0&#xb7;85, sensitivity 0&#xb7;87, and specificity 0&#xb7;82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0&#xb7;79 vs 0&#xb7;71, sensitivity 0&#xb7;88 vs 0&#xb7;81, specificity 0&#xb7;75 vs 0&#xb7;68, p&#xa0;<&#xa0;0&#xb7;05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.<br><br>INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.<br><br>FUNDING: The work was funded by the National Key R&amp;D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&amp;D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University.}, }
@article {pmid40092993, year = {2025}, author = {Wen, J and Li, Y and Deng, W and Li, Z}, title = {Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues.}, journal = {Frontiers in immunology}, volume = {16}, number = {}, pages = {1528363}, pmid = {40092993}, issn = {1664-3224}, mesh = {Humans ; *Neoplasms/immunology/therapy/drug therapy ; *Central Nervous System/immunology ; *Tumor Microenvironment/immunology ; Animals ; Cell Communication/immunology ; Signal Transduction ; Neuroimmunomodulation ; Immunotherapy/methods ; Immune Checkpoint Inhibitors/therapeutic use/pharmacology ; }, abstract = {Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.}, }
@article {pmid40092069, year = {2025}, author = {Sayal, A and Direito, B and Sousa, T and Singer, N and Castelo-Branco, M}, title = {Music in the loop: a systematic review of current neurofeedback methodologies using music.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1515377}, pmid = {40092069}, issn = {1662-4548}, abstract = {Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.}, }
@article {pmid40091139, year = {2025}, author = {Thamaraimanalan, T and Gopal, D and Vignesh, S and Kishore Kumar, K}, title = {Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {9029}, pmid = {40091139}, issn = {2045-2322}, mesh = {Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Brain/physiology ; *Cognition/physiology ; *Principal Component Analysis ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method's robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.}, }
@article {pmid40089693, year = {2025}, author = {Khamisa, N and Madala, S and Fonka, CB}, title = {Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.}, journal = {BMC nursing}, volume = {24}, number = {1}, pages = {290}, pmid = {40089693}, issn = {1472-6955}, abstract = {BACKGROUND: The wellbeing of health care workers (HCWs) has been an ongoing challenge, especially within low and middle-income countries (LMICs) such as South Africa. Evidence suggesting that HCWs are increasingly stressed and burned out is cause for concern. Nurses in particular have been impacted physically, mentally and psychosocially during the recent COVID-19 pandemic. This may leave a disproportionate consequence, affecting various aspects of their wellbeing, thereby justifying a need for a more holistic investigation of the wellbeing of South African nurses and their coping mechanisms during the peak of the pandemic.<br><br>METHODS: This was a cross-sectional study design. Online self-reported questionnaires were administered in six hospitals, sampled purposively and conveniently from three South African provinces. Using STATA 18.0, the Wilcoxon Ranksum test at 5% alpha compared the wellbeing and coping mechanisms of nursing staff and nursing management during COVID-19's peak. Univariable and multivariable linear regression analyses were performed to determine factors associated with burnout in nurses, at a 95% confidence interval (CI). Validated scales measuring burnout, coping, resilience, as well as mental and physical health were utilised.<br><br>RESULTS: Of 139 participants, 112(97.4%) were females, with 91(82%) and 20(18%) being nursing staff and management respectively. The median age of the participants was 43.3 years (n&#x2009;=&#x2009;112), with a practising duration of 12 years (n&#x2009;=&#x2009;111). There was a significant difference in the burnout score between nursing staff and nursing management (p&#x2009;=&#x2009;0.028). In the univariable linear regression model, burnout was significantly (p&#x2009;<&#x2009;0.05) associated with the Brief COPE Inventory (BCI), Conor-Davidson Resilience Scale (CDRS), Global Mental and Health Scale (GMHS), Global Physical and Health Scale (GPHS) and Hospital Anxiety and Depression Scale (HADS), as well as occupation. In the multivariable linear regression model, burnout was significantly associated with the CDRS [Coeff.=0.7, 95%CI 0.4; 0.9], GMHS [Coeff.=-2.4, 95%CI -3.2; -1.6], GPHS [Coeff.2.1, 95%CI 1.3; 2.9], and HADS [Coeff.=0.7, 95%CI 0.2; 1.2].<br><br>CONCLUSION: Investigating multiple aspects of wellbeing in this study, it's shown that coping and resilience may not be key factors in promoting the wellbeing of South African nurses. However, effective mental health interventions are crucial and should be prioritised to mitigate burnout during future health emergencies. Future studies examining the associations between general health, coping and resilience may help generate further evidence towards holistic interventions aimed at promoting nurses' wellbeing.<br><br>CLINICAL TRIAL NUMBER: Not applicable.}, }
@article {pmid40089573, year = {2025}, author = {Sivasakthivel, R and Rajagopal, M and Anitha, G and Loganathan, K and Abbas, M and Ksibi, A and Rao, KS}, title = {Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {8951}, pmid = {40089573}, issn = {2045-2322}, mesh = {Humans ; *Algorithms ; Female ; *Neurodegenerative Diseases/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Neural Networks, Computer ; Electroencephalography/methods ; Middle Aged ; }, abstract = {Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.}, }
@article {pmid40087270, year = {2025}, author = {Eby, J and Beutel, M and Koivisto, D and Achituve, I and Fetaya, E and Zariffa, J}, title = {Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {440}, pmid = {40087270}, issn = {2052-4463}, mesh = {Humans ; *Electromyography ; *Gestures ; Brain-Computer Interfaces ; }, abstract = {Neurotechnological interfaces have the potential to create new forms of human-machine interactions, by allowing devices to interact directly with neurological signals instead of via intermediates such as keystrokes. Surface electromyography (sEMG) has been used extensively in myoelectric control systems, which use bioelectric activity recorded from muscles during contractions to classify actions. This technology has been used primarily for rehabilitation applications. In order to support the development of myoelectric interfaces for a broader range of human-machine interactions, we present an sEMG dataset obtained during key presses in a typing task. This fine-grained classification dataset consists of 16-channel bilateral sEMG recordings and key logs, collected from 19 individuals in two sessions on different days. We report baseline results on intra-session, inter-session and inter-subject evaluations. Our baseline results show that within-session accuracy is relatively high, even with simple learning models. However, the results on between-session and between-participant are much lower, showing that generalizing between sessions and individuals is an open challenge.}, }
@article {pmid40086264, year = {2025}, author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T}, title = {Derivation of human-derived iPSC line from a male adolescent with first-episode of sporadic schizophrenia.}, journal = {Stem cell research}, volume = {85}, number = {}, pages = {103694}, doi = {10.1016/j.scr.2025.103694}, pmid = {40086264}, issn = {1876-7753}, abstract = {Schizophrenia is considered to be a neurodevelopmental disorder with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were collected from a male adolescent diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by reprogramming using the factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The generated iPSC line was validated by karyotype analysis and expression of pluripotency markers. These iPSCs were capable of differentiating into derivatives of all three germ layers in vivo.}, }
@article {pmid40085468, year = {2025}, author = {Chen, J and Yang, H and Xia, Y and Gong, T and Thomas, A and Liu, J and Chen, W and Carlson, T and Zhao, H}, title = {Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1242-1251}, doi = {10.1109/TNSRE.2025.3551676}, pmid = {40085468}, issn = {1558-0210}, mesh = {Humans ; *Tomography, Optical/methods/instrumentation ; *Mental Fatigue/diagnostic imaging/psychology/diagnosis ; Male ; Adult ; *Workload/psychology ; *Wearable Electronic Devices ; Machine Learning ; Female ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; Young Adult ; Reproducibility of Results ; Support Vector Machine ; Algorithms ; Brain/diagnostic imaging ; Imaging, Three-Dimensional ; }, abstract = {Accurately assessing mental states-such as mental workload and fatigue- is crucial for ensuring the reliability and effectiveness of brain-computer interface (BCI)-based applications. Relying on signals from a limited brain region with low spatial resolution may fail to capture the full scope of relevant information. To address this, high-density diffuse optical tomography (HD-DOT), an emerging form of functional near-infrared spectroscopy (fNIRS) was employed in this study, which provides higher spatial resolution for hemodynamic measurements and enables the reconstruction of 3D brain images. An experiment protocol was designed to investigate both mental workload and fatigue, two critical components of cognitive state that often fluctuate concurrently in real-world scenarios. Machine learning methods were applied for subject-specific classification, achieving 95.14% mean accuracy for fatigue/non-fatigue and 97.93% for four n-back tasks using Random Forest, outperforming Support Vector Machines. These results highlight the transformative potential of HD-DOT in advancing multifaceted cognitive state assessment, paving the way for more precise, adaptable, and powerful BCI applications.}, }
@article {pmid40084138, year = {2025}, author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T}, title = {Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1491844}, pmid = {40084138}, issn = {1662-4548}, abstract = {OBJECTIVE: Wireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods. However, conventional wireless neural recording devices have not been optimized for ECoG recording, and few devices have been tested on freely moving primates engaged in behavioral tasks within their home cages.<br><br>METHODS: We developed a compact, low-power, 32-channel wireless ECoG headstage specifically designed for neuroscience research. To evaluate its efficacy, we established a behavioral task setup within a home cage environment.<br><br>RESULTS: The developed headstage weighed merely 1.8 g and had compact dimensions of 25 mm &#xd7; 16 mm &#xd7; 4 mm. It was efficiently powered by a 100-mAh battery (weighing 3 g), enabling continuous recording for 8.5 h. The device successfully recorded data from an unrestrained monkey performing a center-out joystick task within its home cage.<br><br>CONCLUSION: The device demonstrated excellent capability for recording ECoG data from freely moving primates in a home cage environment. This versatile device enhances task design freedom, decrease researchers' workload, and enhances data collection efficiency.}, }
@article {pmid40083893, year = {2025}, author = {Gordienko, Y and Gordienko, N and Taran, V and Rojbi, A and Telenyk, S and Stirenko, S}, title = {Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1521805}, pmid = {40083893}, issn = {1662-5196}, abstract = {Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.}, }
@article {pmid40083152, year = {2025}, author = {Liu, H and Bai, Y and Zheng, Q and Zhao, R and Guo, M and Zhu, J and Ni, G}, title = {Effects of spatial separation and background noise on brain functional connectivity during auditory selective spatial attention.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {35}, number = {3}, pages = {}, doi = {10.1093/cercor/bhaf054}, pmid = {40083152}, issn = {1460-2199}, support = {2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 2023YFF1203500//National Key Research and Development Program of China/ ; }, mesh = {Humans ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; Young Adult ; *Noise ; *Auditory Perception/physiology ; *Magnetic Resonance Imaging ; *Space Perception/physiology ; *Acoustic Stimulation/methods ; Adult ; Neural Pathways/physiology ; Brain Mapping/methods ; Signal-To-Noise Ratio ; }, abstract = {Auditory selective spatial attention (ASSA) plays an important role in "cocktail party" scenes, but the effects of spatial separation between target and distractor sources and background noise on the associated brain responses have not been thoroughly investigated. This study utilized the multilayer time-varying brain network to reveal the effect patterns of different separation degrees and signal-to-noise ratio (SNR) levels on brain functional connectivity during ASSA. Specifically, a multilayer time-varying brain network with six time-windows equally divided by each epoch was constructed to investigate the segregation and integration of brain functional connectivity. The results showed that the inter-layer connectivity strength was consistently lower than the intra-layer connectivity strength for various separation degrees and SNR levels. Moreover, the connectivity strength of the multilayer time-varying brain network increased with decreasing separation degrees and initially increased and subsequently decreased with decreasing SNR levels. The second time-window of the network showed the most significant variation under some conditions and was determined as the core layer. The topology within the core layer was mainly reflected in the connectivity between the frontal and parietal-occipital cortices. In conclusion, these results suggest that spatial separation and background noise significantly modulate brain functional connectivity during ASSA.}, }
@article {pmid40083123, year = {2025}, author = {Meng, M and Chen, G and Chen, S and Ma, Y and Gao, Y and Luo, Z}, title = {DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/10255842.2025.2476184}, pmid = {40083123}, issn = {1476-8259}, abstract = {Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.}, }
@article {pmid40082907, year = {2025}, author = {Bonato, P and Reinkensmeyer, D and Manto, M}, title = {Two decades of breakthroughs: charting the future of NeuroEngineering and Rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {59}, pmid = {40082907}, issn = {1743-0003}, mesh = {Humans ; *Neurological Rehabilitation/instrumentation/trends/methods ; *Biomedical Engineering/trends/methods/instrumentation ; Periodicals as Topic/trends ; Brain-Computer Interfaces/trends ; Forecasting ; Robotics/trends/instrumentation/methods ; }, abstract = {The Journal of NeuroEngineering and Rehabilitation (JNER) has become a major actor for the dissemination of knowledge in the scientific community, bridging the gaps between innovative neuroengineering and rehabilitation. Major fields of innovations have emerged these last 25 years, such as machine learning and the ongoing AI revolution, wearable technologies, human machine interfaces, robotics, advanced prosthetics, functional electrical stimulation and various neuromodulation techniques. With the major burden of disorders impacting on the central/peripheral nervous system and the musculoskeletal system both in adults and in children, successful tailored neurorehabilitation has become a major objective for the research and clinical community at a world scale. JNER contributes to this challenging goal, publishing groundbreaking cutting-edge research using the open access model. The multidisciplinary approaches at the crossroads of biomedical engineering, neuroscience, physical medicine and rehabilitation make of the journal a unique growing platform welcoming breakthrough discoveries to reshape the field and restore function.}, }
@article {pmid40082683, year = {2025}, author = {Verwoert, M and Amigó-Vega, J and Gao, Y and Ottenhoff, MC and Kubben, PL and Herff, C}, title = {Whole-brain dynamics of articulatory, acoustic and semantic speech representations.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {432}, pmid = {40082683}, issn = {2399-3642}, support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)/ ; }, mesh = {Humans ; *Speech/physiology ; Male ; *Brain/physiology ; *Semantics ; Female ; Adult ; Young Adult ; Speech Acoustics ; Brain Mapping ; }, abstract = {Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, ultimately, to the produced audio waveform (acoustic). In this study, we identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. Intracranial neural data is recorded from 15 participants, across 1647 electrode contacts, while overtly speaking 100 unique words. We find a bilateral spatial distribution for all three representations, with a more widespread and temporally dynamic distribution in the left compared to the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation is more widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production neural process and the potential of non-motor representations for speech brain-computer interfaces.}, }
@article {pmid40082601, year = {2025}, author = {Wang, Y and Yang, Z and Shi, X and Han, H and Li, AN and Zhang, B and Yuan, W and Sun, YH and Li, XM and Lian, H and Li, MD}, title = {Investigating the effect of Arvcf reveals an essential role on regulating the mesolimbic dopamine signaling-mediated nicotine reward.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {429}, pmid = {40082601}, issn = {2399-3642}, mesh = {Animals ; *Dopamine/metabolism ; *Reward ; *Nicotine/pharmacology ; Mice ; Male ; *Ventral Tegmental Area/metabolism/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Mice, Knockout ; *Nucleus Accumbens/metabolism/drug effects ; Signal Transduction ; Mice, Inbred C57BL ; }, abstract = {The mesolimbic dopamine system is crucial for drug reinforcement and reward learning, leading to addiction. We previously demonstrated that Arvcf was associated significantly with nicotine and alcohol addiction through genome-wide association studies. However, the role and mechanisms of Arvcf in dopamine-mediated drug reward processes were largely unknown. In this study, we first showed that Arvcf mediates nicotine-induced reward behavior by using conditioned place preference (CPP) model on Arvcf-knockout (Arvcf-KO) animal model. Then, we revealed that Arvcf was mainly expressed in VTA dopaminergic neurons whose expression could be upregulated by nicotine treatment. Subsequently, our SnRNA-seq analysis revealed that Arvcf was directly involved in dopamine biosynthesis in VTA dopaminergic neurons. Furthermore, we found that Arvcf-KO led to a significant reduction in both the dopamine synthesis and release in the nucleus accumbens (NAc) on nicotine stimulation. Specifically, we demonstrated that inhibition of Arvcf in VTA dopaminergic neurons decreased dopamine release within VTA-NAc circuit and suppressed nicotine reward-related behavior, while overexpression of Arvcf led to the opposite results. Taken together, these findings highlight the role of Arvcf in regulating dopamine signaling and reward learning, and its enhancement of dopamine release in the VTA-NAc circuit as a novel mechanism for nicotine reward.}, }
@article {pmid40082534, year = {2025}, author = {Muniyandi, AP and Padmanandam, K and Subbaraj, K and Khadidos, AO and Khadidos, AO and Deepa, N and Selvarajan, S}, title = {An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {8782}, pmid = {40082534}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Animals ; }, abstract = {Emotion recognition and prediction plays a vital role in human-computer interaction (HCI), offering more potential for efficient intuitive and adaptive systems. This presents an innovative and efficient approach for emotion prediction from electroencephalogram (EEG) signals by using an Improved Sand Cat Optimization (ISCO) technique to enhance prediction accuracy and efficiency. EEG signals directly indicates the brain activity and these signals are rich and reliable source of data for capturing emotional states. The proposed method is improved by adapting the Cat movement which uses convex lens opposition based learning technique and this will enhance the Cat movement towards target. The proposed method converges to target identification quickly for achieving efficient emotion prediction by extending the exiting Sand Cat Optimization algorithm. The algorithm has been evaluated by using openly available EEG signals dataset, which contains 2132 labelled records of three categories of emotional classes. The performance of the proposed method is compared with other nature inspired optimization algorithms such as Practical Swam Optimization (PSO), Artificial Rabbit Optimization (ARO), Artificial Bee Colony Optimization (ABCO), and Cat Optimization (CO) algorithm. The experimental evaluation shows that the proposed technique outperforms and showcases significant improvements in emotion prediction with accuracy of 97.5% compared to the other bioinspired optimization techniques. This research article has a scope to contribute to the advancement of emotion prediction system in the field of mental health care monitoring, HCI systems, gaming systems, and affective computing.}, }
@article {pmid40081769, year = {2025}, author = {Yin, Y and Cao, Y and Zhou, Y and Xu, Z and Luo, P and Yang, B and He, Q and Yan, H and Yang, X}, title = {Downregulation of DDIT4 levels with borneol attenuates hepatotoxicity induced by gilteritinib.}, journal = {Biochemical pharmacology}, volume = {236}, number = {}, pages = {116869}, doi = {10.1016/j.bcp.2025.116869}, pmid = {40081769}, issn = {1873-2968}, mesh = {*Chemical and Drug Induced Liver Injury/metabolism/prevention &amp; control/drug therapy ; Animals ; *Down-Regulation/drug effects/physiology ; Humans ; *Aniline Compounds/toxicity ; *Pyrazines/toxicity ; *Transcription Factors/antagonists &amp; inhibitors/metabolism/genetics ; Male ; Mice ; Dose-Response Relationship, Drug ; Hepatocytes/drug effects/metabolism ; Protein Kinase Inhibitors/toxicity ; Hep G2 Cells ; Apoptosis/drug effects ; }, abstract = {Gilteritinib, a multi-target kinase inhibitor, is currently used as standard therapy for acute myeloid leukemia. However, approximately half of the patients encounter liver-related adverse effects during the treatment with gilteritinib, which limiting its clinical applications. The underlying mechanisms of gilteritinib-induced hepatotoxicity and the development of strategies to prevent this toxicity are not well-reported. In our study, we utilized JC-1 dye, and MitoSOX to demonstrate that gilteritinib treatment leads to hepatocytes undergoing p53-mediated mitochondrial apoptosis. Furthermore, qRT-PCR analysis revealed that DNA damage-inducible transcript 4 (DDIT4), a downstream target of p53, was upregulated following gilteritinib administration and was identified as a key factor in gilteritinib-induced hepatotoxicity. After drug screening and western blot analysis, borneol, a bicyclic monoterpenoid, was found to decrease the protein level of DDIT4. This is the first compound found to downregulate DDIT4 levels and ameliorate hepatic injury caused by gilteritinib. Our findings suggest that high levels of DDIT4 are the primary driver behind gilteritinib-induced liver injury, and that borneol could potentially be a clinically safe and feasible therapeutic strategy by inhibiting DDIT4 levels.}, }
@article {pmid40081503, year = {2025}, author = {Xu, F and Lou, Y and Deng, Y and Lun, Z and Zhao, P and Yan, D and Han, Z and Wu, Z and Feng, C and Chen, L and Leng, J}, title = {Motor imagery EEG decoding based on TS-former for spinal cord injury patients.}, journal = {Brain research bulletin}, volume = {224}, number = {}, pages = {111298}, doi = {10.1016/j.brainresbull.2025.111298}, pmid = {40081503}, issn = {1873-2747}, mesh = {Humans ; *Electroencephalography/methods ; *Spinal Cord Injuries/physiopathology/rehabilitation ; *Imagination/physiology ; Neural Networks, Computer ; Adult ; Machine Learning ; Male ; Brain-Computer Interfaces ; Female ; Signal Processing, Computer-Assisted ; Middle Aged ; Motor Activity/physiology ; }, abstract = {Traditional machine learning methods struggle with efficiency when processing large-scale data, while deep learning approaches, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), exhibit certain limitations when handling long-duration sequences. The choice of convolutional kernel size needs to be determined after several experiments, and LSTM has difficulty capturing effective information from long-time sequences. In this paper, we propose a transfer learning (TL) method based on Transformer, which constructs a new network architecture for feature extraction and classification of electroencephalogram (EEG) signals in the time-space domain, named TS-former. The frequency and spatial domain information of EEG signals is extracted using the Filter Bank Common Spatial Pattern (FBCSP), and the resulting features are subsequently processed by the Transformer to capture temporal patterns. The input features are processed by the Transformer using a multi-head attention mechanism, and the final classification outputs are generated through a fully connected layer. A classification model is pre-trained using fine-tuning techniques. When performing a new classification task, only some layers of the model are modified to adapt it to the new data and achieve good classification results. The experiments are conducted on a motor imagery (MI) EEG dataset from 16 spinal cord injury (SCI) patients. After training the model using a ten-time ten-fold cross-validation method, the average classification accuracy reached 95.09 %. Our experimental results confirm a new approach to build a brain-computer interface (BCI) system for rehabilitation training of SCI patients.}, }
@article {pmid40079091, year = {2025}, author = {Zhang, Y and Coid, J}, title = {Testing syndemic models along pathways to psychotic spectrum disorder: implications for population-level preventive interventions.}, journal = {Psychological medicine}, volume = {55}, number = {}, pages = {e85}, doi = {10.1017/S0033291725000455}, pmid = {40079091}, issn = {1469-8978}, mesh = {Humans ; Male ; *Psychotic Disorders/prevention &amp; control ; Adult ; *Syndemic ; Cross-Sectional Studies ; Young Adult ; *Violence/prevention &amp; control/psychology ; Substance-Related Disorders/prevention &amp; control ; United Kingdom ; Middle Aged ; Adolescent ; Sexual Behavior ; Crime/prevention &amp; control/statistics &amp; numerical data/psychology ; Adverse Childhood Experiences/statistics &amp; numerical data ; }, abstract = {BACKGROUND: Population-level preventive interventions are urgently needed and may be effective for psychosis due to social determinants. We tested three syndemic models along pathways from childhood adversity (CA) to psychotic spectrum disorder (PSD) and their implications for prevention.<br><br>METHODS: Cross-sectional data from 7461 British men surveyed in 5 population subgroups. We tested interactions on both additive and multiplicative scales for a syndemic of violence/criminality (VC), sexual behavior (SH), and substance misuse (SM) according to the presence of CA and adult traumatic life events; mediation analysis of path models; and partial least squares path modeling, with PSD as outcome.<br><br>RESULTS: Multiplicative synergistic interactions were found between VC, SH, and SM among men, who experienced CA and traumatic adult life events. However, when disaggregated, only SM mediated the pathway from CA to PSD. Path modeling showed traumatic life events acted on PSD through the syndemic and had no direct effect on PSD. Higher syndemic scores and living in areas of deprivation characterized men with PSD and CA.<br><br>CONCLUSIONS: Our findings support a broad division of PSD into cases due to (i) biological/inherent causes, and (ii) social determinants, the latter including a syndemic pathway determined by CA. Preventive strategies should focus primarily on preventing adverse effects of CA on developmental pathways which result in PSD. Single component prevention strategies may prevent triggering effects of SM on PSD during adolescence/early adulthood among vulnerable individuals due to CA. Future research should determine applicability and transferability of interventions based on these findings to different populations, specifically those experiencing syndemics.}, }
@article {pmid40078487, year = {2025}, author = {Li, M and Yu, P and Shen, Y}, title = {A spatial and temporal transformer-based EEG emotion recognition in VR environment.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1517273}, pmid = {40078487}, issn = {1662-5161}, abstract = {With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios. Moreover, emotion recognition models are typically trained and tested on datasets collected in laboratory environments, with little validation of their effectiveness in real-world situations. VR, providing a highly immersive and realistic experience, is an ideal tool for emotional research. In this paper, we collect EEG data from participants while they watched VR videos. We propose a purely Transformer-based method, EmoSTT. We use two separate Transformer modules to comprehensively model the temporal and spatial information of EEG signals. We validate the effectiveness of EmoSTT on a passive paradigm collected in a laboratory environment and an active paradigm emotion dataset collected in a VR environment. Compared with state-of-the-art methods, our method achieves robust emotion classification performance and can be well transferred between different emotion elicitation paradigms.}, }
@article {pmid40074408, year = {2025}, author = {Zhang, C and Pu, Y and Kong, XZ}, title = {Latent dimensions of brain asymmetry.}, journal = {Handbook of clinical neurology}, volume = {208}, number = {}, pages = {37-45}, doi = {10.1016/B978-0-443-15646-5.00027-0}, pmid = {40074408}, issn = {0072-9752}, mesh = {Humans ; *Functional Laterality/physiology ; *Brain/physiology/anatomy &amp; histology ; }, abstract = {Functional lateralization represents a fundamental aspect of brain organization, where certain cognitive functions are specialized in one hemisphere over the other. Deviations from typical patterns of lateralization often manifest in various brain disorders, such as autism spectrum disorder, schizophrenia, and dyslexia. However, despite its importance, uncovering the intrinsic properties of brain lateralization and its underlying structural basis remains challenging. On the one hand, functional lateralization has long been oversimplified, often reduced to a unidimensional perspective. For instance, individuals are sometimes labeled as left-brained or right-brained based on specific behavioral measures like handedness and language lateralization. Such a perspective disregards the nuanced subtypes of lateralization, each potentially attributed to distinct factors and associated with unique functional correlates. On the other hand, traditional studies of brain structural asymmetry have typically focused on localized analyses of homologous regions in the two hemispheres. This perspective fails to capture the inherent interplay between brain regions, resulting in an overly complex depiction of structural asymmetry. Such conceptual and methodological discrepancies between studies of functional lateralization and structural asymmetry pose significant obstacles to establishing meaningful links between them. To address this gap, a shift toward uncovering the dimensional structure of brain asymmetry has been proposed. This chapter introduces the concept of latent dimensions of brain asymmetry and provides an up-to-date overview of studies regarding dimensions of functional lateralization and structural asymmetry in the human brain. By transcending the traditional analysis and employing multivariate pattern techniques, these studies offer valuable insights into our understanding of the intricate organizational principles governing the human brain's lateralized functions.}, }
@article {pmid40073454, year = {2025}, author = {Pei, Y and Zhao, S and Xie, L and Ji, B and Luo, Z and Ma, C and Gao, K and Wang, X and Sheng, T and Yan, Y and Yin, E}, title = {Toward the enhancement of affective brain-computer interfaces using dependence within EEG series.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbfc0}, pmid = {40073454}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Affect/physiology ; Reproducibility of Results ; Emotions/physiology ; Algorithms ; }, abstract = {In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances.Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs.Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test.Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier's outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy.Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.}, }
@article {pmid40073451, year = {2025}, author = {Dong, Y and Zheng, L and Pei, W and Gao, X and Wang, Y}, title = {A 240-target VEP-based BCI system employing narrow-band random sequences.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbfc1}, pmid = {40073451}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; Algorithms ; Neural Networks, Computer ; }, abstract = {Objective.In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.Approach. We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm. This algorithm predicts corresponding stimulus sequences from EEG data and achieves efficient and accurate classification.Main results.Offline experiments which conducted in a sequential paradigm, resulted in an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min. In online experiments, the system demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min in a cued spelling task.Significance.This work represents an advancement in c-VEP BCI systems, offering one of the largest known instruction set in VEP-based BCIs and demonstrating robust performance metrics. The proposed system is potential for more practical and efficient BCI applications.}, }
@article {pmid40072857, year = {2025}, author = {Ji, D and Huang, Y and Chen, Z and Zhou, X and Wang, J and Xiao, X and Xu, M and Ming, D}, title = {Enhanced Spatial Division Multiple Access BCI Performance via Incorporating MEG With EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1202-1211}, doi = {10.1109/TNSRE.2025.3550653}, pmid = {40072857}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Algorithms ; Male ; Adult ; Female ; Young Adult ; Reproducibility of Results ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Visual Fields/physiology ; }, abstract = {Spatial division multiple access (SDMA) is a way of encoding BCI systems based on spatial distribution of brain signal characteristics. However, SDMA-BCI based on EEG had poor system performance limited by spatial resolution. MEG-EEG fusion modality analysis can help solve this problem. According retina-cortical relationship, this study used stimulus out of the central visual field and tiny fixation points to construct a 16-command SDMA coded MEG-EEG fusion modality BCI system. We achieved this by synchronously acquiring MEG and EEG signals from 10 subjects. We compared the spatiotemporal features between MEG and EEG by analyzing signals in the occipital region. We fused MEG and EEG modalities without any signal processing and used the multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm to evaluate and compare the system performance of EEG, MEG, and MEG-EEG fusion modalities. The result showed that MEG and EEG had obvious differences in spatial distribution characteristics. MEG improves offline classification accuracy of the 16 fixation points by 27.81% over EEG at 4s data length. Specially, the MEG-EEG fusion modality achieves an impressive average offline accuracy of 91.71%, which was a significant improvement over MEG (p<0.01, ANOVA). The MEG-EEG fusion modality achieved average information transfer rate (ITR) of 60.74 bits/min with a data length of 1 s, which was a 14% improvement over MEG. The MEG-EEG fusion modality significantly enhanced the spatial features and performance of SDMA-encoded BCIs. These results highlight the potential and feasibility of MEG-EEG fusion modality BCI, and provide theoretical insights and practical value for promoting the further development and application of SDMA in BCI.}, }
@article {pmid40072172, year = {2025}, author = {Zhang, C and Zhang, C and Liu, Y}, title = {Progress in the Development of Flexible Devices Utilizing Protein Nanomaterials.}, journal = {Nanomaterials (Basel, Switzerland)}, volume = {15}, number = {5}, pages = {}, pmid = {40072172}, issn = {2079-4991}, support = {52473014//National Natural Science Foundation of China/ ; }, abstract = {Flexible devices are soft, lightweight, and portable, making them suitable for large-area applications. These features significantly expand the scope of electronic devices and demonstrate their unique value in various fields, including smart wearable devices, medical and health monitoring, human-computer interaction, and brain-computer interfaces. Protein materials, due to their unique molecular structure, biological properties, sustainability, self-assembly ability, and good biocompatibility, can be applied in electronic devices to significantly enhance the sensitivity, stability, mechanical strength, energy density, and conductivity of the devices. Protein-based flexible devices have become an important research direction in the fields of bioelectronics and smart wearables, providing new material support for the development of more environmentally friendly and reliable flexible electronics. Currently, many proteins, such as silk fibroin, collagen, ferritin, and so on, have been used in biosensors, memristors, energy storage devices, and power generation devices. Therefore, in this paper, we provide an overview of related research in the field of protein-based flexible devices, including the concept and characteristics of protein-based flexible devices, fabrication materials, fabrication processes, characterization, and evaluation, and we point out the future development direction of protein-based flexible devices.}, }
@article {pmid40071135, year = {2025}, author = {Kobayashi, N and Ino, M}, title = {Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1469244}, pmid = {40071135}, issn = {1662-4548}, abstract = {Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. It is also expected to reduce the workload of care workers. In this study, we focused on motor imagery (MI) classification by deep-learning to construct a system that can identify MI obtained by electroencephalography (EEG) measurements with high accuracy and a low latency response. By completing the system on the edge, the privacy of personal MI data can be ensured, and the system is ubiquitous, which improves user convenience. On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. Therefore, by optimizing the MI measurement conditions and various parameters of the deep-learning model, we attempted to reduce the power consumption and improve the response latency of the system by minimizing the computational cost while maintaining high classification accuracy. In addition, we investigated the use of a 3-dimension convolutional neural network (3D CNN), which can retain spatial locality as a feature to further improve the classification accuracy. We propose a method to maintain a high classification accuracy while enabling processing on the edge by optimizing the size and number of kernels and the layer structure. Furthermore, to develop a practical BMI system, we introduced dry electrodes, which are more comfortable for daily use, and optimized the number of parameters and memory consumption size of the proposed model to maintain classification accuracy even with fewer electrodes, less recall time, and a lower sampling rate. Compared to EEGNet, the proposed 3D CNN reduces the number of parameters, the number of multiply-accumulates, and memory footprint by approximately 75.9%, 16.3%, and 12.5%, respectively, while maintaining the same level of classification accuracy with the conditions of eight electrodes, 3.5 seconds sample window size, and 125 Hz sampling rate in 4-class dry-EEG MI.}, }
@article {pmid40070670, year = {2025}, author = {Pan, H and Tang, C and Song, C and Li, J}, title = {Analysis of clinical efficacy of sacral magnetic stimulation for the treatment of detrusor underactivity.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1499310}, pmid = {40070670}, issn = {1664-2295}, abstract = {OBJECTIVE: The objective of this study was to investigate the effectiveness and safety of sacral magnetic stimulation (SMS) in the management of detrusor underactivity (DU).<br><br>METHODS: We retrospectively analyzed 66 patients with detrusor underactivity treated at Hangzhou Third People's Hospital from January 2020 to October 2024, divided into two groups (33 cases each). Both groups had confirmed detrusor underactivity via urodynamic studies. The control group received conventional treatment (medication, catheterization, bladder training), while the observation group received SMS therapy. Urination diaries, urodynamic parameters and self-rating anxiety scale (SAS) were collected before and after the 4-week treatment to evaluate SMS efficacy and safety.<br><br>RESULTS: All patients in the observation group completed the course of sacral magnetic stimulation without experiencing any serious complications. After treatment, the observation group showed a significant reduction in the number of daily urinations, nocturnal urinations, SAS score and residual urine volume (RUV) (p <&#x202f;0.05) compared with the control group. There was no statistically significant difference in maximum cystometric capacity (MCC) (p >&#x202f;0.05). However, improvements were observed in SAS score, Detrusor Pressure at Maximum Flow (Pdet), Bladder Contractility Index (BCI), Maximum urinary Flow Rate (Qmax) and Average Urinary Flow Rate (Qavg) (p <&#x202f;0.05). The effective rate in the observation group was 78.78%, significantly higher than that in the control group (p <&#x202f;0.05). Although there was a slight decrease in the effective rate during the 6-month follow-up, the difference was not statistically significant (p >&#x202f;0.05).<br><br>CONCLUSION: In conclusion, sacral magnetic stimulation therapy has demonstrated effectiveness in improving urinary function in patients with detrusor underactivity while maintaining a high level of safety.}, }
@article {pmid40069360, year = {2025}, author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X}, title = {Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.}, journal = {Nature neuroscience}, volume = {28}, number = {4}, pages = {748-756}, pmid = {40069360}, issn = {1546-1726}, support = {2019YFA0508701//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2022YFA1303300//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, mesh = {*Amyotrophic Lateral Sclerosis/genetics/pathology/metabolism ; Animals ; Rats ; Motor Neurons/metabolism/pathology ; *Electron Transport Complex IV/genetics ; *Mitochondria/metabolism/genetics ; Humans ; Disease Models, Animal ; Mutation/genetics ; }, abstract = {Amyotrophic lateral sclerosis (ALS) is categorized into ~10% familial and ~90% sporadic cases. While familial ALS is caused by mutations in many genes of diverse functions, the underlying pathogenic mechanisms of ALS, especially in sporadic ALS (sALS), are largely unknown. Notably, about half of the cases with sALS showed defects in mitochondrial respiratory complex IV (CIV). To determine the causal role of this defect in ALS, we used transcription activator-like effector-based mitochondrial genome editing to introduce mutations in CIV subunits in rat neurons. Our results demonstrate that neuronal CIV deficiency is sufficient to cause a number of ALS-like phenotypes, including cytosolic TAR DNA-binding protein 43 redistribution, selective motor neuron loss and paralysis. These results highlight CIV deficiency as a potential cause of sALS and shed light on the specific vulnerability of motor neurons, marking an important advance in understanding and therapeutic development of sALS.}, }
@article {pmid40068721, year = {2025}, author = {Kopalli, SR and Shukla, M and Jayaprakash, B and Kundlas, M and Srivastava, A and Jagtap, J and Gulati, M and Chigurupati, S and Ibrahim, E and Khandige, PS and Garcia, DS and Koppula, S and Gasmi, A}, title = {Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.}, journal = {Neuroscience}, volume = {572}, number = {}, pages = {214-231}, doi = {10.1016/j.neuroscience.2025.03.017}, pmid = {40068721}, issn = {1873-7544}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Recovery of Function/physiology ; *Stroke/therapy/diagnosis ; Brain-Computer Interfaces ; }, abstract = {Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.}, }
@article {pmid40068108, year = {2025}, author = {Li, Y and Li, H and Wang, H and Wang, X}, title = {Utilizing Caenorhabditis Elegans as a Rapid and Precise Model for Assessing Amphetamine-Type Stimulants: A Novel Approach to Evaluating New Psychoactive Substances Activity and Mechanisms.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2500808}, doi = {10.1002/advs.202500808}, pmid = {40068108}, issn = {2198-3844}, support = {T2341003//National Natural Science Foundation of China/ ; T2394480(T2394483)//National Natural Science Foundation of China/ ; 2021ZD0203000(2021ZD0203003)//STI2030-Major Projects/ ; 20240304115SF//Science and Technology Development Plan Project of Jilin Province/ ; XDB0450102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; }, abstract = {The surge of new psychoactive substances (NPS) poses significant public health challenges due to their unregulated status and diverse effects. However, existing in vivo models for evaluating their activities are limited. To address this gap, this study utilizes the model organism Caenorhabditis elegans (C. elegans) to evaluate the activity of amphetamine-type stimulants (ATS) and their analogs. The swimming-induced paralysis (SWIP) assay is employed to measure the acute responses of C. elegans to various ATS, including amphetamine (AMPH), methamphetamine (METH), 3,4-methylenedioxymethamphetamine (MDMA) and their enantiomers. The findings reveal distinct responses in wild-type and mutant C. elegans, highlighting the roles of dopaminergic and serotonergic pathways, particularly DOP-3 and SER-4 receptors. The assay also revealed that C. elegans can distinguish between the chiral forms of ATS. Additionally, structural activity relationships (SAR) are observed, with meta-R amphetamines showing more pronounced effects than ortho-R and para-R analogs. This study demonstrates the utility of C. elegans in rapidly assessing ATS activity and toxicity, providing a cost-effective and precise method for high-throughput testing of NPS. These results contribute to a better understanding of ATS pharmacology and offer a valuable framework for future research and potential regulatory applications.}, }
@article {pmid40067734, year = {2025}, author = {Wang, X and Qi, W and Yang, W and Wang, W}, title = {Cholesky Space for Brain-Computer Interfaces.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2025.3542801}, pmid = {40067734}, issn = {2162-2388}, abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms. Our proposed Cholesky space-based model, CSNet, achieves state-of-the-art (SOTA) performance in motor imagery (MI) decoding and emotion recognition and demonstrates competitive performance in error-related negativity (ERN) decoding, all without the need for data preprocessing. Furthermore, our runtime comparison shows that the Cholesky space method is more efficient than the method based on the Riemannian manifold as the matrix dimension increases. To enhance the interpretability of CSNet, we perform t-distributed stochastic neighbor embedding (t-SNE) visualization for MI, frequency band energy visualization for emotion recognition, and temporal importance visualization for ERN. The results indicate that CSNet effectively learns discriminative features, identifies important frequency bands, and focuses on important temporal features. The CSNet effectively captures the non-Euclidean characteristics of EEG signals across various BCI paradigms, while mitigating high computational costs, making it a promising candidate for future BCI algorithms. The code for this study is publicly available at: https://github.com/XingfuWang/CSNet.}, }
@article {pmid40067717, year = {2025}, author = {Du, Y and Chen, J and Liu, Z and Wong, N and Zhang, C and Ding, Z and Liu, J and Ngai, ECH}, title = {Valence-Arousal Disentangled Representation Learning for Emotion Recognition in SSVEP-Based BCIs.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3549727}, pmid = {40067717}, issn = {2168-2208}, abstract = {Steady state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which are widely used in rehabilitation and disability assistance, can benefit from real-time emotion recognition to enhance human-machine interaction. However, the learned discriminative latent representations in SSVEP-BCIs may generalize in an unintended direction, which can lead to reduced accuracy in detecting emotional states. In this paper, we introduce a Valence-Arousal Disentangled Representation Learning (VADL) method, drawing inspiration from the classical two-dimensional emotional model, to enhance the performance and generalization of emotion recognition within SSVEP-BCIs. VADL distinctly disentangles the latent variables of valence and arousal information to improve accuracy. It utilizes the structured state space duality model to thoroughly extract global emotional features. Additionally, we propose a Multisubject Gradient Blending training strategy that individually tailors the learning pace of reconstruction and discrimination tasks within VADL on-the-fly. To verify the feasibility of our method, we have developed a comprehensive database comprising 23 subjects, in which both the emotional states and SSVEPs were effectively elicited. Experimental results indicate that VADL surpasses existing state-of-the-art benchmark algorithms.}, }
@article {pmid40064104, year = {2025}, author = {Rodríguez-García, ME and Carino-Escobar, RI and Carrillo-Mora, P and Hernandez-Arenas, C and Ramirez-Nava, AG and Pacheco-Gallegos, MDR and Valdés-Cristerna, R and Cantillo-Negrete, J}, title = {Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbebf}, pmid = {40064104}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; *Neuronal Plasticity/physiology ; Middle Aged ; *Upper Extremity ; Aged ; *Stroke/physiopathology ; *White Matter/diagnostic imaging/physiopathology ; *Gray Matter/diagnostic imaging/physiopathology ; Electroencephalography/methods ; Adult ; Magnetic Resonance Imaging/methods ; Cerebral Cortex/physiopathology/diagnostic imaging/physiology ; Diffusion Tensor Imaging/methods ; Recovery of Function/physiology ; }, abstract = {Objective. Upper extremity (UE) motor function loss is one of the most impactful consequences of stroke. Recently, brain-computer interface (BCI) systems have been utilized in therapy programs to enhance UE motor recovery after stroke, widely attributed to neuroplasticity mechanisms. However, the effect that the BCI's closed-loop feedback can have in these programs is unclear. The aim of this study was to quantitatively assess and compare the neuroplasticity effects elicited in stroke patients by a UE motor rehabilitation BCI therapy and by its sham-BCI counterpart.Approach. Twenty patients were randomly assigned to either the experimental group (EG), who controlled the BCI system via UE motor intention, or the control group (CG), who received random feedback. The elicited neuroplasticity effects were quantified using asymmetry metrics derived from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) data acquired before, at the middle, and at the end of the intervention, alongside UE sensorimotor function evaluations. These asymmetry indexes compare the affected and unaffected hemispheres and are robust to lesion location variability.Main results. Most patients from the EG presented brain activity lateralisation to one brain hemisphere, as described by EEG (8 patients) and fMRI (6 patients) metrics. Conversely, the CG showed less pronounced lateralisations, presenting primarily bilateral activity patterns. DTI metrics showed increased white matter integrity in half of the EG patients' unaffected hemisphere, and in all but 2 CG patients' affected hemisphere. Individual patient analysis suggested that lesion location was relevant since functional and structural lateralisations occurred towards different hemispheres depending on stroke site.Significance. This study shows that a BCI intervention can elicit more pronounced neuroplasticity-related lateralisations than a sham-BCI therapy. These findings could serve as future biomarkers, helping to better select patients and increasing the impact that a BCI intervention can achieve. Clinical trial: NCT04724824.}, }
@article {pmid40064095, year = {2025}, author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB}, title = {Feasibility of source-level motor imagery classification for people with multiple sclerosis.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbec1}, pmid = {40064095}, issn = {1741-2552}, mesh = {Humans ; *Multiple Sclerosis/physiopathology/classification ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; *Imagination/physiology ; Middle Aged ; *Feasibility Studies ; Movement/physiology ; Electroencephalography/methods ; }, abstract = {Objective.There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Approach.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Main Results.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.Significance.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.}, }
@article {pmid40063703, year = {2025}, author = {Ahmed, AAA and Alegret, N and Almeida, B and Alvarez-Puebla, R and Andrews, AM and Ballerini, L and Barrios-Capuchino, JJ and Becker, C and Blick, RH and Bonakdar, S and Chakraborty, I and Chen, X and Cheon, J and Chilla, G and Coelho Conceicao, AL and Delehanty, J and Dulle, M and Efros, AL and Epple, M and Fedyk, M and Feliu, N and Feng, M and Fernández-Chacón, R and Fernandez-Cuesta, I and Fertig, N and Förster, S and Garrido, JA and George, M and Guse, AH and Hampp, N and Harberts, J and Han, J and Heekeren, HR and Hofmann, UG and Holzapfel, M and Hosseinkazemi, H and Huang, Y and Huber, P and Hyeon, T and Ingebrandt, S and Ienca, M and Iske, A and Kang, Y and Kasieczka, G and Kim, DH and Kostarelos, K and Lee, JH and Lin, KW and Liu, S and Liu, X and Liu, Y and Lohr, C and Mailänder, V and Maffongelli, L and Megahed, S and Mews, A and Mutas, M and Nack, L and Nakatsuka, N and Oertner, TG and Offenhäusser, A and Oheim, M and Otange, B and Otto, F and Patrono, E and Peng, B and Picchiotti, A and Pierini, F and Pötter-Nerger, M and Pozzi, M and Pralle, A and Prato, M and Qi, B and Ramos-Cabrer, P and Genger, UR and Ritter, N and Rittner, M and Roy, S and Santoro, F and Schuck, NW and Schulz, F and Şeker, E and Skiba, M and Sosniok, M and Stephan, H and Wang, R and Wang, T and Wegner, KD and Weiss, PS and Xu, M and Yang, C and Zargarian, SS and Zeng, Y and Zhou, Y and Zhu, D and Zierold, R and Parak, WJ}, title = {Interfacing with the Brain: How Nanotechnology Can Contribute.}, journal = {ACS nano}, volume = {19}, number = {11}, pages = {10630-10717}, pmid = {40063703}, issn = {1936-086X}, support = {R01 MH111872/MH/NIMH NIH HHS/United States ; R01 MH094730/MH/NIMH NIH HHS/United States ; R01 DA045550/DA/NIDA NIH HHS/United States ; R03 NS118156/NS/NINDS NIH HHS/United States ; R21 AT010933/AT/NCCIH NIH HHS/United States ; R61 MH135106/MH/NIMH NIH HHS/United States ; }, mesh = {*Nanotechnology ; Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; Animals ; }, abstract = {Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.}, }
@article {pmid40063028, year = {2025}, author = {Ben-Zion, Z and Simon, AJ and Rosenblatt, M and Korem, N and Duek, O and Liberzon, I and Shalev, AY and Hendler, T and Levy, I and Harpaz-Rotem, I and Scheinost, D}, title = {Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.}, journal = {JAMA network open}, volume = {8}, number = {3}, pages = {e250331}, pmid = {40063028}, issn = {2574-3805}, mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Female ; Adult ; Male ; *Connectome ; *Survivors/psychology/statistics &amp; numerical data ; *Magnetic Resonance Imaging ; Machine Learning ; Middle Aged ; Prognosis ; Israel/epidemiology ; Longitudinal Studies ; Wounds and Injuries/psychology/physiopathology/complications ; }, abstract = {IMPORTANCE: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.<br><br>OBJECTIVE: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.<br><br>This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors. The NMPTDT study was conducted from January 20, 2015, to March 11, 2020, and included adult civilians who were admitted to a general hospital emergency department in Israel and screened for early PTSD symptoms indicative of chronic PTSD risk. Enrolled participants completed comprehensive clinical assessments and functional magnetic resonance imaging (fMRI) scans at 1, 6, and 14 months post trauma. Data were analyzed from September 2023 to March 2024.<br><br>EXPOSURE: Traumatic events included motor vehicle incidents, physical assaults, robberies, hostilities, electric shocks, fires, drownings, work accidents, terror attacks, or large-scale disasters.<br><br>MAIN OUTCOMES AND MEASURES: Connectome-based predictive modeling (CPM), a whole-brain machine learning approach, was applied to resting-state and task-based fMRI data collected at 1 month post trauma. The primary outcome measure was PTSD symptom severity across the 3 time points, assessed with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Secondary outcomes included Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) PTSD symptom clusters (intrusion, avoidance, negative alterations in mood and cognition, hyperarousal).<br><br>RESULTS: A total of 162 recent trauma survivors (mean [SD] age,&#x2009;33.9&#x2009;[11.5] years; 80 women [49.4%] and 82 men [50.6%]) were included at 1 month post trauma. Follow-up assessments were completed by 136 survivors (84.0%) at 6 months and by 133 survivors (82.1%) at 14 months post trauma. Among the 162 recent trauma survivors, CPM significantly predicted PTSD severity at 1 month (&#x3c1;&#x2009;=&#x2009;0.18, P&#x2009;<&#x2009;.001) and 14 months (&#x3c1;&#x2009;=&#x2009;0.24, P&#x2009;<&#x2009;.001) post trauma, but not at 6 months post trauma (&#x3c1;&#x2009;=&#x2009;0.03, P&#x2009;=&#x2009;.39). The most predictive edges at 1 month included connections within and between the anterior default mode, motor sensory, and salience networks. These networks, with the additional contribution of the central executive and visual networks, were predictive of symptoms at 14 months. CPM predicted avoidance and negative alterations in mood and cognition at 1 month, but it predicted intrusion and hyperarousal symptoms at 14 months.<br><br>CONCLUSIONS AND RELEVANCE: In this prognostic study of recent trauma survivors, individual differences in large-scale neural networks shortly after trauma were associated with variability in PTSD symptom trajectories over the first year following trauma exposure. These findings suggest that CPM may identify potential targets for interventions.}, }
@article {pmid40062568, year = {2025}, author = {Pilipović, K and Parpura, V}, title = {The potential of single-walled carbon nanotube-based therapeutic platforms targeting astrocytes.}, journal = {Nanomedicine (London, England)}, volume = {}, number = {}, pages = {1-3}, doi = {10.1080/17435889.2025.2476376}, pmid = {40062568}, issn = {1748-6963}, }
@article {pmid40061257, year = {2025}, author = {Song, Y and Han, L and Zhang, T and Xu, B}, title = {Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1551656}, pmid = {40061257}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.}, }
@article {pmid40060989, year = {2025}, author = {Huang, J and Huang, L and Li, Y and Fang, F}, title = {A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.}, journal = {Journal of multidisciplinary healthcare}, volume = {18}, number = {}, pages = {1297-1317}, pmid = {40060989}, issn = {1178-2390}, abstract = {OBJECTIVE: This study aims to conduct a bibliometric analysis of the application of brain- computer interface (BCI) in rehabilitation medicine, assessing the current state, developmental trends, and future potential of this field. By systematically analyzing relevant literature, we seek to identify key research themes and enhance understanding of BCI technology in rehabilitation.<br><br>METHODS: We utilized bibliometric analysis tools such as VOSviewer and CiteSpace to screen and analyze 426 relevant articles from the Web of Science Core Collection (WoSCC) database. We quantitatively evaluated citation patterns, publication trends, and the collaboration networks of research institutions and authors to uncover research hotspots and frontier dynamics in the field.<br><br>RESULTS: The findings indicate a continuous increase in research publications since 2003, with a notable peak occurring between 2019 and 2021. The analysis revealed that motor imagery, motor recovery, and signal processing are the predominant research themes. Furthermore, the United States and China are leading in the publication volume related to BCI and rehabilitation medicine. Key research institutions include the University of T&#xfc;bingen and the New York State Department of Health, with significant contributions from scholars like Niels Birbaumer.<br><br>CONCLUSION: Although the current research on BCI in rehabilitation medicine shows significant potential and efficacy, further exploration of certain research directions is needed, along with the promotion of interdisciplinary collaboration to comprehensively address complex real-world issues such as motor function impairment. Future research should focus on optimizing training models, enhancing technical feasibility, and exploring home rehabilitation applications to facilitate the broader adoption of BCI technology in rehabilitation medicine.}, }
@article {pmid40060849, year = {2025}, author = {Castañeda-Valencia, G and Gama, LF and Panneerselvam, M and Vaiss, VS and Guedes, IA and Dardenne, LE and Costa, LT}, title = {Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds.}, journal = {ACS omega}, volume = {10}, number = {8}, pages = {8314-8335}, pmid = {40060849}, issn = {2470-1343}, abstract = {In this work, we performed a comprehensive benchmark study for the ground state of five small- and medium-sized platinum derivatives, PtH, PtCl, [PtCl4][2-], [Pt(NH3)4][2+], and cis-[Pt(NH3)2Cl2], in the gas phase and two cisplatin polymorphs in the solid phase. The benchmark encompassed 16 density functionals, including nonhybrids, hybrids, and double hybrids. Furthermore, Hartree-Fock (HF) and Post-HF by Møller-Plesset MP2 methods were also tested. Additionally, 11 basis sets were explored, comparing relativistic all-electron and RECP approaches. Our results indicate that the methodologies best suited for predicting structural parameters do not excel in predicting vibrational frequencies and vice versa. In the context of this theoretical framework, we also examine the derivation of partial atomic charges and bond charge increments (bci) as fundamental parameters within the MMFF94 classical force field. Our results show that the partial atomic charges of CHELPG present a slight charge fluctuation in Pt for all investigated levels of theory, and this behavior reproduces well the soft acid definition for Pt[2+], giving the best description of the chemical environment of platinum in the cisplatin complex. The average calculated bci values effectively capture the atomic charge variations in the chemical environment of Pt in the investigated species. The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. This methodology will be further implemented in the DockThor receptor-ligand docking program, allowing future molecular docking validations involving ligand compounds containing Pt atoms.}, }
@article {pmid40060267, year = {2025}, author = {Shawki, N and Napoli, A and Vargas-Irwin, CE and Thompson, CK and Donoghue, JP and Serruya, MD}, title = {Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1544397}, pmid = {40060267}, issn = {1662-5161}, abstract = {INTRODUCTION: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia.<br><br>METHODS: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function.<br><br>RESULTS: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains.<br><br>DISCUSSION: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.}, }
@article {pmid40059723, year = {2025}, author = {Han, JS and Jeon, MC and Lee, CM and Velasco, GC and Park, SY and Park, SN}, title = {Comparing Tinnitus Suppression in Asymmetric Hearing Loss and Single-Sided Deafness: Cochlear Versus Bone Conduction Implants.}, journal = {The Laryngoscope}, volume = {}, number = {}, pages = {}, doi = {10.1002/lary.32090}, pmid = {40059723}, issn = {1531-4995}, support = {RS-2024-00340841//National Research Foundation of Korea/ ; }, abstract = {OBJECTIVES: Implantable hearing devices, such as cochlear implants (CI) and bone conduction implants (BCI), are options for hearing rehabilitation in patients with asymmetric hearing loss (AHL) and single-sided deafness (SSD). This study aimed to compare the effects of CI and BCI on tinnitus in AHL/SSD patients with tinnitus.<br><br>METHODS: This retrospective study enrolled adult AHL/SSD patients with significant tinnitus who underwent CI or BCI placement between 2017 and 2023. Clinical characteristics, preoperative and postoperative audiologic test results, and tinnitus questionnaires (tinnitus handicap inventory, THI; visual analog scale, VAS) were collected and analyzed.<br><br>RESULTS: Of 33 AHL/SSD patients with significant tinnitus (THI &#x2265;&#x2009;18), 16 received CI and 17 BCI. In the CI group, all four VAS scores (loudness, awareness, annoyance, and effect on life) and THI scores significantly improved. In the BCI group, annoyance and effect on life categories of VAS and THI scores significantly improved, while VAS loudness and awareness remained similar. Linear mixed model analysis showed that the decrease in VAS loudness, awareness, and annoyance scores was significantly greater in the CI group compared to the BCI group. The CI group showed a significantly higher tinnitus cure rate (62.5.0%) compared with the BCI group (11.8%) at 6-months postoperative.<br><br>CONCLUSION: Both CI and BCI effectively improved tinnitus in AHL/SSD patients with tinnitus. However, CI is considered the first-line therapeutic option for tinnitus due to its stronger effect on tinnitus suppression as well as the higher cure rate in SSD/AHL patients with tinnitus.}, }
@article {pmid40059266, year = {2025}, author = {Meng, J and Wei, Y and Mai, X and Li, S and Wang, X and Luo, R and Ji, M and Zhu, X}, title = {Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {40059266}, issn = {1741-0444}, abstract = {Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.}, }
@article {pmid40058464, year = {2025}, author = {Zhao, Z and Li, Y and Peng, Y and Camilleri, K and Kong, W}, title = {Multi-view graph fusion of self-weighted EEG feature representations for speech imagery decoding.}, journal = {Journal of neuroscience methods}, volume = {418}, number = {}, pages = {110413}, doi = {10.1016/j.jneumeth.2025.110413}, pmid = {40058464}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; *Brain/physiology ; Adult ; Male ; Female ; }, abstract = {BACKGROUND: Electroencephalogram (EEG)-based speech imagery is an emerging brain-computer interface paradigm, which enables the speech disabled to naturally and intuitively communicate with external devices or other people. Currently, speech imagery research decoding performance is limited. One of the reasons is that there is still no consensus on which domain features are more discriminative.<br><br>NEW METHOD: To adaptively capture the complementary information from different domain features, we treat each domain as a view and propose a multi-view graph fusion of self-weighted EEG feature representations (MVGSF) model by learning a consensus graph from multi-view EEG features, based on which the imagery intentions can be effectively decoded. Considering that different EEG features in each view have different discriminative abilities, the view-dependent feature importance exploration strategy is incorporated in MVGSF.<br><br>RESULTS: (1) MVGSF exhibits outstanding performance on two public speech imagery datasets (2) The learned consensus graph from multi-view features effectively characterizes the relationships of EEG samples in a progressive manner. (3) Some task-related insights are explored including the feature importance-based identification of critical EEG channels and frequency bands in speech imagery decoding.<br><br>We compared MVGSF with single-view counterparts, other multi-view models, and state-of-the-art models. MVGSF achieved the highest accuracy, with average accuracies of 78.93% on the 2020IBCIC3 dataset and 53.85% on the KaraOne dataset.<br><br>CONCLUSIONS: MVGSF effectively integrates features from multiple domains to enhance decoding capabilities. Furthermore, through the learned feature importance, MVGSF has made certain contributions to identify the EEG spatial-frequency patterns in speech imagery decoding.}, }
@article {pmid40057290, year = {2025}, author = {Chuang, CH and Chang, KY and Huang, CS and Bessas, AM}, title = {Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals.}, journal = {NeuroImage}, volume = {310}, number = {}, pages = {121123}, doi = {10.1016/j.neuroimage.2025.121123}, pmid = {40057290}, issn = {1095-9572}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; *Brain/physiology ; }, abstract = {Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution that simultaneously addresses multiple artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.}, }
@article {pmid40054446, year = {2025}, author = {Natraj, N and Seko, S and Abiri, R and Miao, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K}, title = {Sampling representational plasticity of simple imagined movements across days enables long-term neuroprosthetic control.}, journal = {Cell}, volume = {188}, number = {5}, pages = {1208-1225.e32}, pmid = {40054446}, issn = {1097-4172}, support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; R01 HD111562/HD/NICHD NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Neuronal Plasticity ; Adult ; *Movement ; Female ; Electrocorticography ; Middle Aged ; Quadriplegia/physiopathology ; Robotics ; Young Adult ; }, abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what the representational stability of simple well-rehearsed actions is, particularly in humans, and their adaptability to new contexts. Using an electrocorticography brain-computer interface (BCI) in tetraplegic participants, we found that the low-dimensional manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. The manifold's absolute location, however, demonstrated constrained day-to-day drift. Strikingly, neural statistics, especially variance, could be flexibly regulated to increase representational distances during BCI control without somatotopic changes. Discernability strengthened with practice and was BCI-specific, demonstrating contextual specificity. Sampling representational plasticity and drift across days subsequently uncovered a meta-representational structure with generalizable decision boundaries for the repertoire; this allowed long-term neuroprosthetic control of a robotic arm and hand for reaching and grasping. Our study offers insights into mesoscale representational statistics that also enable long-term complex neuroprosthetic control.}, }
@article {pmid40051983, year = {2025}, author = {Lingelbach, K and Rips, J and Karstensen, L and Mathis-Ullrich, F and Vukelić, M}, title = {Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.}, journal = {Frontiers in neuroergonomics}, volume = {6}, number = {}, pages = {1535799}, pmid = {40051983}, issn = {2673-6195}, abstract = {INTRODUCTION: Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.<br><br>METHODS: We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.<br><br>RESULTS: Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.<br><br>DISCUSSION: The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.}, }
@article {pmid40051611, year = {2025}, author = {Martínez-Cagigal, V and Thielen, J and Hornero, R and Desain, P}, title = {Editorial: The role of code-modulated evoked potentials in next-generation brain-computer interfacing.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1548183}, pmid = {40051611}, issn = {1662-5161}, }
@article {pmid40051554, year = {2025}, author = {Teman, SJ and Atwood, TC and Converse, SJ and Fry, TL and Laidre, KL}, title = {Measuring polar bear health using allostatic load.}, journal = {Conservation physiology}, volume = {13}, number = {1}, pages = {coaf013}, pmid = {40051554}, issn = {2051-1434}, abstract = {The southern Beaufort Sea polar bear sub-population (Ursus maritimus) has been adversely affected by climate change and loss of sea ice habitat. Even though the sub-population is likely decreasing, it remains difficult to link individual polar bear health and physiological change to sub-population effects. We developed an index of allostatic load, which represents potential physiological dysregulation. The allostatic load index included blood- and hair-based analytes measured in physically captured southern Beaufort bears in spring. We examined allostatic load in relation to bear body condition, age, terrestrial habitat use and, over time, for bear demographic groups. Overall, allostatic load had no relationship with body condition. However, allostatic load was higher in adult females without cubs that used terrestrial habitats the prior year, indicating potential physiological dysregulation with land use. Allostatic load declined with age in adult females without cubs. Sub-adult males demonstrated decreased allostatic load over time. Our study is one of the first attempts to develop a health scoring system for free-ranging polar bears, and our findings highlight the complexity of using allostatic load as an index of health in a wild species. Establishing links between individual bear health and population dynamics is important for advancing conservation efforts.}, }
@article {pmid40050621, year = {2025}, author = {Cui, H and Xiao, Y and Yang, Y and Pei, M and Ke, S and Fang, X and Qiao, L and Shi, K and Long, H and Xu, W and Cai, P and Lin, P and Shi, Y and Wan, Q and Wan, C}, title = {A bioinspired in-materia analog photoelectronic reservoir computing for human action processing.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {2263}, pmid = {40050621}, issn = {2041-1723}, support = {92364106//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92364204//National Natural Science Foundation of China (National Science Foundation of China)/ ; BK20220121//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; }, mesh = {Humans ; *Algorithms ; Neural Networks, Computer ; Transistors, Electronic ; }, abstract = {Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achieved based on such system. Furthermore, falling behaviors recognition is also verified by our system with low energy consumption for processing per action (~45.78 μJ) which outperforms most previous reports on human action processing. Our results are of profound potential for advancing computer vision based on neuromorphic electronics.}, }
@article {pmid40049535, year = {2025}, author = {Kong, L and Zhang, Q and Wang, H and Xu, Y and Xu, C and Chen, Y and Lu, J and Hu, S}, title = {Exploration of the optimized portrait of omega-3 polyunsaturated fatty acids in treating depression: A meta-analysis of randomized-controlled trials.}, journal = {Journal of affective disorders}, volume = {379}, number = {}, pages = {489-501}, doi = {10.1016/j.jad.2025.03.006}, pmid = {40049535}, issn = {1573-2517}, mesh = {Humans ; *Fatty Acids, Omega-3/therapeutic use ; Randomized Controlled Trials as Topic ; *Depressive Disorder/drug therapy ; Treatment Outcome ; }, abstract = {BACKGROUND: According to previous studies, omega-3 polyunsaturated fatty acids (PUFAs) are controversial for the efficacy of treating depression.<br><br>AIMS: This meta-analysis aims to investigate whether omega-3 PUFAs are able to treat depression, and find out the most beneficial clinical portrait.<br><br>METHODS: More than two reviewers searched six registries, and 36 studies were eventually considered eligible. The PRISMA guidelines were used for data extraction, Cochrane Handbook for quality assessment, and random effects model for data pooling.<br><br>OUTCOMES: Significant heterogeneity and publication bias were observed. According to the results, significant efficacy was detected in the overall analysis [SMD&#xa0;=&#xa0;-0.26, 95&#xa0;% CI&#xa0;=&#xa0;(-0.41, -0.11)] and several subgroups, while total daily dosage might be a potential heterogeneity source (P&#xa0;<&#xa0;0.05). No between-group difference was observed in the rate of response [RR&#xa0;=&#xa0;0.99, 95&#xa0;% CI&#xa0;=&#xa0;(0.82, 1.20)], remission [RR&#xa0;=&#xa0;1.17, 95&#xa0;% CI&#xa0;=&#xa0;(0.92, 1.48)], and adverse events [RR&#xa0;=&#xa0;1.07, 95&#xa0;% CI&#xa0;=&#xa0;(0.90, 1.29)]. Total daily intake of eicosapentaenoic acid (EPA) and remission rate conformed to linear correlation (P&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: 1) Omega-3 PUFAs might be effective in treating depression; 2) For Asian patients with mild to moderate depression and no other baseline medication, over 8&#xa0;weeks of omega-3 PUFAs 1000-1500&#xa0;mg/day with ratio of EPA/docosahexaenoic acid (DHA) between 1:1 and 2:1 might benefit the most; 3) Omega-3 PUFAs are no superior than placebo in rates of response, remission, and adverse events. Although several limitations exist, the evidence-based information provides guidance for clinical practice and directions for further research.<br><br>PROSPERO REGISTRATION NUMBER: CRD42023464823.}, }
@article {pmid40049458, year = {2025}, author = {Premchand, B and Toe, KK and Wang, C and Wan, KR and Selvaratnam, T and Toh, VE and Ng, WH and Libedinsky, C and Chen, W and Lim, R and Cheng, MY and Gao, Y and Ang, KK and So, RQY}, title = {Comparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model.}, journal = {Brain research bulletin}, volume = {223}, number = {}, pages = {111289}, doi = {10.1016/j.brainresbull.2025.111289}, pmid = {40049458}, issn = {1873-2747}, mesh = {*Multiple System Atrophy/physiopathology ; *Brain-Computer Interfaces ; Humans ; Animals ; Male ; Disease Models, Animal ; Neural Networks, Computer ; Electroencephalography/methods ; Macaca mulatta ; Machine Learning ; Brain/physiopathology ; Middle Aged ; }, abstract = {Paralysis affects many people worldwide, and the people affected often suffer from impaired communication. We developed a microelectrode-based Brain-Computer Interface (BCI) for enabling communication in patients affected by paralysis, and implanted it in a patient with Multiple System Atrophy (MSA), a neurodegenerative disease that causes widespread neural symptoms including paralysis. To verify the effectiveness of the BCI system, it was also tested by implanting it in a non-human primate (NHP). Data from the human and NHP were used to train binary classifiers two different types of machine learning models: a Linear Discriminant Analysis (LDA) model, and a Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN). The LDA model performed at up to 72.7 % accuracy for binary decoding in the human patient, however, performance was highly variable and was much lower on most recording days. The BCI system was able to accurately decode movement vs non-movement in the NHP (accuracy using LDA: 82.7 ± 3.3 %, LSTM: 83.7 ± 2.2 %, 95 % confidence intervals), however it was not able to with recordings from the human patient (accuracy using LDA: 47.0 ± 5.1 %, LSTM: 44.6 ± 9.9 %, 95 % confidence intervals). We discuss how neurodegenerative diseases such as MSA can impede BCI-based communication, and postulate on the mechanisms by which this may occur.}, }
@article {pmid40049046, year = {2025}, author = {Chai, C and Yang, X and Zheng, Y and Bin Heyat, MB and Li, Y and Yang, D and Chen, YH and Sawan, M}, title = {Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: Trends for wearable and noninvasive Brain-Computer interface.}, journal = {Biosensors &amp; bioelectronics}, volume = {278}, number = {}, pages = {117321}, doi = {10.1016/j.bios.2025.117321}, pmid = {40049046}, issn = {1873-4235}, mesh = {Humans ; *Magnetoencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Photoacoustic Techniques/instrumentation/methods ; *Wearable Electronic Devices ; Brain/physiology/diagnostic imaging ; Electroencephalography/instrumentation ; Biosensing Techniques/instrumentation ; Spectroscopy, Near-Infrared/methods/instrumentation ; Multimodal Imaging/instrumentation ; }, abstract = {Wearable noninvasive brain-computer interface (BCI) technologies, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), have experienced significant progress since their inception. However, these technologies have not achieved revolutionary advancements, largely because of their inherently low signal-to-noise ratio and limited penetration depth. In recent years, the application of quantum-theory-based optically pumped (OP) technologies, particularly optical pumped magnetometers (OPMs) for magnetoencephalography (MEG) and photoacoustic imaging (PAI) utilizing OP pulsed laser sources, has opened new avenues for development in noninvasive BCIs. These advanced technologies have garnered considerable attention owing to their high sensitivity in tracking neural activity and detecting blood oxygen saturation. This paper represents the first attempt to discuss and compare technologies grounded in OP theory by examining the technical advantages of OPM-MEG and PAI over traditional EEG and fNIRS. Furthermore, the paper investigates the theoretical and structural feasibility of hardware reuse in OPM-MEG and PAI applications.}, }
@article {pmid40048825, year = {2025}, author = {Kacker, K and Chetty, N and Feldman, AK and Bennett, J and Yoo, PE and Fry, A and Lacomis, D and Harel, NY and Nogueira, RG and Majidi, S and Opie, NL and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ}, title = {Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, pmid = {40048825}, issn = {1741-2552}, support = {UH3 NS120191/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology/diagnosis ; *Motor Cortex/physiopathology/physiology ; Male ; *Brain-Computer Interfaces ; *Gamma Rhythm/physiology ; *Electrocorticography/methods/instrumentation ; Middle Aged ; Female ; Stents ; Electrodes, Implanted ; Motor Activity/physiology ; Movement/physiology ; Aged ; }, abstract = {Objective.This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.Approach.vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.Main results.Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.Significance.By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.}, }
@article {pmid40048822, year = {2025}, author = {Berger, LM and Wood, G and Kober, SE}, title = {Manipulating cybersickness in virtual reality-based neurofeedback and its effects on training performance.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbd76}, pmid = {40048822}, issn = {1741-2552}, mesh = {Humans ; Female ; Male ; *Virtual Reality ; *Neurofeedback/methods/physiology ; Adult ; Young Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Psychomotor Performance/physiology ; Heart Rate/physiology ; Nausea/etiology ; Motion Sickness/etiology/physiopathology/prevention &amp; control ; }, abstract = {Objective. Virtual reality (VR) serves as a modern and powerful tool to enrich neurofeedback (NF) and brain-computer interface (BCI) applications as well as to achieve higher user motivation and adherence to training. However, between 20%-80% of all the users develop symptoms of cybersickness (CS), namely nausea, oculomotor problems or disorientation during VR interaction, which influence user performance and behavior in VR. Hence, we investigated whether CS-inducing VR paradigms influence the success of a NF training task.Approach. We tested 39 healthy participants (20 female) in a single-session VR-based NF study. One half of the participants was presented with a high CS-inducing VR-environment where movement speed, field of view and camera angle were varied in a CS-inducing fashion throughout the session and the other half underwent NF training in a less CS-inducing VR environment, where those parameters were held constant. The NF training consisted of 6 runs of 3 min each, in which participants should increase their sensorimotor rhythm (SMR, 12-15 Hz) while keeping artifact control frequencies constant (Theta 4-7 Hz, Beta 16-30 Hz). Heart rate and subjectively experienced CS were also assessed.Main results. The high CS-inducing condition tended to lead to more subjectively experienced CS nausea symptoms than the low CS-inducing condition. Further, women experienced more CS, a higher heart rate and showed a worse NF performance compared to men. However, the SMR activity during the NF training was comparable between both the high and low CS-inducing groups. Both groups were able to increase their SMR across feedback runs, although, there was a tendency of higher SMR power for male participants in the low CS group.Significance. Hence, sickness symptoms in VR do not necessarily impair NF/BCI training success. This takes us one step further in evaluating the practicability of VR in BCI and NF applications. Nevertheless, inter-individual differences in CS susceptibility should be taken into account for VR-based NF applications.}, }
@article {pmid40048236, year = {2025}, author = {Zhang, Y and Hedley, FE and Zhang, RY and Jin, J}, title = {Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.}, journal = {Journal of psychopathology and clinical science}, volume = {134}, number = {4}, pages = {363-388}, doi = {10.1037/abn0000972}, pmid = {40048236}, issn = {2769-755X}, support = {//National Key R&amp;D Program of China/ ; //National Natural Science Foundation of China/ ; //Zhejiang University; State Key Laboratory of Brain-Machine Intelligence/ ; //Ministry of Education; Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; }, mesh = {Humans ; *Phobia, Social/physiopathology/psychology ; *Models, Psychological ; *Cognitive Behavioral Therapy ; }, abstract = {Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).}, }
@article {pmid40047565, year = {2025}, author = {Haghighi, P and Jeakle, EN and Sturgill, BS and Abbott, JR and Solis, E and Devata, VS and Vijayakumar, G and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ}, title = {Enhanced Performance of Novel Amorphous Silicon Carbide Microelectrode Arrays in Rat Motor Cortex.}, journal = {Micromachines}, volume = {16}, number = {2}, pages = {}, pmid = {40047565}, issn = {2072-666X}, support = {2R01TW104344-22S5/NH/NIH HHS/United States ; }, abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity from cortical neurons for applications that include brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic implantation conditions. This has largely been attributed to the brain's foreign body response, which is marked by neuroinflammation and gliosis in the immediate vicinity of the MEA implantation site. This has prompted the development of novel MEAs with either coatings or architectures that aim to reduce the tissue response. The present study examines the comparative performance of multi-shank planar, silicon-based devices and low-flexural-rigidity amorphous silicon carbide (a-SiC) MEAs that have a similar architecture but differ with respect to the shank cross-sectional area. Data from a-SiC arrays were previously reported in a prior study from our group. In a manner consistent with the prior work, larger cross-sectional area silicon-based arrays were implanted in the motor cortex of female Sprague-Dawley rats and weekly recordings were made for 16 weeks after implantation. Single unit metrics from the recordings were compared over the implantation period between the device types. Overall, the expression of single units measured from a-SiC devices was significantly higher than for silicon-based MEAs throughout the implantation period. Immunohistochemical analysis demonstrated reduced neuroinflammation and gliosis around the a-SiC MEAs compared to silicon-based devices. Our findings demonstrate that the a-SiC MEAs with a smaller shank cross-sectional area can record single unit activity with more stability and exhibit a reduced inflammatory response compared to the silicon-based device employed in this study.}, }
@article {pmid40046512, year = {2025}, author = {Fang, K and Wang, Z and Tang, Y and Guo, X and Li, X and Wang, W and Liu, B and Dai, Z}, title = {Dynamically Controlled Flight Altitudes in Robo-Pigeons via Locus Coeruleus Neurostimulation.}, journal = {Research (Washington, D.C.)}, volume = {8}, number = {}, pages = {0632}, pmid = {40046512}, issn = {2639-5274}, abstract = {Robo-pigeons, a novel class of hybrid robotic systems developed using brain-computer interface technology, hold marked promise for search and rescue missions due to their superior load-bearing capacity and sustained flight performance. However, current research remains largely confined to laboratory environments, and precise control of their flight behavior, especially flight altitude regulation, in a large-scale spatial range outdoors continues to pose a challenge. Herein, we focus on overcoming this limitation by using electrical stimulation of the locus coeruleus (LoC) nucleus to regulate outdoor flight altitude. We investigated the effects of varying stimulation parameters, including stimulation frequency (SF), interstimulus interval (ISI), and stimulation cycles (SC), on the flight altitude of robo-pigeons. The findings indicate that SF functions as a pivotal switch controlling the ascending and descending flight modes of the robo-pigeons. Specifically, 60 Hz stimulation effectively induced an average ascending flight of 12.241 m with an 87.72% success rate, while 80 Hz resulted in an average descending flight of 15.655 m with a 90.52% success rate. SF below 40 Hz did not affect flight altitude change, whereas over 100 Hz caused unstable flights. The number of SC was directly correlated with the magnitude of altitude change, enabling quantitative control of flight behavior. Importantly, electrical stimulation of the LoC nucleus had no significant effects on flight direction. This study is the first to establish that targeted variation of electrical stimulation parameters within the LoC nucleus can achieve precise altitude control in robo-pigeons, providing new insights for advancing the control of flight animal-robot systems in real-world applications.}, }
@article {pmid40044089, year = {2025}, author = {Jialin, A and Zhang, HG and Wang, XH and Wang, JF and Zhao, XY and Wang, C and Cao, MN and Li, XJ and Li, Y and Cao, LL and Zhong, BL and Deng, W}, title = {Cortical activation patterns in generalized anxiety and major depressive disorders measured by multi-channel near-infrared spectroscopy.}, journal = {Journal of affective disorders}, volume = {379}, number = {}, pages = {549-558}, doi = {10.1016/j.jad.2025.02.116}, pmid = {40044089}, issn = {1573-2517}, mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnosis/psychology ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Anxiety Disorders/physiopathology/diagnosis/psychology ; Adult ; *Prefrontal Cortex/physiopathology ; Temporal Lobe/physiopathology ; Middle Aged ; Generalized Anxiety Disorder ; }, abstract = {BACKGROUND: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent mental disorders in psychiatry, but their overlapping symptoms often complicate precise diagnoses. This study aims to explore differential brain activation patterns in healthy controls (HC), MDD, and GAD groups through functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) to enhance the accuracy of clinical diagnoses.<br><br>METHODS: This study recruited 30 patients with MDD, 45 patients with GAD, and 34 demographically matched HCs. Hemodynamic changes in the prefrontal cortex (PFC) and temporal lobes were measured using a 48-channel fNIRS during the VFT task. Demographics information, clinical characteristics and VFT performance were also recorded.<br><br>RESULTS: Compared to HCs, both MDD and GAD share a neurobiological phenotype of hypoactivation in the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) during VFT. Moreover, MDD patients exhibited significantly greater hypoactivation in the left DLPFC and mPFC than GAD patients.<br><br>CONCLUSIONS: Although both GAD and MDD patients exhibit disrupted cortical function, the impairment is less severe in GAD. These findings provide preliminary neurophysiological evidence supporting the utility of the fNIRS-VFT paradigm in differentiating GAD from MDD. This approach may complement traditional diagnostic methods, inform targeted interventions, and ultimately enhance patient outcomes.}, }
@article {pmid40044088, year = {2025}, author = {Liang, S and Gao, Y and Palaniyappan, L and Song, XM and Zhang, T and Han, JF and Tan, ZL and Li, T}, title = {Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder.}, journal = {Journal of affective disorders}, volume = {379}, number = {}, pages = {118-126}, doi = {10.1016/j.jad.2025.03.003}, pmid = {40044088}, issn = {1573-2517}, mesh = {Humans ; *Depressive Disorder, Major/genetics/pathology/diagnostic imaging/psychology ; *Anhedonia/physiology ; Male ; Female ; Adult ; *Cerebral Cortex/pathology/diagnostic imaging ; Middle Aged ; Magnetic Resonance Imaging ; Neuroimaging ; *Brain Cortical Thickness ; Gene Expression ; Transcriptome ; }, abstract = {BACKGROUND: Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.<br><br>METHODS: We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.<br><br>RESULTS: Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.<br><br>LIMITATIONS: These include measuring anhedonia using interest-activity score and employing a cross-sectional design.<br><br>CONCLUSIONS: This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.}, }
@article {pmid40043367, year = {2025}, author = {Demchenko, I and Shavit, T and Benyamini, M and Zacksenhouse, M}, title = {Self-correcting brain computer interface based on classification of multiple error-related potentials.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbcda}, pmid = {40043367}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Algorithms ; Evoked Potentials/physiology ; Reproducibility of Results ; }, abstract = {Objective.Electroencephalogram (EEG) based brain-computer interfaces (BCIs) have shown tremendous promise in facilitating direct non-invasive brain-control over external devices. However, their practical application is hampered due to errors in command interpretation. A promising strategy for improving BCI accuracy is based on detecting error-related potentials (ErrPs), which are EEG potentials evoked in response to errors. Thus, performance can be improved by undoing actions that evoke potentials that the BCI detects as ErrPs. To achieve further improvement, we aimed to classify the type of error and correct, rather than just undo, erroneous actions. The objectives of this study are to develop an error classifier (EC) and to investigate the hypothesis that correcting the actions according to the EC decisions improves performance.Approach.To evaluate our hypothesis we developed a BCI application to control the pose of virtual hands with three possible commands: change the pose of either the right or left hand and maintain pose. Thus, when an action elicits an ErrP, the identity of the correct command is still undecided. The self-correcting BCI included an EC and was developed in three phases: hand control, initial brain control and self-correcting brain control. The first two phases were conducted by 22 participants, and half of them (n= 11) also completed the last phase.Main results.Detecting the type of error and correcting actions accordingly improved the success rate of the self-correcting BCI for each participant (n= 11), with a significant average improvement of 6.6%and best improvement of 13.5%.Significance.Self-correction, based on an EC, was demonstrated to improve the accuracy of BCIs for three commands. Thus, our work presents a significant step toward the development of more reliable and user-friendly non-invasive BCIs.}, }
@article {pmid40043361, year = {2025}, author = {Luca, IS and Vuckovic, A}, title = {How are opposite neurofeedback tasks represented at cortical and corticospinal tract levels?.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbcdb}, pmid = {40043361}, issn = {1741-2552}, mesh = {Humans ; *Neurofeedback/methods/physiology ; *Pyramidal Tracts/physiology ; Male ; Female ; Adult ; Young Adult ; *Electroencephalography/methods ; *Evoked Potentials, Motor/physiology ; Motor Cortex/physiology ; Cerebral Cortex/physiology ; }, abstract = {Objective.The study objective was to characterise indices of learning and patterns of connectivity in two neurofeedback (NF) paradigms that modulate mu oscillations in opposite directions, and the relationship with change in excitability of the corticospinal tract (CST).Approach.Forty-three healthy volunteers participated in 3 NF sessions for upregulation (N = 24) or downregulation (N = 19) of individual alpha (IA) power at central location Cz. Brain signatures from multichannel electroencephalogram (EEG) were analysed, including oscillatory (power, spindles), non-oscillatory components (Hurst exponent), and effective connectivity directed transfer function (DTF) of participants who were successful at enhancing or suppressing IA power at Cz. CST excitability was studied through leg motor-evoked potential, tested before and after the last NF session. We assessed whether participants modulated widespread alpha or central mu rhythm through the use of current source density derivation (CSD), and related the change in activity in mu and upper half of mu band, to CST excitability change.Main results.In the last session, IA/mu power suppression was achieved by 79% of participants, while 63% enhanced IA. CSD-EEG revealed that mu power was upregulated through an increase in the incidence rate of bursts of alpha band activity, while downregulation involved changes in oscillation amplitude and temporal patterns. Neuromodulation also influenced frequencies adjacent to the targeted band, indicating the use of common mental strategies within groups. DTF analysis showed, for both groups, significant connectivity between structures commonly associated with motor imagery tasks, known to modulate the excitability of the motor cortex, although most connections did not remain significant after correcting for multiple comparisons. CST excitability modulation was related to the absolute amplitude of upper mu modulation, rather than the modulation direction.Significance.The upregulation and downregulation of IA/mu power during NF, with respect to baseline were achieved via distinct mechanisms involving oscillatory and non-oscillatory EEG features. Mu enhancement and suppression post-NF and during the last NF block with respect to the baseline, respectively corresponded to opposite trends in motor-evoked potential changes post-NF. The ability of NF to modulate CST excitability could be a valuable rehabilitation tool for central nervous system disorders (stroke, spinal cord injury), where increased excitability and neural plasticity are desired. This work may inform future neuromodulation protocols, and may improve NF training effectiveness by rewarding certain EEG signatures.}, }
@article {pmid40043320, year = {2025}, author = {Alcolea, PI and Ma, X and Bodkin, K and Miller, LE and Danziger, ZC}, title = {Less is more: selection from a small set of options improves BCI velocity control.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbcd9}, pmid = {40043320}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; Animals ; *Macaca mulatta ; Male ; Algorithms ; Female ; Adult ; Electroencephalography/methods ; Young Adult ; Psychomotor Performance/physiology ; }, abstract = {Objective.Decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities.Approach. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 naïve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF).Main Result. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per visit compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF.Significance. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control.}, }
@article {pmid40043184, year = {2025}, author = {Chandler, JA}, title = {Inferring Mental States from Brain Data: Ethico-legal Questions about Social Uses of Brain Data.}, journal = {The Hastings Center report}, volume = {55}, number = {1}, pages = {22-32}, pmid = {40043184}, issn = {1552-146X}, support = {//ERANET-Neuron Program/ ; /CAPMC/CIHR/Canada ; }, mesh = {Humans ; *Brain ; Privacy/legislation &amp; jurisprudence ; Reproducibility of Results ; }, abstract = {Neurotechnologies that collect and interpret data about brain activity are already in use for medical and nonmedical applications. Refinements of existing noninvasive techniques and the discovery of new ones will likely encourage broader uptake. The increased collection and use of brain data and, in particular, their use to infer the existence of mental states have led to questions about whether mental privacy may be threatened. It may be threatened if the brain data actually support inferences about the mind or if decisions are made about a person in the belief that the inferences are justified. This article considers the chain of inferences lying between data about neural activity and a particular mental state as well as the ethico-legal issues raised by making these inferences, focusing here on what the threshold of reliability should be for using brain data to infer mental states.}, }
@article {pmid40043182, year = {2025}, author = {Yang, Y and Wang, Y and Wang, X}, title = {Harnessing psychedelics for stroke recovery: therapeutic potential and mechanisms.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awaf093}, pmid = {40043182}, issn = {1460-2156}, }
@article {pmid40042910, year = {2025}, author = {Xie, B and Xiong, T and Guo, G and Pan, C and Ma, W and Yu, P}, title = {Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {10}, pages = {e2417040122}, pmid = {40042910}, issn = {1091-6490}, support = {22125406 22074149//MOST | National Natural Science Foundation of China (NSFC)/ ; }, abstract = {The fluidic memristor has attracted growing attention as a promising candidate for neuromorphic computing and brain-computer interfaces. However, a fluidic memristor with ion selectivity as that of natural ion channels remains a key challenge. Herein, inspired by the structure of natural biomembranes, we developed an ion-shuttling memristor (ISM) by utilizing organic solvents and artificial carriers to emulate ion channels embedded in biomembranes, which exhibited both neuromorphic functions and ion selectivity. Pinched hysteresis I-V loop curve, scan rate dependency, and distinctive impedance spectra confirmed the memristive characteristics of the as-prepared device. Moreover, the memory mechanism was discussed theoretically and validated by finite-element modeling. The ISM features multiple neuromorphic functions, such as paired-pulse facilitation, paired-pulse depression, and learning-experience behavior. More importantly, the ion selectivity of the ISM was observed, which allowed further emulation of ion-selective neural functions like resting membrane potential. Benefiting from the structural similarity to membrane-embedded ion channels, the ISM opens the door for ion-based neuromorphic computing and sophisticated chemical regulation by manipulating multifarious ions with neuromorphic functions.}, }
@article {pmid40042891, year = {2025}, author = {Gielas, AM}, title = {Man, Hibernating Animals, and Poikilothermic Fish: The Present and Future of BCI Technology.}, journal = {Journal of special operations medicine : a peer reviewed journal for SOF medical professionals}, volume = {25}, number = {1}, pages = {50-54}, doi = {10.55460/FA29-NVKE}, pmid = {40042891}, issn = {1553-9768}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Military Personnel ; Animals ; }, abstract = {In 2024 and early 2025, several successful surgeries involving brain-computer interfaces (BCIs) gained media attention, including those conducted by Elon Musk's company Neuralink, which implanted BCIs in three paralyzed volunteers, allowing them to control computers through thought alone. While the concept of merging humans with machines dates back to the 1960s, BCI technology has now entered the clinical trial stage, with a focus on restoring communication, mobility, and sensation in individuals with severe disabilities and neurodegenerative disorders. For over two decades, BCIs have also been explored as tools to enhance the cognitive and physical abilities of military personnel. However, before Special Operations Forces (SOF) medical staff encounter BCIs in an enhancement capacity, they are likely to first come across them in medical settings. This article provides an overview of BCI technology, focusing on 1) how it works, 2) its potential significance for injured SOF servicemembers, 3) current challenges, and 4) its potential to enhance SOF in the future.}, }
@article {pmid40040918, year = {2025}, author = {Jiang, Y and Liu, YL and Zhou, X and Shu, QQ and Dong, L and Xu, Z and Wan, JQ}, title = {A retrospective study of the Dual-channels Bolus Contrast Injection (Dc-BCI) technique during endovascular mechanical thrombectomy in the management of acute ischemic stroke due to large-vessel occlusion: a technical report.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1508976}, pmid = {40040918}, issn = {1664-2295}, abstract = {Endovascular mechanical thrombectomy (EMT) is an effective treatment for acute ischemic stroke and identifying the precise thrombus size remains key to a successful EMT. However, no imaging modality has been able to provide this information simultaneously and efficiently in an emergency setting. The present study introduces a novel technique named dual-channel bolus contrast injection (Dc-BCI) for determining thrombus size and location during EMT. In the in vitro study, the Dc-BCI demonstrated an accurate projection of the thrombus size, as the actual thrombus diameter (R[2] = 0.92, p < 0.01) and length (R[2] = 0.94, p < 0.01) exhibited a high degree of correlation with that of obtained from Dc-BCI. Consequently, between February 2023 and August 2024, 87 patients diagnosed with acute cerebral large vessel occlusions were enrolled in the study and received EMT for the treatment of acute cerebral large vessel occlusions. The Dc-BCI was successfully performed in all patients to measure the diameter and length of the thrombus. These information were used to select an appropriate stent-retriever for EMT. The restoration of blood flow was achieved in 84 patients (96.6%) to an mTICI score of 2b/3. Additionally, a low incidence of postoperative complications was observed (e.g., subarachnoid hemorrhage 8% and cerebral hemorrhage 5.7%). In conclusion, it can be posited that the Dc-BCI has the potential to enhance the outcomes of EMT, as it is capable of revealing the thrombus size information, which optimizes the interaction between the stent retriever and the thrombus, while simultaneously reducing the risk of vascular injury that is associated with the prolonged use of the stent retriever.}, }
@article {pmid40040909, year = {2025}, author = {Tekin, U and Dener, M}, title = {A bibliometric analysis of studies on artificial intelligence in neuroscience.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1474484}, pmid = {40040909}, issn = {1664-2295}, abstract = {The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.}, }
@article {pmid40040811, year = {2025}, author = {Feng, X and Bao, X and Huang, H and Wang, Z and Hu, W and Xue, C and Song, Z and Cai, Y and Huang, Q and Li, Y}, title = {Frontal gamma-alpha ratio reveals neural oscillatory mechanism of attention shifting in tinnitus.}, journal = {iScience}, volume = {28}, number = {3}, pages = {111929}, pmid = {40040811}, issn = {2589-0042}, abstract = {In clinical practice, the symptoms of tinnitus patients can be temporarily alleviated by diverting their attention away from disturbing sounds. However, the precise mechanisms through which this alleviation occurs are still not well understood. Here, we aimed to directly evaluate the role of attention in tinnitus alleviation by conducting distraction tasks with multilevel loads and resting-state tests among 52 adults with tinnitus and 52 healthy controls. We demonstrated that the abnormal neural oscillations in tinnitus subjects, reflected in an altered gamma/alpha ratio index in the frontal lobe, could be regulated by attention shifting in a linear manner for which the regulatory effect increased with the load of distraction. Quantitative measures of the regulation significantly correlated with symptom severity. Altogether, our work provides proof-of-concept for the role of attention in tinnitus perception and lays a solid foundation to support evidence-based applications of attention shifting in clinical interventions for tinnitus.}, }
@article {pmid40040231, year = {2024}, author = {Chaudhry, ZA and Baxter, RH and Fu, JL and Wang, PT and Sohn, WJ and Do, AH}, title = {Feasibility of Immersive Virtual Reality Feedback for Enhancing Learning in Brain-Computer Interface Control of Ambulation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782667}, pmid = {40040231}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Walking/physiology ; *Electroencephalography ; Male ; Adult ; Feasibility Studies ; Learning ; Female ; Young Adult ; Spinal Cord Injuries/rehabilitation/physiopathology ; User-Computer Interface ; }, abstract = {After prolonged paralysis, paraplegic spinal cord injury (SCI) patients typically lose the ability to generate the expected electroencephalogram (EEG) α/β modulation associated with leg movements. Brain computer interface (BCI)-controlled ambulation devices have emerged as a way to restore brain-controlled walking, but this loss of EEG signal modulation may impede the ability to operate such systems and prolonged training may be necessary to restore this physiologic phenomenon. To address this issue, this study explores the use of immersive virtual reality (VR) in providing more convincing feedback to enhance learning within a BCI training paradigm. Here, an EEG-based BCI-controlled walking simulator with an environment composed of 10 designated stop zones along a linear course was used to test this concept. Able-bodied subjects were tasked with using idling or kinesthetic motor imagery (KMI) of gait to control an avatar to either dwell at each designated stop for 5 s or advance along the course respectively. Subject performance was measured using a composite score per run and learning rate across runs. Composite scores were calculated as the geometric mean of two subscores: a stop score (reflecting the number of successful stops), and a time score (reflecting how fast the course was completed). The learning rate was calculated as the slope of the composite scores across all runs. A random walk procedure was performed to determine the statistical likelihood that each BCI run was purposeful (p≤ 0.001). Three able-bodied subjects were recruited (2 in immersive VR group and 1 in non-immersive VR group), and operated the simulator for up to 4 separate visits. The immersive VR group achieved an average composite score of 60.4% ± 12.9, while the non-VR group had an average composite score of 79.0% ± 12.2. The learning rate was 1.07%/run and 0.42%/run for the immersive and non-immersive VR groups, respectively. Purposeful control was attained in a higher proportion of runs for the immersive VR group than in the non-immersive VR group. Although limited by small sample size, this study demonstrates a conceptual framework of implementing immersive VR feedback using more convincing sensory feedback to aid training with BCI devices. Future work will test this protocol in SCI patients and with larger sample size.}, }
@article {pmid40040213, year = {2024}, author = {Okitsu, K and Isezaki, T and Obara, K and Nishimura, Y}, title = {Enhancing Brain Machine Interface Decoding Accuracy through Domain Knowledge Integration.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782166}, pmid = {40040213}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Animals ; Macaca mulatta ; Muscle, Skeletal/physiology ; Algorithms ; Humans ; }, abstract = {This paper introduces a novel decoding approach for Brain Machine Interface (BMI) that enhances the estimation accuracy and stability of muscle activity by incorporating domain knowledge of motor control. Our approach uniquely integrates domain knowledge, focusing on the relationship between torque direction and muscle activity in isometric wrist tasks. We demonstrate the effectiveness of our approach through decoding analysis with non-human primates performing a wrist torque tracking task. By implementing a Kalman filter augmented with models of muscle activity and torque for specific movement directions, we show significant improvements compared to vanilla Kalman filter in the accuracy of muscle activity estimation. The proposed approach presents a promising direction for enhancing the performance of BMI by leveraging domain-specific insights into motor control.}, }
@article {pmid40040208, year = {2024}, author = {Li, D and Shin, HB and Yin, K and Lee, SW}, title = {Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10781886}, pmid = {40040208}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification. Specifically, to learn and retain common features between subjects, we separate latent representations into subject-invariant and subject-specific features through adversarial training, while also proposing an extensible architecture to preserve features that are easily forgotten. Additionally, we incorporate a memory replay mechanism to reinforce previously acquired knowledge. Through extensive experiments, we demonstrate our framework's effectiveness in mitigating forgetting within the continual MI-EEG classification task.}, }
@article {pmid40040206, year = {2024}, author = {Huang, CM and Lai, WL and Yang, CC and Hsieh, YJ and Wu, CM and Lee, CH}, title = {EEG Channel Localization and Selection via Training with Noise Injection for BCI Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782774}, pmid = {40040206}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; }, abstract = {Electroencephalography (EEG) is crucial for monitoring brain activity in neuroscience and clinical applications. However, the multitude of channels recorded by scalp electrodes poses challenges, including impractical usage and high model complexity. This paper addresses the challenges of high dimensionality in EEG data and introduces an innovative EEG channel selection algorithm, LSvT-NI, based on model training and noise injection, achieving substantial reductions in channels, model size, and complexity while maintaining high classification accuracy. Validated through experiments on EEGNet and the BCI Competition IV 2a dataset, the algorithm proves beneficial for practical and cost-efficient scenarios. Specifically, experiments on the BCI Competition IV 2a dataset demonstrate that LSvT-NI with white noise and pink noise at 5dB SNR achieves a remarkable 77.3% and 72.7% reduction in channels, along with 11.7% and 11% reductions in model size, and 86.9% and 71.8% in computation complexity.}, }
@article {pmid40040181, year = {2024}, author = {Sartipi, S and Cetin, M}, title = {Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782060}, pmid = {40040181}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; }, abstract = {Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance.}, }
@article {pmid40040170, year = {2024}, author = {Meng, J and Yang, M and Zhang, S and Xu, M and Meng, L and Ming, D}, title = {An online brain-computer interface for a precise positioning of target based on rapid serial visual presentation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782815}, pmid = {40040170}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Evoked Potentials/physiology ; Brain/physiology ; Photic Stimulation ; }, abstract = {The brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) provides a novel approach for efficiently optimizing traditional machine-based target detection, revealing a broad application prospect in security, entrainment, monitoring, etc. A bottleneck of current RSVP-BCI is that its detectable result is limited to a binary way, i.e., target vs. non-target, more detailed and important information about targets, such as the precise position, remains undetectable. To solve this problem, this study investigated the relationship between targets positions (up, down, left, right) and electroencephalogram (EEG) characteristics, and tested the separability of EEGs induced by the four targets positions in an online RSVP-BCI. Twelve healthy subjects participated in this study, event-related potential (ERP), topographies, laterality index (LI), discriminant canonical pattern matching (DCPM) methods were used to analyzed the EEG data. Consequently, left-right targets induced ipsilateral ERPs between bilateral hemispheres; when targets appeared at up and down positions, opposite ERPs were found between frontal and occipital areas; up-down and left-right difference reached its maximum in the 140~190ms and 190~240ms temporal window, respectively. Single-trial classification showed five-class balanced accuracy (BACC) (non-target, target at up/ down/ left/ right position) was 71.02% and 67.91% for offline and online sessions, respectively. The results provide new understanding of the RSVP features for developing BCIs.}, }
@article {pmid40040166, year = {2024}, author = {Zhuo, F and Lv, B and Tang, F}, title = {Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782640}, pmid = {40040166}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {The existing Riemannian geometry-based approaches for brain computer interface (BCI) employ fixed time windows. However, the inherent variability and dynamic changes among subjects necessitate robust and adaptive solutions for time window optimization. Recognizing the current limitations of Riemannian classifiers, we propose a time window selection confidence metric (TWSCM) based on Riemannian geometry. This metric operates on the manifold of symmetric positive definite (SPD) matrices, providing a theoretically grounded and computationally efficient approach for time window optimization. The optimization process is unsupervised, which is able to deal with the online scenario without training labels. Experimental results on the BCI competition IV dataset IIa demonstrate that the classification performance is significantly improved for most subjects. The average performance over six subjects improved by 7.52%. The simulated online experiment shows enhanced performance in comparison to baseline experiments without time window optimization. Additionally, an in-depth analysis of TWSCM provides insights into performance variations among subjects. Overall, this paper introduces the first time window optimization method within the Riemannian geometric framework, presenting an effective and interpretable approach for optimizing time windows in motor imagery classification, providing a novel and promising perspective in EEG signal analysis.}, }
@article {pmid40040155, year = {2024}, author = {Huang, J and Tostado-Marcos, P and Narasimha, SM and Patel, AN and Arneodo, EM and Gentner, TQ and Mishne, G and Gilja, V}, title = {Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782761}, pmid = {40040155}, issn = {2694-0604}, mesh = {Animals ; *Finches/physiology ; *Vocalization, Animal/physiology ; Humans ; Brain/physiology ; Brain-Computer Interfaces ; Speech/physiology ; }, abstract = {State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.}, }
@article {pmid40040137, year = {2024}, author = {Idowu, OP and Kinney-Lang, E and Gulamhusein, A and Irvine, B and Kirton, A and Abou-Zeid, H}, title = {Profiling a Raspberry Pi-Based Motor Imagery Classification to Facilitate At-Home BCI for Children with Disabilities.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-7}, doi = {10.1109/EMBC53108.2024.10781873}, pmid = {40040137}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Children with Disabilities/rehabilitation ; Neural Networks, Computer ; Imagination/physiology ; Electroencephalography/methods ; Machine Learning ; }, abstract = {There has been incremental progress in moving BCI out of the laboratory environment and into the homes of those who would benefit most, especially children living with severe physical disabilities. Practical issues, such as available computational resources and long calibration times, have slowed down the adoption of such systems. To develop an efficient and scalable machine learning framework consistent with early approaches that facilitate at-home BCI use, this study provides valuable insights into measuring the behavioral characteristics of a Raspberry Pi 4 (RPi4) during the operation and execution of standard BCI processes, including the training and evaluation of classifier models. The results, which evaluated ten standard classifiers, including the Riemannian Geometry (RG) framework and more advanced deep learning approaches like Artificial Neural Network (ANN), were profiled on RPi4. These were compared to Desktop and MacBook computations for metrics such as training time, inference time, peak memory, and incremental memory usage, with computational bottlenecks identified. Our assessment revealed comparable performance metrics (84.3% of accuracy, recall, and f1_score, and 84.7% precision) for the neural network models despite the lower computational resources. Profiling results, including 1.74 sec training time, 0.405 sec inference time, 1154.9 MiB peak memory, and 405.2 MiB incremental memory usage, also demonstrated that the RPi4 is a potentially viable device for low-cost BCI systems. However, high-resource demanding classifiers such as ANN may need to be carefully considered in their implementation, which, in turn, will scale down the potential cost and complexity of adopting practical, impactful at-home BCI systems.}, }
@article {pmid40040110, year = {2024}, author = {Wang, Z and Liu, Y and Wu, W and Huang, S and An, X and Ming, D}, title = {EEG Pattern Comparison and Classification Performance of Motor Imagery Between Supernumerary and Inherent Limbs.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782967}, pmid = {40040110}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Movement/physiology ; Robotics ; Female ; Young Adult ; }, abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm" and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm. The results showed that imagining extra limbs induced more obvious event-related desynchronization (ERD) phenomenon in sensorimotor areas compared to imagining inherent limbs. Classification results indicate well separable performance among different mental tasks (all above 86%, with a maximum of 90.5%). This work proposed a novel type of MI paradigm, and offered new way for widening the control bandwidth of the BCI system.}, }
@article {pmid40040096, year = {2024}, author = {Lim, EY and Yin, K and Shin, HB and Lee, SW}, title = {Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781970}, pmid = {40040096}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Signal-To-Noise Ratio ; }, abstract = {Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification. Our approach employs data augmentation to strengthen model robustness, an autoencoder to extract features from baseline and MI signals, a feature alignment module to separate specific task and noise, and a classifier. We achieve state-of-the-art performance, an improvement of 5.9% and 3.7% on the BCIC IV 2a and 2b datasets, respectively. The qualitative analysis of our learned features proves superior representational power over baseline models, a critical aspect in dealing with noisy EEG signals. Our findings demonstrate the efficacy of readily available baseline signals in enhancing performance, opening possibilities for simplified BCI systems in brain-based communication applications.}, }
@article {pmid40040056, year = {2024}, author = {Huang, S and Liu, Y and Xu, W and Wang, Z and Ming, D}, title = {Enhancement of Functional Connectivity in Frontal-Parietal Regions After BCI-Actuated Supernumerary Robotic Finger Training.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781807}, pmid = {40040056}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiology ; Male ; *Robotics ; *Parietal Lobe/physiology/diagnostic imaging ; *Frontal Lobe/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Adult ; Female ; Young Adult ; }, abstract = {The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanisms of brain-actuated SRF training is not clear. In this study, a BCI-actuated SRF was used to investigate the concurrent changes in behavior and brain activity. After 4 weeks BCI-SRF training, the novel sequence operation accuracy rate enhanced by more than 350% compared with innate finger training (IFT). Task-based fMRI showed a significant increase in lateral activation of sensorimotor cortex and found a significant activation change in S1M1_L area. Moreover, BCI-SRF training significantly increase functional connectivity (FC) between S1M1_L and Frontal_Mid_L compared with IFT at post stage. And this FC increase in frontal-parietal is also significant at post vs pre in BCI-SRF group and significantly correlated with the improvement of motor sequence accuracy rate. Our findings provide useful insights into the enhanced human-machine interaction and this efficacy exhibited significant potential for clinical rehabilitation application.}, }
@article {pmid40040035, year = {2024}, author = {Norouzi, M and Amirani, MZ and Shahriari, Y and Abiri, R}, title = {Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782784}, pmid = {40040035}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Young Adult ; }, abstract = {In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.}, }
@article {pmid40040033, year = {2024}, author = {Ferdous, TR and Pollonini, L and Francis, JT}, title = {Enhancing Auditory BCI Performance: Incorporation of Connectivity Analysis.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782147}, pmid = {40040033}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Support Vector Machine ; Brain/physiology/diagnostic imaging ; Acoustic Stimulation ; }, abstract = {Brain connectivity analysis to classify auditory stimuli applicable to invasive auditory BCI technology, particularly intracranial electroencephalography (iEEG) remains an exciting frontier. This study revealed insights into brain network dynamics, improving analysis precision to distinguish related auditory stimuli such as speech and music. We thereby contribute to advancing auditory BCI systems to bridge the gap between noninvasive and invasive BCI by utilizing noninvasive BCI methodological frameworks to invasive BCI (iEEG) data. We focused on the viability of using connectivity matrices in BCI calculated across brain waves such as alpha, beta, theta, and gamma. The research highlights that the traditional machine learning classifier, Support Vector Machine (SVM), demonstrates exceptional capabilities in handling brain connectivity data, exhibiting an outstanding 97% accuracy in classifying brain states, surpassing previous relevant studies with an improvement of 9.64% The results are significant as we show that neural activity in the gamma band provides the best classification performance using connectivity matrices calculated with Phase Locking Values and Coherence methods.}, }
@article {pmid40040032, year = {2024}, author = {Veeranki, YR and Posada-Quintero, HF}, title = {High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782482}, pmid = {40040032}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Algorithms ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; Adult ; Emotions/physiology ; }, abstract = {Affective computing is a critical aspect of human-computer interaction. Electroencephalographic (EEG) signals, which reflect electrical brain activity, are widely used for the understanding of human emotional states. However, these signals are nonlinear and nonstationary, making traditional analysis methods insufficient. To address these challenges, recent studies have focused on time-frequency analysis. In this paper, we propose a variable frequency complex demodulation (VFCDM) approach to obtain high-resolution time-frequency spectra (TFS) from EEG signals. First, we compute the TFS using the time-varying optimal parameter search technique to capture the spectral information. Then we generate VFCDM sub-bands and extract statistical features from each of the sub-bands. These features are then used with the Random Forest algorithm to classify arousal and valence dimensions. Our results demonstrate the robustness of this approach and its ability to accurately discriminate complex affective dimensions. The δ-VFCDM and γ-VFCDM bands produced the highest F1 scores of 71.80% for Arousal and 69.55% for Valence differentiation. This work significantly advances EEG-based affective computing and opens avenues for more emotionally attuned human-computer interaction systems.}, }
@article {pmid40039972, year = {2024}, author = {Ye, H and Goerttler, S and He, F}, title = {EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782694}, pmid = {40039972}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Electrodes ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; }, abstract = {Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.}, }
@article {pmid40039945, year = {2024}, author = {Kanda, T and Isezaki, T and Okitsu, K}, title = {A Study on Changes in Estimation Accuracy for EEG Data During Calibration and Operation in MI-BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782616}, pmid = {40039945}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Calibration ; Male ; Female ; Adult ; Young Adult ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; }, abstract = {Changes in psychological factors have been suggested to cause variations in brain-computer interface (BCI) performance. More specifically, differences in psychological variables between the calibration and operation phases may cause a decrease in accuracy during operation, presenting a potential challenge for the adoption of BCI technology. The purpose of this study is to analyze the differences in accuracy between the calibration and operation phases of a BCI using a deep learning model. We structured tasks to simulate the calibration and operation phases, and participants performed motor imagery tasks under both conditions. The analysis revealed a significant decrease in accuracy for data obtained under the operation condition, highlighting the need for techniques capable of adapting to the electroencephalography signal data produced when users execute operations.}, }
@article {pmid40039943, year = {2024}, author = {Wang, J and Li, X and Huang, Y and Xiao, D and Fan, Y and Huang, W and Hu, Y}, title = {Patient-Involved Validation of A Somatosensory ERP-BCI Facilitated by Electric Stimulation for Stroke Rehabilitation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10781706}, pmid = {40039943}, issn = {2694-0604}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Electric Stimulation/methods ; Female ; Evoked Potentials, Somatosensory/physiology ; Adult ; }, abstract = {Brain-computer interface (BCI) is emerging as an effective complementary solution in the field of rehabilitation for the interaction between patients and robotic assistive devices. Specifically, the somatosensory event-related potentials (ERP) BCI has unique advantage for post-stroke motor rehabilitation scenarios and has been proven feasible on healthy subjects. We conducted the first patient-involved somatosensory ERP-BCI experiment with electric stimulation to evaluate its feasibility for real-world clinical usage. In the experiment, participant selectively attended to electric stimuli applied on either left or right wrist, which represented the operation of robot-assisted exercise of corresponding hand. An integrated platform that included exercise, stimulation, and electroencephalography (EEG) sampling modules was used. For evaluation, we used convolutional neural network (CNN) with transformer module to construct subject-specific intent decoder. The network demonstrated on average 58.95% accuracy in classifying target response from a single ERP trial. When using the classification from multiple consecutive trials, the decoder achieved a maximum of 80.12% mean accuracy in recognizing participants intent, and the highest rate from a single participant was 97.21%. The best information transfer rate (ITR) achieved was 1.956 Bit/min. These results demonstrated that the proposed BCI paradigm could be a valid choice for stroke rehabilitation. In the next stage, we anticipate the involvement of larger patient population, real-time feedback training, and the subsequent quantified motor function recovery results.}, }
@article {pmid40039935, year = {2024}, author = {Han, HT and Kim, SJ and Lee, DH and Lee, SW}, title = {Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782698}, pmid = {40039935}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals). When conducting domain adaptation (DA) on MI signals with sparse MI signals, it could interrupt the training process. In this paper, we proposed the proxy-based masking module (PMM) for masking sparse MI signals within MI signals. The proposed module was designed to suppress the amplitude of sparse MI signals using the negative similarity-based mask generated between the proxy of rest signals and the feature vectors of MI signals. We attached our proposed module to the conventional DA methods (i.e., the DJDAN, the MAAN, and the DRDA) to verify the effectiveness in the cross-subject environment on dataset 2a of BCI competition IV. When our proposed module was attached to each conventional DA method, the average accuracy was improved by much as 4.67 %, 0.76 %, and 1.72 %, respectively. Hence, we demonstrated that our proposed module could emphasize the information related to MI characteristics. The code of our implementation is accessible on GitHub.[1].}, }
@article {pmid40039926, year = {2024}, author = {Zhong, Y and Yao, L and Wang, Y}, title = {Enhanced BCI Performance using Diffusion Model for EEG Generation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782900}, pmid = {40039926}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {In the realm of Motor Imagery (MI)-based Brain-Computer Interface (BCI), the widespread adoption of deep learning-based algorithms has resulted in an increased demand for a larger training sample size, thereby placing a heightened burden on users. This study advocates the utilization of one of the most advanced generative models, the denoising diffusion probabilistic model (DDPM), for the artificial synthesis of Electroencephalogram (EEG) raw signals. The quality of the generated EEG signals is evaluated through both qualitative and quantitative analyses. Through dimensionality reduction projection, we observed a notable similarity in the data distributions between the generated EEG signals and real EEG signals. Additionally, spectral analysis indicates a striking similarity in energy distribution between the two, accompanied by the presence of an event-related synchronization (ERS) phenomenon in the generated EEG signals. Quantitative analysis reveals that the accuracy of generated EEG signals for left and right-hand motor imagery tasks is 89.81 ± 2.11%, with discriminative information related to classes predominantly concentrated in the motor-sensory cortex area and alpha-beta frequency band. Furthermore, the integration of generated EEG samples contributes to a 3.17% improvement in the classification performance of BCI-deficiency subjects. These artificially generated EEG signals exhibit promising potential for application in calibrating MI-BCI deep learning models, thereby alleviating the burden on participants.}, }
@article {pmid40039924, year = {2024}, author = {Hoshino, T and Kanoga, S and Aoyama, A}, title = {Channel- and Label-Flip Data Augmentation for Motor Imagery-Based Brain-Computer Interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782028}, pmid = {40039924}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Machine Learning ; Movement/physiology ; Algorithms ; Brain/physiology ; }, abstract = {Achieving high classification accuracy in motor-imagery-based brain-computer interfaces (BCIs) requires substantial amounts of training data. A challenge arises because of the impracticality of measuring large amounts of data from users. Data augmentation (DA) has emerged as a promising solution for this challenge. We propose a novel DA method called channel&amp;label-flip DA that involves not only flipping channels but also flipping class labels. This method is based on the neuroscience finding that motor imageries of left- and right-hand movements are roughly symmetrical. The efficiency of the proposed method was evaluated using the OpenBMI dataset, which comprises electroencephalograms collected from 54 participants engaged in left- and right-hand motor imagery tasks. To compare the impact on classifiers, we employed three classical machine learning models utilizing filter bank common spatial pattern features, along with a deep learning-based model that uses raw signal input. As a result, the channel&amp;label-flip DA improved the classification accuracy on average, whereas simple flipping of the channels reduced the classification accuracy compared to the case without DA.}, }
@article {pmid40039912, year = {2024}, author = {Tan, J and Wang, Y}, title = {Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782800}, pmid = {40039912}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Reward ; *Algorithms ; Reinforcement, Psychology ; Machine Learning ; Feedback ; }, abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p<0.01). These preliminary results suggests that the dynamic IRL method for feedback-driven reward estimation holds potential for improving the design of RL-based BMIs.}, }
@article {pmid40039908, year = {2024}, author = {Patel, K and Safavi, F and Chandramouli, R and Vinjamuri, R}, title = {Transformer-Based Emotion Recognition with EEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781700}, pmid = {40039908}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; }, abstract = {Emotion recognition via electroencephalography (EEG) has emerged as a pivotal domain in biomedical signal processing, offering valuable insights into affective states. This paper presents a novel approach utilizing a tailored Transformer-based model to predict valence and arousal levels from EEG signals. Diverging from traditional Transformers handling singular sequential data, our model adeptly accommodates multiple EEG channels concurrently, enhancing its ability to discern intricate temporal patterns across the brain. The modified Transformer architecture enables comprehensive exploration of spatiotemporal dynamics linked with emotional states. Demonstrating robust performance, the model achieves mean accuracies of 92.66% for valence and 91.17% for arousal prediction, validated through 10-fold cross-validation across subjects on the DEAP dataset. Trained for subject-specific analysis, our methodology offers promising avenues for enhancing understanding and applications in emotion recognition through EEG. This research contributes to a broader discourse in biomedical signal processing, paving the way for refined methodologies in decoding neural correlates of emotions with implications across various domains including brain-computer interfaces, and human-robot interaction.}, }
@article {pmid40039888, year = {2024}, author = {Sturgill, BS and Jiang, MS and Jeakle, EN and Smith, TJ and Hoeferlin, GF and Duncan, J and Thai, TTD and Hess, JL and Alam, NN and Hernandez-Reynoso, AG and Capadona, JR and Pancrazio, JJ}, title = {Antioxidant Coated Microelectrode Arrays: Effects on Putative Inhibitory and Excitatory Neurons.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10781940}, pmid = {40039888}, issn = {2694-0604}, mesh = {*Microelectrodes ; *Neurons/physiology ; Animals ; *Antioxidants/pharmacology ; Metalloporphyrins/pharmacology ; Rats ; Coated Materials, Biocompatible/chemistry ; Brain-Computer Interfaces ; }, abstract = {Intracortical microelectrode arrays (MEAs) are used to record neural activity in vivo at single-cell resolution for both neuroscience studies and for engineering restorative devices such as brain-computer interfaces (BCIs). The recording performance of these devices are known to degrade over weeks to months after implantation due, in part, to neuroinflammation and oxidative stress. Characterizing and mitigating the degradation of recording performance is of particular interest for chronic applications. Literature suggests that inhibitory neurons may be more susceptible to oxidative stress than excitatory neurons. In this study, we classify recorded neural signals as either putative inhibitory or excitatory based on their waveform characteristics and aim to identify if one preferentially benefits from the use of a Mn(III)tetrakis94-benzoic acid)porphyrin (MnTBAP) coating to reduce reactive oxygen species, which we have previously demonstrated improves chronic neural recordings. In this study, we found that the MnTBAP coating affects these two classes of neurons differently, depending on the cortical depth. The MnTBAP coating improves the number of putative inhibitory signals recorded on the middle electrode sites (L5) and putative excitatory units on the superficial (L2/3 &amp; L4) electrode sites. Our results suggest that decreases in recording performance may be influenced by both cortical depth and neuronal cell type. Furthermore, we show that the benefits of a MnTBAP coating to chronic neural recordings differ between putative inhibitory and excitatory neurons with a depth dependence.}, }
@article {pmid40039787, year = {2024}, author = {Won, E and Lim, S and Kim, Y and Dong, SY}, title = {Toward the TCN-based Real-Time BCI System for Target Detection.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782928}, pmid = {40039787}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Deep Learning ; Male ; Adult ; Neural Networks, Computer ; }, abstract = {This study focuses on developing a real-time Brain-Computer Interface (BCI) system, specifically designed for military applications, to enhance target detection in rapid serial visual presentation (RSVP) tasks. The proposed BCI system utilizes electroencephalogram (EEG) signals based on dry electrodes, known for their exceptional temporal resolution, to identify swiftly specific target symbols within sequences of visual stimuli. Leveraging deep learning techniques, particularly Temporal Convolutional Networks (TCN), this study demonstrates the accuracy and efficiency improvement in target detection for RSVP tasks. According to our findings, the adaptability and efficacy of TCN in handling temporal dynamics of EEG signals exhibit outstanding performance in target detection, thus offering the potential for accurate and efficient real-time BCI system.}, }
@article {pmid40039782, year = {2024}, author = {Park, JH and Lee, SH and Lee, SW}, title = {Towards EEG-based Talking-face Generation for Brain Signal-driven Dynamic Communication.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10781922}, pmid = {40039782}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology/diagnostic imaging ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Face/physiology ; Speech/physiology ; Communication ; }, abstract = {Research on decoding speech or generating images from human brain activity holds intriguing potential as neuroprosthesis for patients and innovative communication tools for general users. However, previous studies have been constrained in generating fragmented or abstract outputs, rendering them less applicable for serving as an alternative form of communication. In this paper, we propose an integrated framework that synthesizes speech from non-invasive speech-related brain signals and generates a talking-face that performs "lip-sync" using intermediate input decoded from brain signals. For realistic and dynamic brain signal-mediated communication, we generated a personalized talking-face by utilizing various forms of target data such as a real face or an avatar. Additionally, we performed a denoising process to enhance the quality of synthesized voices from brain signals, and to minimize unnecessary facial movements according to the noise. Therefore, clear and natural talking-faces, applicable to both real faces and avatars, could be generated from noisy brain signals, enabling dynamic communication. These findings serve as a pivotal contribution to the advancement of brain signal-driven face-to-face communication through the provision of integrated speech and visual interfaces. This represents a significant step towards the development of a more intuitive and dynamic brain-computer interface communication system.}, }
@article {pmid40039768, year = {2024}, author = {He, F and Zhang, S and Yang, M and Meng, J and Xu, M and Meng, L and Ming, D}, title = {Prediction errors from distinct perspectives induce separable EEG features for brain-computer interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781933}, pmid = {40039768}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Algorithms ; Female ; Adult ; Young Adult ; Evoked Potentials/physiology ; }, abstract = {The ability to efficiently detect error is fundamental for human adaptive behaviors, and plays an increasingly crucial role in developing more intelligent brain-computer interface (BCI). Error-related potential (ErrP), which can reflect prediction error, has been widely used by the BCI to read whether outcomes accord with users' expectation or not. However, current ErrP-BCI cannot distinguish the prediction error is induced by user's own (first-person perspective, 1PP) or other's (third-person perspective, 3PP) wrong action, hindering it from being applied in social interactions. This study used virtual reality (VR) to make subjects aware of prediction errors from the first- or third-person perspective, and recorded electroencephalogram (EEG) data of 22 healthy subjects. Event-related potential (ERP), event-related spectral perturbation (ERSP), inter-trial coherence (ITC) and shrinkage discriminant canonical pattern matching (SKDCPM) algorithm were used to investigate EEG features and the separability of prediction errors induced by distinct perspectives. Consequently, ErrP induced by the 1PP emerged significantly earlier than that of 3PP, and caused greater ERSP and ITC in the prefrontal region in the theta and alpha bands. Decoding result achieved 76.4%± 9.13% accuracy for the two types of errors (1PP-incorrect vs 3PP-incorrect). This study fills in the fine-grained classification of different error types and provides a finer metric for the systematic error correction efficiency of two-person collaborative brain control, which is the basis for future human-machine hybrid intelligence.}, }
@article {pmid40039762, year = {2024}, author = {Li, M and Wang, M and Wang, Y}, title = {An Adaptive Superposition Point Process Model with Neuronal Encoding Engagement Identification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781885}, pmid = {40039762}, issn = {2694-0604}, mesh = {*Neurons/physiology ; *Brain-Computer Interfaces ; *Models, Neurological ; Animals ; Humans ; Algorithms ; }, abstract = {Neuronal encoding is realized by modulating firing rates in response to various encoding factors, including external stimuli, behaviors, and complex neural interactions. The neuronal encoding engagement of various factors are dynamic, which reflects how neurons aggregate different information. The process of uncovering the extent to which these factors contribute to neuronal encoding over time is neuronal encoding engagement identification. Brain-machine interface (BMI) establishes a closed-loop framework to investigate how neurons response to different encoding factors. Since neurons don't fully participate in encoding one specific factor, accurate encoding engagement identification contributes to leveraging encoding and decoding in more naturalistic BMI application scenarios. However, previous works focus on modeling and estimating tuning properties instead of analyzing the neuronal information aggregation. We develop a dual adaptive superposition point process filter (DASPPF), which explicitly incorporates various encoding factors. DASPPF not only enables decoding kinematics but also identifies the engagement of individual kinematics encoding and functional neural connectivity encoding. DASPPF is validated on numerical simulations of monkey circle-tracking tasks. The proposed method can effectively promote decoding performance and uncover how neurons engage themselves in different effects with point process observation, which may help enhance the development of neurotechnologies.}, }
@article {pmid40039684, year = {2024}, author = {Tang, Y and Robinson, N and Fu, X and Thomas, KP and Wai, AAP and Guan, C}, title = {Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781850}, pmid = {40039684}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Movement/physiology ; *Hand Strength/physiology ; Adult ; *Brain-Computer Interfaces ; Male ; Female ; Hand/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; }, abstract = {Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). While MTP provides continuous control signals suitable for high-precision tasks, its feasibility and applications are challenged by the low signal-to-noise ratio, especially in noninvasive settings. Previous research has predominantly focused on kinematic reconstruction of upper (e.g., arm reaching) and lower limbs (e.g., gait). However, finger movements have received much less attention, despite their crucial role in daily activities. To address this gap, our study explores the potential of noninvasive Electroencephalography (EEG) for reconstructing finger movements, specifically during hand grasping actions. A new experimental paradigm to collect multichannel EEG data from 20 healthy subjects, while performing full, natural hand opening and closing movements, was designed. Employing state-of-the-art deep learning algorithms, continuous decoding models were constructed for eight key finger joints. The Convolutional Neural Network with Attention approach achieved an average decoding performance of r=0.63. Furthermore, a post-hoc metric was proposed for hand grasp cycle detection, and 83.5% of hand grasps were successfully detected from the reconstructed motion signals, which can potentially serve as a new BCI command. Explainable AI algorithm was also applied to analyze the topographical relevance of trained features. Our findings demonstrate the feasibility of using EEG to reconstruct hand joint movements and highlight the potential of MTP-BCI in control and rehabilitation applications.}, }
@article {pmid40039675, year = {2024}, author = {Tang, C and Jiang, D and Chen, B}, title = {MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782066}, pmid = {40039675}, issn = {2694-0604}, mesh = {*Magnetoencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Entropy ; Imagination/physiology ; Algorithms ; Movement/physiology ; }, abstract = {Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p < 0.05) improve single-session classification accuracy and greatly reduce the number of MEG channels. Cross-session classification also outperforms the competing method.}, }
@article {pmid40039672, year = {2024}, author = {Irvine, B and Abou-Zeid, H and Kirton, A and Kinney-Lang, E}, title = {Benchmarking motor imagery algorithms for pediatric users of brain-computer interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782164}, pmid = {40039672}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Adolescent ; *Algorithms ; Child, Preschool ; Male ; Benchmarking ; Female ; Electroencephalography/methods ; Imagination/physiology ; }, abstract = {Brain-computer interfaces (BCIs) can enable opportunities for self-expression and life participation for children with severe neurological disabilities. Unfortunately, the development and evaluation of state-of-the-art algorithms has largely neglected pediatric users. This work tests 12 state-of-the-art algorithms for motor imagery classification on three datasets of typically developing pediatric users (n=94 ages 5-17). When all datasets were combined, there were no significant differences between most non-deep learning algorithms, with all having a mean AUC score of 0.64 or 0.65. All the non-deep learning algorithms significantly outperformed the deep learning algorithms, which can be partially attributed to a lack of hyperparameter tuning. The best of the deep learning algorithms was ShallowConvNet, with a mean AUC score of 0.57. Of the algorithms tested, only the filter bank common spatial pattern (FBCSP) and ShallowConvNet exhibited significant age effects. This general lack of age effects, combined with examples of children as young as 6 having AUC scores as high as 0.8, provides evidence that young children are capable of producing measurable motor imagery activations. The age effects that were present for some algorithms suggest that the changing EEG patterns associated with development could have a measurable impact on classification algorithm outcomes, and such algorithms should be evaluated to ensure that they are not performing disproportionately poorly for younger children. This work serves as a first step towards ensuring that the state-of-the-art improvements in BCI classification can be evaluated, and where necessary, adapted to meet the needs of pediatric users.}, }
@article {pmid40039651, year = {2024}, author = {Kolbl, N and Tziridis, K and Krauss, P and Schilling, A}, title = {Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-7}, doi = {10.1109/EMBC53108.2024.10782081}, pmid = {40039651}, issn = {2694-0604}, mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; Evoked Potentials, Auditory/physiology ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Acoustic Stimulation ; Brain/physiology ; }, abstract = {Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.}, }
@article {pmid40039633, year = {2024}, author = {Lu, JB and Tsao, Y and Wang, YT}, title = {Design and Evaluate Semi-dry Watermill-like EEG Electrodes.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782960}, pmid = {40039633}, issn = {2694-0604}, mesh = {*Electroencephalography/instrumentation/methods ; *Electrodes ; Humans ; *Equipment Design ; Brain-Computer Interfaces ; }, abstract = {Semi-dry electrodes act as the middle ground between wet and dry electrodes as they not only have similar contact features (equivalent circuit) with Ag/AgCl-based wet electrodes but also carry the conduct material in their cavity or sponge (e.g. absorb saline water) for long-term brain-computer interface(BCI) applications. However, the trade off between hair-layer penetration and dose control of conductive material is challenging e.g. two electrodes might be bridged when the headset continuously presses or squeezes the reservoir and electrolyte flows on the scalp. The goal of this study is to design, prototype, and evaluate watermill-like electroencephalogram (EEG) electrodes that aim to simultaneously overcome two issues: hair-layer penetration and dose control of conductive material. Two electrode profiles, straight and spiral, were 3D printed, coated and evaluated with participants' EEGs. Without any help from skilled technicians, the self-wearing mechanical design allows users to wear and acquire their EEGs in few minutes. In addition, the refillable reservoir enable the possibility for long-term BCI applications. The results show that the proposed electrodes can read neural activities on the hair-covered area. Furthermore, straight profile electrodes outperform the spiral profile in the steady-state visually evoked potential (SSVEP) response. In sum, the watermill-like EEG electrodes can shorten the preparation time as well as the dose control of conductive material for naive users. The results suggest the proposed electrodes might open opportunities for BCI users to develop real-world BCI applications in the future.}, }
@article {pmid40039626, year = {2024}, author = {Lin, X and Eldele, E and Chen, Z and Wu, M and Ng, HW and Guan, C}, title = {Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782755}, pmid = {40039626}, issn = {2694-0604}, mesh = {Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks (CNNs) have significantly enhanced decoding accuracy. Nevertheless, the majority of these CNN designs did not explicitly incorporate the inter-hemisphere functional connections, omitting crucial spatial information. Notably, in binary MI decoding of the left-hand versus right-hand, the Event-Related Desynchronization is observed in the contralateral hemisphere. Building upon this concept and various Neuroscience research, we have designed a CNN architecture that forges a functional connection between the two hemispheres. Specifically, we applied the Channel Average Referencing to one hemisphere and compared the output with all channels of the opposite hemisphere. Then, we utilized the cosine similarity to identify the most correlated channels and combined with them the original hemisphere for spatial filtering to learn the inter-hemispheric connections. This innovative technique aligns more closely with the actual brain functionality. Our method has demonstrated superior results on the Cho2017 and OpenBMI datasets, underscoring its effectiveness.}, }
@article {pmid40039616, year = {2024}, author = {Guerrero-Mendez, CD and Rivera-Flor, H and Villa-Parra, AC and Bastos-Filho, TF}, title = {Exploring Novel Practical Approach to Post-Stroke Upper-Limb Neurorehabilitation Based on Complex Motor Imagery Tasks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10782286}, pmid = {40039616}, issn = {2694-0604}, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; Male ; Electroencephalography/methods ; Female ; Stroke/physiopathology/complications ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Middle Aged ; Neurological Rehabilitation/methods ; Adult ; }, abstract = {Motor imagery (MI) is one of the main strategies for upper-limb movement rehabilitation in post-stroke individuals. Promising results of MI applied for rehabilitation have been reported in the literature. However, there is currently a need related to the recovery of movements aimed to Activities of Daily Living (ADLs) for individuals with severe motor impairments. Therefore, this study presents the evaluation of a novel MI protocol for post-stroke upper-limb neurorehabilitation using complex tasks related to the manipulation of a drinking cup. The protocol is based on the Action Observation (AO), which was used under a first-person 2D virtual reality. Subjects had to simultaneously imagine the movements presented in AO for the manipulation of a cup varying in four positions. EEG signals were recorded from 16 channels located mainly in the motor cortex of the brain. Two computational strategies based on Riemannian Geometry (RG) with and without Feature Selection (FS) using Pair-Wise Feature Proximity (PWFP) were implemented for the binary identification of each complex MI-Task vs. MI-Rest. This approach was evaluated on 30 healthy individuals and 2 post-stroke individuals. Using Linear Discriminant Analysis (LDA) as a classifier, the results report a maximum accuracy of 0.78 for both healthy and post-stroke individuals, and a minimum FPR of 0.21 and 0.13 for healthy and post-stroke individuals, respectively. This highlights the potential use of this type of paradigms for the implementation of more robust BCI systems that allow the rehabilitation of movements close to ADLs. Therefore, complex MI tasks may be a suitable variant for rehabilitation in post-stroke individuals.}, }
@article {pmid40039598, year = {2024}, author = {Noble, SC and Ward, T and Ringwood, JV}, title = {Assessing the Impact of Environment and Electrode Configuration on P300 Speller Performance and EEG Signal Quality.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782158}, pmid = {40039598}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/instrumentation ; *Electrodes ; Adult ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; Environment ; }, abstract = {Recent years have seen extensive use of brain-computer interfaces (BCIs) using electroencephalography (EEG). A critical element in BCI research is electrode selection, which influences performance, experiment duration, resource utilization, and consequently, cost. Electrode choice is partly dictated by the study location, as environmental electrical noise can impact EEG signal quality. This study evaluates the performance of a P300 speller and EEG signal quality using 4-, 6-, 8-, and 16-electrode configurations in two different office environments. Ten healthy adults participated in a single session, using a P300 speller to spell three words with each electrode set. Participants were split between two locations, with five individuals in each. Significant performance disparities were observed between the locations. Notably, within each location, the performance differences among 4-, 6-, and 8-electrode sets were minimal; only the 16-electrode set outperformed the others in both settings. The location associated with poorer performances also exhibited lower P300 amplitudes and higher levels of mains electricity noise.}, }
@article {pmid40039596, year = {2024}, author = {Ben Ticha, MB and Ran, X and Roussel, P and Bocquelet, F and Le Godais, G and Aubert, M and Costecalde, T and Struber, L and Zhang, S and Charvet, G and Kahane, P and Chabardes, S and Yvert, B}, title = {A Vision Transformer Architecture For Overt Speech Decoding From ECoG Data.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781877}, pmid = {40039596}, issn = {2694-0604}, mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Algorithms ; Electrocorticography/methods ; }, abstract = {Speech Brain-Computer Interfaces rely on decoding algorithms that transform neural activity into speech. A current challenge is to achieve intelligible speech synthesis in real time from continuous ongoing brain activity, ideally without the need of language models that prevent free-speech production. As a first step toward this goal, we introduce here an encoder-decoder architecture, in which neural data is first encoded into a latent space using a multi-layer vision transformer (ViT), and then these latent variables are converted into acoustic coefficients using a bidirectional LSTM recurrent network. This network is compared to a more conventional architecture where the encoding is performed using a convolutional neural network. Moreover, we introduce a new data-driven data augmentation strategy based on Dynamic Time Warping (DTW) to increase a training dataset based on the intrinsic variability of its input neural features. On two ECoG datasets obtained in participants performing an overt speech task, we found that ViT-encoding outperforms CNN-encoding to predict produced speech offline and that DTW-based data augmentation also improves decoding performance.}, }
@article {pmid40039592, year = {2024}, author = {Schrag, E and Comaduran-Marquez, D and Kirton, A and Kinney-Lang, E}, title = {Textured Stimuli Comfort and Response in SSVEP-Based Brain Computer Interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782587}, pmid = {40039592}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Adult ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; Electroencephalography/methods ; }, abstract = {State of the art steady-state visual evoked potential (SSVEP) brain computer interface (BCI) stimuli are commonly high-contrast, solid color flashing objects which can contribute to visual discomfort and fatigue. The use of low-contrast, textured flashing stimuli is proposed as a more comfortable alternative stimulus presentation paradigm. Eight participants (aged 19-35) were presented with four textured stimuli at varying frequencies, alongside standard stimuli. Results indicate significant effects of stimulus type as well as an interaction between frequency and channel subset on signal-to-noise ratio (SNR) values. Comfort scores consistently favored textured stimuli over high-contrast options at all frequencies The observed lack of SNR differences between stimulus conditions supports the feasibility of using textured stimuli in BCIs. This study lays a foundation for developing comfortable and effective BCI systems. The promising results of textured stimuli suggest a potential alternative for SSEVP-based BCI systems, emphasizing the importance of balancing neural responses and user comfort in stimulus design.}, }
@article {pmid40039576, year = {2024}, author = {Jiang, R and Qiu, S and Wang, Y and Zhang, C and He, H}, title = {Evaluation of EEG and MEG responses during Fine Motor Imagery from the same limb.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782038}, pmid = {40039576}, issn = {2694-0604}, mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; Male ; *Brain-Computer Interfaces ; Adult ; *Imagination/physiology ; Female ; Movement/physiology ; }, abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. BCI systems based on fine MI can provide an intuitive control pathway of the outer device. Electroencephalography (EEG) is a widely used modality for MI due to its high temporal resolution and portability. Magnetoencephalography (MEG) has high spatial and temporal resolution, which has received more and more attention. This study designed four kinds of MI tasks of different joints from the same upper limb, including finger, wrist, elbow, and shoulder joints, and additionally added a resting task. The EEG and MEG signals of eight subjects were acquired synchronously. Analysis was conducted on the EEG and MEG data to find the time, time-frequency, and spatial difference between MI tasks of different joints from the same limb. The induced event-related desynchronization (ERD) in EEG signals at the electrode position of the left motor area are more broad and stronger in the alpha frequency band than that in MEG signals during fine MI tasks. From the topographical distribution, different MI tasks affects the area and intensity of the activated area, and topographical distribution of MEG signals in different MI tasks are more discriminative than that of EEG signals. Moreover, the analysis of movement-related cortical potentials (MRCP) showed that significant negative potentials were detected near the onset of the motor imagery events and there is a significant difference in temporal dimension between magnetoencephalogram and electroencephalogram signals. The work implies that there exist the separable differences between EEG and MEG during fine MI tasks, which can be utilized to build a multimodal classification method for fine MI-BCI systems.}, }
@article {pmid40039573, year = {2024}, author = {Delavari, F and Santaniello, S}, title = {Role of Scalp EEG Brain Connectivity in Motor Imagery Decoding for BCI Applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781532}, pmid = {40039573}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Brain/physiology/diagnostic imaging ; Scalp/physiology ; Imagination/physiology ; }, abstract = {Brain Connectivity (BC) features of multichannel EEG have been proposed for Motor Imagery (MI) decoding in Brain-Computer Interface applications, but the advantages of BC features vs. single-channel features are unclear. Here, we consider three BC features, i.e., Phase Locking Value (PLV), Granger Causality, and weighted Phase Lag Index, and investigate the relationship between the most central nodes in BC-based networks and the most influential EEG channels in single-channel classification based on common spatial pattern filtering. Then, we compare the accuracy of MI decoders that use BC features in source vs. sensor space. Applied to the BCI Competition VI Dataset 2a (left- vs. right-hand MI decoding), our study found that PLV in sensor space achieves the highest classification accuracy among BC features and has similar performance compared to single-channel features, while the transition from sensor to source space reduces the average accuracy of BC features. Across all BC measures, the network topology is similar in left- vs. right-hand MI tasks, and the most central nodes in BC-based networks partially overlap with the most influential channels in single-channel classification.}, }
@article {pmid40039515, year = {2024}, author = {Marquez, DC and Minhas, A and Kinney-Lang, E and Kirton, A}, title = {Automated Hyper-Parameter Optimization for Eye Movement Artifact Removal.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782395}, pmid = {40039515}, issn = {2694-0604}, mesh = {Humans ; *Artifacts ; *Electroencephalography/methods ; *Eye Movements/physiology ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interface (BCI) systems allow users to control external devices with their brain waves. However, electroencephalography (EEG) signals used by most BCI systems are prone to artifacts from various sources (e.g., muscle activity, eye movements, and electrical interference). These artifacts can degrade the performance and usability of BCI systems. Many tools exist to eliminate these artifacts. However, not all methods are automated, and some might require tuning certain hyper-parameters for optimal performance. We propose a method to automatically optimize the hyper-parameters of an eye blink artifact removal tool to improve the removal of artifacts in resting state EEG. We use a subset of eye movement artifacts to optimize the hyper-parameters using the EEG Quality Index (EQI) as the objective function. The optimized hyper-parameters are then used in a test artifact to quantify the improvement of the EQI. Results show improvement in the EQI when compared to the default artifact removal hyper-parameters, and raw EEG traces. We conclude that our method can provide a personalized and robust artifact removal solution for BCI users with complex needs.}, }
@article {pmid40039504, year = {2024}, author = {Osborn, LE and Christie, B and McMullen, DP and Thomas, TM and Thompson, MC and Nickl, RW and Pawar, AS and Wester, BA and Cantarero, GL and Celnik, PA and Crone, NE and Fifer, MS and Tenore, FV}, title = {Artificial touch feedback using microstimulation of human somatosensory cortex to convey grip force from a robotic hand.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782061}, pmid = {40039504}, issn = {2694-0604}, mesh = {Humans ; *Somatosensory Cortex/physiology/physiopathology ; *Robotics/instrumentation ; *Hand Strength/physiology ; *Hand/physiology ; *Touch/physiology ; Brain-Computer Interfaces ; Male ; Electric Stimulation ; Feedback, Sensory/physiology ; }, abstract = {Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living with sensorimotor deficits. However, the extent of tactile information that can be conveyed through ICMS has not been fully explored. In a human participant with spinal cord injury and chronically implanted microelectrode arrays, we used ICMS to the somatosensory cortex to provide grip force feedback in the hands during grasping of objects with varying stiffness with a robotic arm. Using only ICMS-evoked touch sensations, the participant was able to identify between two and three objects with an accuracy of 92% and 67%, respectively. In a compliant grasping task with the goal of grasping a delicate object without crushing it, objects were deformed on average only 2.8 mm with ICMS-based touch feedback compared to 8.7 mm without. These results demonstrate that ICMS-evoked touch sensations to the hands can be used to provide force-based feedback for perceiving object properties and enable more precise grasping during closed-loop control of a robotic limb through a cortical interface.}, }
@article {pmid40039500, year = {2024}, author = {Teymourlouei, A and Hu, M and Gentili, R and Reggia, J}, title = {Functional Connectivity Methods for Multi-Class Mental Workload Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782848}, pmid = {40039500}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Workload ; Brain/physiology/diagnostic imaging ; Male ; Adult ; Female ; Support Vector Machine ; }, abstract = {Recently, significant attention has been drawn to the ability of network-based features to classify EEG signals reflecting varying levels of mental workload. Such features are based on methods of functional connectivity (FC), which quantify the statistical relationship between EEG electrode potentials. Here, we compare three FC-based feature extraction methods for the classification of mental workload from the Multi-Attribute Task Battery. The approaches used are weighted phase lag index (WPLI), imaginary coherence (IC), and layer entanglement (LE). WPLI and IC are popular methods for FC analysis. LE is a new approach which was introduced in recent literature. When classifying between three levels of workload, a support vector machine classifier achieved an 88% average (person-dependent) accuracy using all FC methods together, 89% using only the LE method, 67% with the IC method, and 61% with the WPLI method. When classifying between two levels of workload, these scores improve to 97%, 97%, 86%, and 81%, respectively. These results support and extend the findings of prior work and suggest that LE-based methods may enable accurate mental workload prediction which is suitable for passive brain-computer interfaces.}, }
@article {pmid40039452, year = {2024}, author = {N, GR and Guha, D and Mahadevappa, M}, title = {EEG Artifact Removal using Stacked Multi-Head Attention Transformer Architecture.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10782044}, pmid = {40039452}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Head/physiology ; }, abstract = {This study presents a transformer attention model with stacked multi-head attention layer designed to remove noise from electroencephalogram (EEG) signals, specifically addressing the problem of signal distortion caused by artifacts such as ocular and muscular noise. This is a crucial step in improving the efficacy of EEG, for disease diagnostics and BCI applications. Deep learning (DL) models have been increasingly employed for denoising EEG data in recent years, demonstrating comparable performance to classical approaches. However, the current models have been unsuccessful in capturing temporal long-term dependencies to efficiently eliminating ocular and muscular abnormalities. In this study, we address those challenges faced in the DL models by introducing multiple multi-head attention layers in the transformer model, which surpass the performance measures of previous works in EEGdenoiseNet dataset.}, }
@article {pmid40039435, year = {2024}, author = {Chen, J and Xia, Y and Thomas, A and Carlson, T and Zhao, H}, title = {Mental Fatigue Classification with High-Density Diffuse Optical Tomography: A Feasibility Study.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782566}, pmid = {40039435}, issn = {2694-0604}, mesh = {Humans ; *Tomography, Optical/methods ; *Feasibility Studies ; *Mental Fatigue/physiopathology/diagnostic imaging ; *Support Vector Machine ; Male ; Adult ; Female ; Spectroscopy, Near-Infrared/methods ; Young Adult ; }, abstract = {High-Density Diffuse Optical Tomography (HD-DOT) presents as a promising tool for not only clinical use but also daily monitoring of mental states. This study employed wearable HD-DOT to evaluate mental fatigue, specifically examining the differences in functional near-infrared spectroscopy (fNIRS) data between states of low and high fatigue among healthy participants for data collection. Data processing involved filtering, channel selection, and dimensionality reduction through Uniform Manifold Approximation (UMAP) and Projection, followed by classification using Support Vector Machines (SVM). We developed two models to assess the accuracy and generalizability of our findings: one based on individually tailored models and another employing a leave-one-participant-out cross-validation strategy. We evaluated different kernel functions, resulting in various accuracy, F1 score, and Area Under the Curve (AUC) metrics. The study achieved an average accuracy of approximately 90% for participant-specific classifiers, underscoring the effectiveness of our approach to differentiate between low and high states of mental fatigue. Our analyses led to a robust model demonstrating high classification accuracy, proving its suitability and potential for real-time Brain-Computer Interface (BCI) applications.}, }
@article {pmid40039431, year = {2024}, author = {Ziegelman, L and Hernandez, ME}, title = {Application of a Neural ODE to Classify Motion Control Strategy using EEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782326}, pmid = {40039431}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; Brain-Computer Interfaces ; Wrist/physiology ; Young Adult ; }, abstract = {Speed-accuracy trade offs exist in a variety of functional tasks, which may require differences in control strategies in future neuroprosthetic devices. It is the goal of this work to evaluate the predictability of different motor control strategies during wrist rotation tasks. Participants were asked to perform a series of discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort and motion type. Competing neural ordinary differential equation (NODE) and random forest (RF) models were evaluated to explore the feasibility of classifying control strategy using cortical data alone. In comparison to traditional ML techniques, such as RF models, the NODE model provided achieved comparable classification accuracy at a fraction of the time. Furthermore, the use of a single motor cluster or two frontal clusters provided similar accuracy to the full data from 4 clusters, which may due to increased information from these cortical areas. This study provided a promising initial demonstration of the benefits of NODE models for future brain-computer-interface applications that require near real-time classification.}, }
@article {pmid40039415, year = {2024}, author = {Flores, C and Casas, P and de Carvalho, SN and Attux, R}, title = {Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782739}, pmid = {40039415}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Machine Learning ; Calibration ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; }, abstract = {The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject.}, }
@article {pmid40039404, year = {2024}, author = {Rabbito, R and Cinanni, A and Bussi, L and Guiot, C and Roatta, S}, title = {A neuro-feedback prototype based on transcranial Doppler ultrasound for brain computer interface applications.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782446}, pmid = {40039404}, issn = {2694-0604}, mesh = {Humans ; *Ultrasonography, Doppler, Transcranial/methods ; *Brain-Computer Interfaces ; Adult ; Male ; Neurofeedback/methods ; Female ; Middle Cerebral Artery/diagnostic imaging/physiology ; }, abstract = {This study proposes a TCD-based neurofeedback system designed to visualize interhemispheric hemodynamic imbalance based on the bilateral monitoring of middle cerebral arteries (MCAs). The difference between cerebral blood velocities collected from the right and left side is calculated in real time and used to drive the horizontal position of the ball displayed on a screen. With this visual feedback, the user may see how different thoughts impact on the position of the ball and possibly acquire and improve control of the ball through progressive training. Four healthy volunteers participated in a preliminary assessment conducted over four training sessions, on average demonstrating increased control over the ball movement. The results provide a proof of concept of the methodology, confirm the feasibility of the approach. The system's novelty lies in its simplicity, cost-effectiveness, and focus on cerebral lateralization, which make TCD an intriguing alternative to other neurofeedback systems, typically based on EEG, fMRI or fNIRS. The results encourage larger sample size, investigations on the TCD-based neurofeedback's therapeutic and rehabilitative potential.}, }
@article {pmid40039400, year = {2024}, author = {Kondo, S and Tanaka, H}, title = {High-Frequency SSVEP-BCI Stimulation Frequency Optimization Based on BCI accuracy.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782291}, pmid = {40039400}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Electroencephalography/methods ; Male ; Photic Stimulation ; }, abstract = {This study investigates optimization of the stimulation frequency of blinking stimuli used for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) for individuals. Heare, we set the target BCI accuracy to 90%, and we propose and evaluate an efficient algorithm to search for stimulation frequencies that satisfy the accuracy target for each subject. The results of a four-input SSVEP-BCI operation experiment with various stimulation frequencies indicate that the experimental system obtained optimal stimulation frequency for the subject based on BCI accuracy. However, we found that the optimization time was greater for subjects who are not proficient at BCI operations, which caused subject fatigue.}, }
@article {pmid40039379, year = {2024}, author = {Lim, J and Wang, PT and Joon Sohn, W and Serrano-Amenos, C and Ibrahim, M and Lin, D and Thaploo, S and Shaw, SJ and Armacost, M and Gong, H and Lee, B and Lee, D and Andersen, RA and Heydari, P and Liu, CY and Nenadic, Z and Do, AH}, title = {Early feasibility of an embedded bi-directional brain-computer interface for ambulation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782271}, pmid = {40039379}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Walking ; Feasibility Studies ; Spinal Cord Injuries/rehabilitation/physiopathology ; Robotics/instrumentation/methods ; Exoskeleton Device ; Gait/physiology ; }, abstract = {Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research set-tings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80±0.08 between cues and decoded states over 5 runs.}, }
@article {pmid40039323, year = {2024}, author = {Kasprzak, H and Niewinska, N and Komendzinski, T and Otake-Matsuura, M and Rutkowski, TM}, title = {Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782826}, pmid = {40039323}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Smell/physiology ; Female ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents can be assigned to specific commands to allow users to interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting and interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) and electrobulbography (EBG) to improve classification accuracy. Our pilot study shows promising results for a new olfactory brain-computer interface (BCI) modality that combines common spatial pattern (CSP) filtration applied to EEG and EBG to classify responses to six scent stimuli in a classical oddball paradigm.}, }
@article {pmid40039276, year = {2024}, author = {Lim, RY and Jiang, M and Ang, KK and Lin, X and Guan, C}, title = {Brain-Computer-Brain system for individualized transcranial alternating current stimulation with concurrent EEG recording: a healthy subject pilot study.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782251}, pmid = {40039276}, issn = {2694-0604}, mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Pilot Projects ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Motor Cortex/physiology ; Young Adult ; Healthy Volunteers ; }, abstract = {In this study, we introduce a novel brain-computer-brain (BCB) system to investigate the aftereffects of individualized, task-dependent transcranial alternating current stimulation (tACS) delivered to the motor cortex. While previous studies utilized either a generic stimulation frequency or matched it to an individual's resting frequency (e.g. individual alpha frequency, iAF), our study employed a trial-by-trial tACS stimulation design wherein the stimulation frequency delivered matches the individual's peak motor imagery (MI) performance frequency. 14 healthy subjects participated in both tACS and tACS-sham on separate days in a within-subject, randomized controlled design. We found that active tACS delivered to subjects receiving alpha (α)-tACS resulted in a decline in MI performance while that with tACS-sham did not differ significantly from baseline. However, subjects receiving beta (β)-tACS showed no significant difference in effect for both active tACS and tACS-sham conditions. These findings indirectly corroborated with that from literature advocating the notion of α tACS as functionally inhibitory; hence the consequential deterioration of MI performance observed only in α-tACS subjects. A more conclusive analysis will be conducted once more data is collected from this ongoing study.Clinical Relevance: The results gathered suggest the differential functional significance of α- and β-tACS in an individualized MI task-specific tACS delivery to the motor cortex with concurrent EEG recording. Although insignificant at the point of data analysis where sample size is small in this ongoing study, tACS-sham (30 Hz) seemed to potentially modulate neural oscillations in the direction of improving MI performance. These findings can inform future tACS study designs based on a system with personalized stimulation delivery for MI task investigations within laboratory and clinical settings - potentially beneficial towards upper limb stroke rehabilitation.}, }
@article {pmid40039273, year = {2024}, author = {Jiang, H and Xiao, X and Mei, J and Xu, M and Wang, K and Ming, D}, title = {A Novel Real-time Algorithm Based on Phase-Locked Data Alignment for Continuously Controlled SSVEP-BCI.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782824}, pmid = {40039273}, issn = {2694-0604}, mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Adult ; Male ; Female ; }, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) show good performance. However, algorithms always decode segments of electroencephalogram (EEG) and can only satisfy discrete output instructions, which limit the real-time continuous control of the BCI system. This article proposes a novel algorithm for SSVEP-BCI that can translate continuous EEG into control commands, achieving real-time monitoring of user intentions.<br><br>METHODS: A phase synchronicity maximum strategy has been employed in this algorithm, which could capture a fixed-duration SSVEP epoch near any given moment, ensuring each trial is aligned with the phase of the potential corresponding template. Then, the algorithm utilized an update strategy of a small-step sliding window to recognize and output commands in approximately real-time.<br><br>RESULTS: We constructed an SSVEP-BCI system with continuous stimulation and recruited nine subjects. The results showed that the algorithm proposed in this study efficiently decoded continuously evoked SSVEP signals. The BCI's online average accuracy and ITR were 92.03% and 143.38 bits/min, respectively.<br><br>SIGNIFICANCE: The proposed algorithm can decode SSVEP at any time theoretically, which improves command output density as well as maintains high recognition accuracy. This study provides novel methods for real-time control of external devices using SSVEP-BCIs and helps to develop BCIs that are more compatible with human control habits.}, }
@article {pmid40039271, year = {2024}, author = {Umezawa, K and Isezaki, T and Okitsu, K and Yokoyama, O and Suzuki, M and Nishimura, Y}, title = {Refined Force Estimation in Monkey's Pinching Tasks Through Integrated EMG and ECoG Data: A Kalman Filter Method.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782574}, pmid = {40039271}, issn = {2694-0604}, mesh = {*Electromyography/methods ; Animals ; *Brain-Computer Interfaces ; Electrocorticography/methods ; Algorithms ; Macaca mulatta ; }, abstract = {In the development of brain-computer interfaces (BCIs), precise decoding of motor outputs is crucial. This study presents an enhanced Kalman filter approach that integrates electromyography (EMG) with electrocorticography (ECoG) to improve force estimation in pinching tasks. By incorporating EMG data as a state variable in the filter, we aim to account for musculoskeletal dynamics, enhancing the accuracy of force predictions. This integration significantly improves the decoding performance, particularly during dynamic force phases. The results confirm the importance of embedding musculoskeletal dynamics into ECoG-based BCIs, which may help improve prosthetic control and motor rehabilitation for people with motor impairments.}, }
@article {pmid40039264, year = {2024}, author = {Li, M and Pun, SH and Chen, F}, title = {Cross-paradigm data alignment to improve the calibration of asynchronous BCI systems in EEG-based speech imagery.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781999}, pmid = {40039264}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Calibration ; Speech/physiology ; Algorithms ; }, abstract = {The brain-computer interfaces (BCIs) based on speech imagery with asynchronous (self-paced) paradigms enable users to directly access and manipulate BCIs with more freedom. Compared with the indirect BCIs with traditional synchronous (cue-based) paradigms, the calibration time of asynchronous paradigms was much longer and with the unbalanced number of task states and idle states. This work aimed to improve the calibration of asynchronous BCI systems by applying a data alignment (DA) approach on cue-based and self-paced paradigms. The cue-based paradigm was regarded as the calibration paradigm and the self-paced paradigm was the testing paradigm. The data alignment approach based on the parallel transport mapped their features on the same tangent space. The logistic regression was used as the classifier to classify task states and idle states. The average result with DA was 7.52% higher than that without DA (baseline), which were 78.45% and 70.92%, respectively. Specially, the best classification accuracy was for 91.82% with DA, and the largest improvement in accuracy was 22.92%. These results suggest that it is practical to use a synchronous paradigm as calibration paradigm in asynchronous BCI systems and the data alignment approach has positive impacts on the classification of task states and idle states.}, }
@article {pmid40039208, year = {2024}, author = {Geng, Y and Yang, B and Ke, S and Chang, L and Zhang, J and Zheng, Y}, title = {Motor Imagery Decoding from EEG under Visual Distraction via Feature Map Attention EEGNet.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781898}, pmid = {40039208}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Attention/physiology ; Imagination/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; }, abstract = {The investigation of motor imagery (MI)-based brain-computer interface (BCI) is vital to the domains of human-computer interaction and rehabilitation. Few existing studies on electroencephalogram(EEG) signals decoding based on MI consider any distractions. However, it is difficult for users to do a single MI task in real life, which is especially affected by visual distraction. In this paper, we aim to investigate the effects of visual distraction on MI decoding performance. We first design a noval MI paradigm under visual distraction and observe distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) in MI under visual distraction. Then, we propose a robust decoding method of MI under visual distraction from EEG signals by using the feature map attention EEGNet (named FMA-EEGNet) and use EEG data under conditions without and with distraction to compare the decoding performance of five methods (including the proposed method and other methods). The results demonstrate that FMA-EEGNet achieved mean accuracy of 89.1% and 82.2% without and with visual distraction, respectively, indicating superior performance compared to other methods while exhibiting minimal degradation in performance. This work contributes significantly to the advancement of practical applications in MI-BCI technology.}, }
@article {pmid40039201, year = {2024}, author = {Li, Q and Zhang, Z and Shi, M and Tao, X}, title = {Multi-channel Neural Signal Recording System for an Implantable Brain-Computer Interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782545}, pmid = {40039201}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Electroencephalography/instrumentation ; Equipment Design ; Brain/physiology ; }, abstract = {Simultaneous recordings of neural activity at massive scope, in the long term, and under bio-safety conditions, could provide crucial information, which helps in better understanding the operation mechanism of the brain and promotes the clinical application evolution for the brain-computer interface. For this purpose, a multi-channel neural signal recording system is presented, which can record up to 2048-channel neural signals by multiple connections of a customized collection system. The system consists of a sensor array module, a central controller module, and an upper computer module. Using the modular design method, the sensor array module can be contrived by changing the number of channels. The single-channel data acquisition module has a sampling resolution of 16 bits, and a sampling rate of 30 KSamples/s. The central controller module can establish a connection between the sensor array module and the upper computer module, and control their operations. The upper computer module can display the data results. The system verifies the performance of the multi-channel data acquisition through the analog neural signal.}, }
@article {pmid40039185, year = {2024}, author = {Raghavan, V and Patel, P and He, X and Mesgarani, N}, title = {Decoding auditory attention for real-time BCI control.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10782575}, pmid = {40039185}, issn = {2694-0604}, support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Attention/physiology ; Acoustic Stimulation ; Auditory Perception/physiology ; Male ; }, abstract = {Brain-Computer Interfaces (BCI) facilitate interaction with devices, enhancing the quality of life for individuals with disabilities and offering a more direct method for controlling smart devices. Auditory BCIs commonly utilize event-related potentials (ERPs) necessitating a sequential presentation of choices through auditory stimuli. However, such methods impose constraints on the achievable Information Transfer Rate (ITR) compared to visual BCIs due to extended stimulus presentation times. Here, we introduce an auditory BCI approach in which the selective representation of attended speech in a listener's brain enables the decoding of one target sound source from the background. The simultaneous delivery of options in our proposed method reduces presentation durations by 2.5x compared to previous auditory BCI paradigms. This approach yields an average ITR exceeding 17 bits/min, with the best subject surpassing 33 bits/min. By outdoing current state-of-the-art auditory BCI paradigms, our research represents a significant advancement in the development of practical auditory BCI technologies.}, }
@article {pmid40039137, year = {2024}, author = {Cinquetti, E and Siviero, I and Babiloni, F and Menegaz, G and Storti, SF}, title = {Passive BCI Towards Health and Safety in Industry: Forecasting Human Vigilance 5.5 s Ahead.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782689}, pmid = {40039137}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Industry ; Forecasting ; Deep Learning ; }, abstract = {Brain-computer interfaces based on electroencephalography (EEG) recordings are gaining increasing interest in the industrial domain, aiming to enhance health, safety and performance by optimizing the cognitive load of industrial operators and facilitating human-robot interactions. This study introduces a novel experimental protocol and analysis pipeline for predicting vigilance degradation during repetitive tasks. A dataset was recorded from 10 volunteers who observed a robotic arm executing three distinct movements. The EEG power spectrum was analyzed over time using the continuous wavelet transform. Upon verifying the increased amplitude of EEG oscillations in the 8-12 Hz frequency band, we forecast its behaviour, comparing the vector autoregressive model with two deep learning recurrent architectures. The proposed encoder-decoder gated recurrent unit model obtained accurate forecasts (mean absolute error = 0.048, R[2] = 0.726) up to 5.5 s into the future. The findings suggested the feasibility of vigilance monitoring in the Industry 5.0 framework, proposing a strategy to prevent human accidents and performance decline during monotonous activities.}, }
@article {pmid40039126, year = {2024}, author = {Amrani, H and Micucci, D and Nalin, M and Napoletano, P and Rizzi, I}, title = {EEG Acquisition and Motor Imagery Classification for Robotic Control.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782723}, pmid = {40039126}, issn = {2694-0604}, mesh = {*Electroencephalography/methods ; Humans ; *Robotics/instrumentation ; *Brain-Computer Interfaces ; Support Vector Machine ; Movement/physiology ; Imagination/physiology ; }, abstract = {The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating the effectiveness of using a minimally invasive electroencephalography (EEG) device combined with machine learning techniques to control fundamental movements in a robotic setting, and 2) demonstrating these findings practically through the construction of a robotic vehicle. In this vehicle, tasks involving motor imagery align directly with control commands for the vehicle. To validate our approach, we identified four-class and two-class classification tasks. The signals have been acquired from a portable EEG device equipped with eight dry electrodes. We employed sliding window strategies to segment the data, along with feature extraction using the Common Spatial Pattern (CSP) method. Classification modules were implemented based on Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) models. The experimentation involved five participants, each with their own personalized model. While the accuracy of results in the four-class tasks is not notably high, the outcomes in binary classification tasks are promising, boasting an average accuracy of approximately 61%. Results suggest a promising potential for this approach in the realm of robot control, particularly when employing dry-electrode EEG devices.}, }
@article {pmid40039119, year = {2024}, author = {Rajpura, P and Meena, YK}, title = {Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10781846}, pmid = {40039119}, issn = {2694-0604}, mesh = {*Electroencephalography ; Validation Studies as Topic ; *Brain-Computer Interfaces ; Models, Neurological ; Humans ; }, abstract = {Decoding Electoencephalography (EEG) during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning methods advance, the challenge lies in balancing model complexity with the need for human interpretability and trust. Despite strides in EEG-based BCIs, challenges like artefacts and low signal-to-noise ratio emphasise the ongoing importance of model transparency. This work proposes using post-hoc explanations to interpret model outcomes and validate them against domain knowledge. Leveraging the GradCAM post-hoc explanation technique on the EEG motor movement/imagery dataset, this work demonstrates that relying solely on accuracy metrics may be inadequate to ensure BCI performance and acceptability. A model trained using all EEG channels of the dataset achieves 72.60% accuracy, while a model trained with motor-imagery/movement-relevant channel data has a statistically insignificant decrease of 1.75%. However, the relevant features for both are very different based on neurophysiological facts. This work demonstrates that integrating domain-specific knowledge with Explainable AI (XAI) techniques emerges as a promising paradigm for validating the neurophysiological basis of model outcomes in BCIs. Our results reveal the significance of neurophysiological validation in evaluating BCI performance, highlighting the potential risks of exclusively relying on performance metrics when selecting models for dependable and transparent BCIs.}, }
@article {pmid40039013, year = {2024}, author = {Cetera, A and Rabiee, A and Ghafoori, S and Shahriari, Y and Abiri, R}, title = {Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782523}, pmid = {40039013}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand Strength/physiology ; Support Vector Machine ; Male ; Adult ; }, abstract = {There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively.}, }
@article {pmid40038999, year = {2024}, author = {Farabbi, A and Mainardi, L}, title = {Advancing Brain-Computer Interface Systems: Asynchronous Classification of Error Potentials.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782785}, pmid = {40038999}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Discriminant Analysis ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {This paper explores the paradigm shift in the classification of Error-Related Potentials (ErrP) in Brain-Computer Interfaces (BCIs) by introducing an asynchronous approach. Traditional synchronous methods, relying on precise temporal alignment between stimuli presentation and neural responses, face challenges in real-world scenarios with human response variability.The proposed asynchronous classification liberates BCI systems from strict temporal constraints, allowing for a more natural interaction paradigm. The study introduces an innovative ensemble method comprising Linear Discriminant Analysis (LDA) and EEGNet for asynchronous ErrP classification.The method is evaluated on EEG data from the BNCI Horizon 2020 dataset, demonstrating high balanced accuracy. While the introduction of EEGNet refines the classification, reducing false positives, challenges persist in achieving a balanced trade-off between precision and recall.The findings suggest the ensemble method's potential for practical applications, emphasizing the need for further refinement and exploration of advanced techniques in asynchronous ErrP classification.}, }
@article {pmid40038985, year = {2024}, author = {Lee, KY and Chang, KY and Hsu, HC and Tseng, YT and Wei, CS and Lin, SS and Chuang, CH}, title = {Utilizing Motor-Imagery Brain-Computer Interfaces for the Assessment of Developmental Coordination Disorder in Children.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781534}, pmid = {40038985}, issn = {2694-0604}, mesh = {Humans ; *Motor Skills Disorders/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Child ; *Electroencephalography/methods ; Male ; Female ; Imagination/physiology ; }, abstract = {Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by significant motor difficulties that affect daily life. Current assessment methods primarily focus on behavioral analysis, lacking in neuroscientific metrics for a comprehensive evaluation. This study introduced an electroencephalography-based motor imagery brain-computer interface classification system for evaluating children with DCD. A key of this system was the implementation of entropy-based data screening, which markedly enhanced classification performance. Notably, using mu band power in a support vector machine achieved an accuracy rate of 79.0%. These findings pave the way for developing a tool that could assist professionals in identifying children potentially affected by DCD.}, }
@article {pmid40038962, year = {2024}, author = {Sreekantham, S and Chetty, N and Weber, DJ}, title = {Detecting and Eliminating Cardiac Artifact from Endovascular EEG Signals.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782938}, pmid = {40038962}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Heart/physiology/physiopathology ; }, abstract = {Paralysis is a debilitating condition that affects more than 5.4 million people in the U.S. In severe cases, the paralyzed patient is incapable of communication. Restoring this communication is a primary goal of caretakers and is critical to improving the patient's quality of life. Brain-computer interfaces (BCIs) that directly access signals from the motor cortex are a promising method of circumventing the condition causing paralysis, typically using machine learning (ML) to predict motor intent from brain signals. However, BCIs are highly invasive and subjects have primarily been limited to patients with mild to moderate paralysis. The Stentrode is a novel technology that records electroencephalographic (EEG) signals via an electrode array placed endovascularly in the superior sagittal sinus. The first clinical trials of this technology aim to enable digital communication for severely paralyzed patients, translating brain signals from attempted movements into computer control inputs like mouse clicks. However, recordings of EEG are often contaminated with artifacts, including biopotentials arising from other excitable tissues, such as the heart and skeletal muscle. This study characterizes the electrocardiographic (ECG) artifact detected in the Stentrode recordings and proposes an automated Independent Component Analysis (ICA) method for removing this artifact. We compare the effectiveness of this method to previous methods for removal. Quantifying and eliminating the cardiac artifact is critical to accurately decode signals from the motor cortex and restore patients' ability to communicate.}, }
@article {pmid40038951, year = {2024}, author = {Song, Z and Zhang, X and Tan, J and Wang, M and Wang, Y}, title = {Facilitating Knowledge Transfer: An Approach for Matching Neural Patterns between Motor Tasks.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781969}, pmid = {40038951}, issn = {2694-0604}, mesh = {Rats ; *Brain-Computer Interfaces ; Animals ; Algorithms ; }, abstract = {Brain-machine interface (BMI) holds great promise for restoring the impaired motor functions of individuals. In real-life scenarios, BMI users often face the challenge of quickly learning new tasks to adapt to complex environments. Consequently, it becomes essential to investigate the transferability of knowledge (neural-action mapping) of the decoder gained from previously learned tasks to new tasks. This paper introduces an approach for matching neural patterns between motor tasks to facilitate knowledge transfer, which is a key step in facilitating knowledge transfer. We project neural data into a 6D jPCA feature space and observe that neural patterns associated with the same action are preserved in the last four dimensions. By utilizing the decoder trained from the previous task, we obtain a prior estimate of the matched class. This prior estimate is further refined by clustering the neural patterns in the first two dimensions, as the data demonstrates distinct cluster shapes. To validate our approach, we conducted an experiment where a rat learned two related motor tasks sequentially. The preliminary results showed that our proposed method achieved an accuracy of 87.04% in estimating the matched class compared to the ground truth. In contrast, utilizing the decoder trained from the previous task within the entire jPCA space resulted in a significantly lower accuracy of merely 39.8%. These findings highlight the efficacy of our proposed method in matching neural patterns between motor tasks, thus facilitating knowledge transfer.}, }
@article {pmid40038942, year = {2024}, author = {Rabiee, A and Ghafoori, S and Cetera, A and Shahriari, Y and Abiri, R}, title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782674}, pmid = {40038942}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Wavelet Analysis ; *Hand Strength/physiology ; *Brain-Computer Interfaces ; Adult ; Male ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; Machine Learning ; }, abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.}, }
@article {pmid40037510, year = {2025}, author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, FV and Rieth, L and Andersen, RA}, title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2130 days.}, journal = {Acta biomaterialia}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.actbio.2025.02.030}, pmid = {40037510}, issn = {1878-7568}, abstract = {The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956-2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability). Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal. These findings link quantitative measurements to the microelectrodes' physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. STATEMENT OF SIGNIFICANCE: Long-term performance stability of the electrode-tissue interface is essential for clinical viability of brain computer interface (BCI) devices; currently, materials degradation is a critical component for performance loss. Across three human participants, ten micro-electrode arrays (plus one control) were implanted for 956-2130 days. Using scanning electron microscopy (SEM), we analyzed degradation of 980 electrodes, comparing two types of commonly implanted electrode tip metals: Platinum (Pt) and Sputtered Iridium Oxide Film (SIROF). We correlated observed degradation with in vivo electrode performance: recording (signal-to-noise ratio, noise, impedance) and stimulation (evoked somatosensory percepts). We hypothesize penetration of the electrode tip by biotic processes leads to erosion of the supporting silicon core, which then accelerates further tip metal damage. These data could lead to improved manufacturing processes or novel electrode designs towards the goal of a stable BCI electrical interface, spanning a multi-decade participant lifetime.}, }
@article {pmid40037493, year = {2025}, author = {Tang, G and Chen, B and Wu, M and Sun, L and Fan, R and Hou, R and Liu, W and Kang, J and Li, Y and Wang, M and Zhang, Y and Lu, N and Guo, W and Zhang, Y and Li, X and Wei, W and Yu, H and Li, T}, title = {Effectiveness of mindfulness-based cognitive therapy for treating generalized anxiety disorder and the moderating influence of abuse during childhood: A randomized controlled trial.}, journal = {Journal of affective disorders}, volume = {379}, number = {}, pages = {510-518}, doi = {10.1016/j.jad.2025.02.103}, pmid = {40037493}, issn = {1573-2517}, mesh = {Humans ; *Anxiety Disorders/therapy/psychology ; Female ; Male ; *Mindfulness/methods ; *Cognitive Behavioral Therapy/methods ; Adult ; Treatment Outcome ; Child ; Middle Aged ; *Child Abuse/psychology ; Surveys and Questionnaires ; Generalized Anxiety Disorder ; }, abstract = {BACKGROUND: Mindfulness-based cognitive therapy (MBCT) has emerged as a promising intervention for generalized anxiety disorder (GAD). This study evaluated MBCT's effectiveness for GAD and examined whether childhood maltreatment moderates its impact.<br><br>METHODS: Individuals with GAD were randomized to receive one of two 8-week interventions, either MBCT in-person or psychoeducation on-line (n&#xa0;=&#xa0;27 per group). At baseline and after 4 and 8&#xa0;weeks of intervention, both groups were assessed using the Beck Anxiety Inventory and Penn State Worry Questionnaire as well as several secondary questionnaires. Changes in the severity of anxiety and worry over time, as determined using linear mixed modeling, were compared between the two groups as a whole and among subgroups stratified according to type of maltreatment in childhood.<br><br>RESULTS: Among all participants, severity of worry decreased significantly more in the MBCT group than in the psychoeducation group, whereas severity of anxiety decreased to a similar extent in the two groups. Among individuals who had experienced emotional abuse in childhood, MBCT reduced the severity of anxiety significantly more than psychoeducation. In fact, MBCT was significantly more effective against anxiety in individuals who had experienced emotional abuse than in those who had not.<br><br>CONCLUSIONS: MBCT might be effective in alleviating worry symptoms in GAD, while its effectiveness against anxiety symptoms appears to be influenced by the history of maltreatment, particularly emotional abuse.<br><br>TRIAL REGISTRATION: ChiCTR2400087188 (Chictr.org).}, }
@article {pmid40036596, year = {2025}, author = {Chen, Q and Huang, X and Ju, Z and Lin, H and Tang, H and Guo, C and Fan, F and Zhao, X and Ma, Y and Luo, Y and Li, W and Zhong, W and Zhao, D}, title = {A Triband Metasurface Covering Visible, Midwave Infrared, and Long-Wave Infrared for Optical Security.}, journal = {Nano letters}, volume = {25}, number = {11}, pages = {4459-4466}, doi = {10.1021/acs.nanolett.5c00083}, pmid = {40036596}, issn = {1530-6992}, abstract = {The independent manipulation of light across multiple wavelength bands provides new opportunities for optical security. Although dual-band optical encryption methods in the visible (VIS) and infrared bands have been developed, achieving synchronized and synergistic optical security across the VIS, midwave infrared (MWIR), and long-wave infrared (LWIR) bands remains a significant challenge. Here, we experimentally demonstrate a triband metasurface that covers the VIS, MWIR, and LWIR bands. While VIS imaging is achieved by structural color, MWIR, and LWIR imaging are achieved by selective emissivity structures, with MWIR/LWIR emissivities in the MWIR imaging region of 0.81/0.17, and in the LWIR imaging region of 0.21/0.83. Importantly, the MWIR and LWIR information is completely hidden in the VIS band. We also validate the ability of metasurface to encode complex information and information-misleading encryption. This work introduces new approaches for enhancing optical security and holds significant potential for applications such as anticounterfeiting and thermal camouflage.}, }
@article {pmid40036537, year = {2025}, author = {Ravi, A and Wolfe, P and Tung, J and Jiang, N}, title = {Signal Characteristics, Motor Cortex Engagement, and Classification Performance of Combined Action Observation, Motor Imagery and SSMVEP (CAMS) BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1004-1013}, doi = {10.1109/TNSRE.2025.3544479}, pmid = {40036537}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Motor Cortex/physiology ; *Imagination/physiology ; Female ; Adult ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Young Adult ; Healthy Volunteers ; Movement/physiology ; Algorithms ; Reproducibility of Results ; Gait/physiology ; Psychomotor Performance/physiology ; Photic Stimulation ; }, abstract = {Motor imagery (MI)-based Brain-Computer Interfaces (BCIs) have shown promise in engaging the motor cortex for recovery. However, individual responses to MI-based BCIs are highly variable and relatively weak. Conversely, combined action observation (AO) and motor imagery (MI) paradigms have demonstrated stronger responses compared to AO or MI alone, along with enhanced cortical excitability. In this study, a novel BCI called Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS) was proposed. CAMS was designed based on gait observation and imagination. Twenty-five healthy volunteers participated in the study with CAMS serving as the intervention and SSMVEP checkerboard as the control condition. We hypothesized the CAMS intervention can induce observable increases in the negativity of the movement-related cortical potential (MRCP) associated with ankle dorsiflexion. MRCP components, including Bereitschaftspotential, were measured pre- and post-intervention. Additionally, the signal characteristics of the visual and motor responses were quantified. Finally, a two-class visual BCI classification performance was assessed. A consistent increase in negativity was observed across all MRCP components in signals over the primary motor cortex, compared to the control condition. CAMS visual BCI achieved a median accuracy of 83.8%. These findings demonstrate the ability of CAMS BCI to enhance cortical excitability in relation to movement preparation and execution. The CAMS stimulus not only evokes SSMVEP-like activity and sensorimotor rhythm but also enhances the MRCP. These findings contribute to the understanding of CAMS paradigm in enhancing cortical excitability, consistent and reliable classification performance holding promise for motor rehabilitation outcomes and future BCI design considerations.}, }
@article {pmid40036449, year = {2025}, author = {Kim, MK and Shin, HB and Cho, JH and Lee, SW}, title = {Developing Brain-Based Bare-Handed Human-Machine Interaction via On-Skin Input.}, journal = {IEEE transactions on cybernetics}, volume = {55}, number = {4}, pages = {1554-1567}, doi = {10.1109/TCYB.2025.3533088}, pmid = {40036449}, issn = {2168-2275}, mesh = {Humans ; *Touch/physiology ; Male ; Adult ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; Man-Machine Systems ; Brain-Computer Interfaces ; Deep Learning ; Gestures ; Brain/physiology ; Electroencephalography/methods ; }, abstract = {Developing natural, intuitive, and human-centric input systems for mobile human-machine interaction (HMI) poses significant challenges. Existing gaze or gesture-based interaction systems are often constrained by their dependence on continuous visual engagement, limited interaction surfaces, or cumbersome hardware. To address these challenges, we propose MetaSkin, a novel neurohaptic interface that uniquely integrates neural signals with on-skin interaction for bare-handed, eyes-free interaction by exploiting human's natural proprioceptive capabilities. To support the interface, we developed a deep learning framework that employs multiscale temporal-spectral feature representation and selective feature attention to effectively decode neural signals generated by on-skin touch and motion gestures. In experiments with 12 participants, our method achieved offline accuracies of 81.95% for touch location discrimination, 71.00% for motion type identification, and 46.08% for 10-class touch-motion classification. In pseudo-online settings, accuracies reached 99.43% for touch onset detection, and 80.34% and 67.02% for classification of touch location and motion type, respectively. Neurophysiological analyses revealed distinct neural activation patterns in the sensorimotor cortex, underscoring the efficacy of our multiscale approach in capturing rich temporal and spectral dynamics. Future work will focus on optimizing the system for diverse user populations and dynamic environments, with a long-term goal of advancing human-centered, neuroadaptive interfaces for next-generation HMI systems. This work represents a significant step toward a paradigm shift in design of brain-computer interfaces, bridging sensory and motor paradigms for building more sophisticated systems.}, }
@article {pmid40035637, year = {2025}, author = {Sun, B and Zhang, X and Zhang, X and Xu, B and Wang, Y}, title = {Data collection, enhancement, and classification of functional near-infrared spectroscopy motor execution and imagery.}, journal = {The Review of scientific instruments}, volume = {96}, number = {3}, pages = {}, doi = {10.1063/5.0236392}, pmid = {40035637}, issn = {1089-7623}, mesh = {*Spectroscopy, Near-Infrared/methods/instrumentation ; Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Electroencephalography/instrumentation/methods ; Data Collection ; Male ; Adult ; }, abstract = {Recognition and execution of motor imagery play a key role in brain-computer interface (BCI) and are prerequisites for converting thoughts into executable instructions. However, to date, data acquired through commonly used electroencephalography (EEG) methods are very sensitive to motion interference, which will affect the accuracy of the data classification. The emerging functional near-infrared spectroscopy (fNIRS) technique, while overcoming the drawbacks of EEG's susceptibility to interference and difficulty in detecting motor signals, has less publicly available data. In this paper, we designed a motor execution and imagery experiment based on a wearable fNIRS device to acquire brain signals and proposed a modified Kolmogorov-Arnold network (named SE-KAN) for recognizing fNIRS signals corresponding to the task. Due to the small number of subjects in this experiment, the Wasserstein generative adversarial network was used to enhance the data processing. For the fNIRS data recognition task, the SE-KAN method achieved 96.36 ± 2.43% single-subject accuracy and 84.72 ± 3.27% cross-subject accuracy. It is believed that the dataset and method of this paper will help the development of BCI.}, }
@article {pmid40035554, year = {2025}, author = {Khan, WU and Shen, Z and Mugo, SM and Wang, H and Zhang, Q}, title = {Implantable hydrogels as pioneering materials for next-generation brain-computer interfaces.}, journal = {Chemical Society reviews}, volume = {54}, number = {6}, pages = {2832-2880}, doi = {10.1039/d4cs01074d}, pmid = {40035554}, issn = {1460-4744}, mesh = {*Brain-Computer Interfaces ; *Hydrogels/chemistry ; Humans ; *Electrodes, Implanted ; Biocompatible Materials/chemistry ; Brain/physiology ; Animals ; }, abstract = {Use of brain-computer interfaces (BCIs) is rapidly becoming a transformative approach for diagnosing and treating various brain disorders. By facilitating direct communication between the brain and external devices, BCIs have the potential to revolutionize neural activity monitoring, targeted neuromodulation strategies, and the restoration of brain functions. However, BCI technology faces significant challenges in achieving long-term, stable, high-quality recordings and accurately modulating neural activity. Traditional implantable electrodes, primarily made from rigid materials like metal, silicon, and carbon, provide excellent conductivity but encounter serious issues such as foreign body rejection, neural signal attenuation, and micromotion with brain tissue. To address these limitations, hydrogels are emerging as promising candidates for BCIs, given their mechanical and chemical similarities to brain tissues. These hydrogels are particularly suitable for implantable neural electrodes due to their three-dimensional water-rich structures, soft elastomeric properties, biocompatibility, and enhanced electrochemical characteristics. These exceptional features make them ideal for signal recording, neural modulation, and effective therapies for neurological conditions. This review highlights the current advancements in implantable hydrogel electrodes, focusing on their unique properties for neural signal recording and neuromodulation technologies, with the ultimate aim of treating brain disorders. A comprehensive overview is provided to encourage future progress in this field. Implantable hydrogel electrodes for BCIs have enormous potential to influence the broader scientific landscape and drive groundbreaking innovations across various sectors.}, }
@article {pmid40035293, year = {2025}, author = {Schilling, KG and Grussu, F and Ianus, A and Hansen, B and Howard, AFD and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO}, title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.}, journal = {Magnetic resonance in medicine}, volume = {93}, number = {6}, pages = {2535-2560}, pmid = {40035293}, issn = {1522-2594}, support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R01EB031954/NH/NIH HHS/United States ; K01EB032898/NH/NIH HHS/United States ; R56EB031765/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 CA160620/CA/NCI NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; R01EB019980/NH/NIH HHS/United States ; R01EB031765/NH/NIH HHS/United States ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; }, mesh = {*Diffusion Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging ; Animals ; *Image Processing, Computer-Assisted/methods ; Signal-To-Noise Ratio ; Humans ; Reproducibility of Results ; }, abstract = {The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.}, }
@article {pmid40034942, year = {2025}, author = {Tang, X and Fan, D and Wang, X and Xing, Z and Yu, S and Wang, A and Yu, H}, title = {Exploring how sensory dominance modulated by modality-specific expectation: an event-related potential study.}, journal = {Frontiers in psychology}, volume = {16}, number = {}, pages = {1548100}, pmid = {40034942}, issn = {1664-1078}, abstract = {The Colavita visual dominance effect refers to the phenomenon in which tend to respond only or preferentially to visual stimuli of bimodal audiovisual stimulus. Previous evidence has indicated that sensory dominance can be modulated by top-down expectation. However, it remains unclear how expectations directed toward a single sensory modality influence Colavita visual dominance at the electrophysiology level. Using event-related potential (ERP) measurements, we investigated how modality expectation modulates sensory dominance by manipulating the different unimodal target probabilities used in previous related Colavita studies. For the behavioral results, a significantly larger visual dominance effect was found when the modality expectation was directed to the visual sensory condition (40% V:10% A). Further ERPs results revealed that the mean amplitude of P2 (200-250 ms) in the central-parietal region was larger in the visual precedence auditory response (V_A) type than in the auditory precedence visual response (A_V) type when modality expectation was directed to visual sensory stimuli (40% V:10% A). In contrast, the mean amplitude of N2 (290-330 ms) in the frontal region was larger for the V_A type than in the A_V type when modality expectation was directed to the auditory sensory stimuli (10% V:40% A). Additionally, for the A_V type N1 (150-170 ms) in the frontal region was larger in visual versus auditory expectation condition. Overall, the study tentatively suggested that increasing unimodal target probability may lead to greater top-down expectation direct to target modality stimulus, and then sensory dominance emerges in the late phase when participant response to visual stimuli of bimodal audiovisual stimulus.}, }
@article {pmid40034836, year = {2025}, author = {Alsuradi, H and Hong, J and Sarmadi, A and Volcic, R and Salam, H and Atashzar, SF and Khorrami, F and Eid, M}, title = {BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {6}, number = {}, pages = {305-311}, pmid = {40034836}, issn = {2644-1276}, abstract = {Objective: Human movement augmentation through supernumerary effectors is an emerging field of research. However, controlling these effectors remains challenging due to issues with agency, control, and synchronizing movements with natural limbs. A promising control strategy for supernumerary effectors involves utilizing electroencephalography (EEG) through motor imagery (MI) functions. In this work, we investigate whether MI activity associated with a supernumerary effector could be reliably differentiated from that of a natural one, thus addressing the concern of concurrency. Twenty subjects were recruited to participate in a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements, conducted in a virtual reality setting. Results: A lightweight deep-learning model that accounts for the temporal, spatial and spectral nature of the EEG data is proposed and called BandFocusNet, achieving an average classification accuracy of 70.9% using the leave-one-subject-out cross validation method. The trustworthiness of the model is examined through explainability analysis, and influential regions-of-interests are cross-validated through event-related-spectral-perturbation (ERSPs) analysis. Explainability results showed the importance of the right and left frontal cortical regions, and ERSPs analysis showed an increase in the delta and theta powers in these regions during the MI of the natural thumb but not during the MI of the supernumerary thumb. Conclusion: Evidence in the literature indicates that such activation is observed during the MI of natural effectors, and its absence could be interpreted as a lack of embodiment of the supernumerary thumb.}, }
@article {pmid40034215, year = {2025}, author = {Li, K and Li, M and Liu, W and Wu, Y and Li, F and Xie, J and Zhou, S and Wang, S and Guo, Y and Pan, J and Wang, X}, title = {Electroencephalographic differences between waking and sleeping periods in patients with prolonged disorders of consciousness at different levels of consciousness.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1521355}, pmid = {40034215}, issn = {1662-5161}, abstract = {OBJECTIVE: This study aimed to explore differences in sleep electroencephalogram (EEG) patterns in individuals with prolonged disorders of consciousness, utilizing polysomnography (PSG) to assist in distinguishing between the vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), thereby reducing misdiagnosis rates and enhancing the quality of medical treatment.<br><br>METHODS: A total of 40 patients with prolonged disorders of consciousness (pDOC; 27 patients in the VS/UWS and 13 in the MCS) underwent polysomnography. We analyzed differential EEG indices between VS/UWS and MCS groups and performed correlation analyses between these indices and the Coma Recovery Scale-Revised (CRS-R) scores. The diagnostic accuracy of the differential indices was evaluated using receiver operating characteristic (ROC) curves.<br><br>RESULTS: 1. The fractal dimension (Higuchi's fractal dimension (HFD)) of patients in the MCS tended to be higher than that of patients in the VS/UWS across all phases, with a significant difference only in the waking phase (p <&#x202f;0.05). The HFD in the waking phase was positively correlated with the CRS-R score and exhibited the highest diagnostic accuracy at 88.3%. The Teager-Kaiser energy operator (TKEO) also showed higher levels in patients in the MCS compared to those in the VS/UWS, significantly so in the NREM2 phase (p <&#x202f;0.05), with a positive correlation with the CRS-R score and diagnostic accuracy of 75.2%. The &#x3b4;-band power spectral density [PSD(&#x3b4;)] in the patients in the MCS was lower than that in those in the VS/UWS, significantly so in the waking phase (p <&#x202f;0.05), and it was negatively correlated with the CRS-R score, with diagnostic accuracy of 71.5%.<br><br>CONCLUSION: Polysomnography for the VS/UWS and MCS revealed significant differences, aiding in distinguishing between the two patient categories and reducing misdiagnosis rates. Notably, the HFD and PSD(&#x3b4;) showed significantly better performance during wakefulness compared to sleep, while the TKEO was more prominent in the NREM2 stage. Notably, the HFD exhibited a robust correlation with the CRS-R scores, the highest diagnostic accuracy, and immense promise in the clinical diagnosis of prolonged disorders of consciousness.}, }
@article {pmid40033447, year = {2025}, author = {Li, D and Li, R and Song, Y and Qin, W and Sun, G and Liu, Y and Bao, Y and Liu, L and Jin, L}, title = {Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {44}, pmid = {40033447}, issn = {1743-0003}, mesh = {*Brain-Computer Interfaces ; Humans ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; Randomized Controlled Trials as Topic ; Stroke/complications/physiopathology ; }, abstract = {BACKGROUND: Previous research has used the brain-computer interface (BCI) to promote upper-limb motor rehabilitation. However, the results of these studies were variable, leaving efficacy unclear.<br><br>OBJECTIVES: This review aims to evaluate the effects of BCI-based training on post-stroke upper-limb rehabilitation and identify potential factors that may affect the outcome.<br><br>DESIGN: A meta-analysis including all available randomized-controlled clinical trials (RCTs) that reported the efficacy of BCI-based training on upper-limb motor rehabilitation after stroke.<br><br>DATA SOURCES AND METHODS: We searched PubMed, Cochrane Library, and Web of Science before September 15, 2024, for relevant studies. The primary efficacy outcome was the Fugl-Meyer Assessment-Upper extremity (FMA-UE). RevMan 5.4.1 with a random effect model was used for data synthesis and analysis. Mean difference (MD) and 95% confidence interval (95%CI) were calculated.<br><br>RESULTS: Twenty-one RCTs (n&#x2009;=&#x2009;886 patients) were reviewed in the meta-analysis. Compared with control, BCI-based training exerted significant effects on FMA-UE (MD&#x2009;=&#x2009;3.69, 95%CI 2.41-4.96, P&#x2009;<&#x2009;0.00001, moderate-quality evidence), Wolf Motor Function Test (WMFT) (MD&#x2009;=&#x2009;5.00, 95%CI 2.14-7.86, P&#x2009;=&#x2009;0.0006, low-quality evidence), and Action Research Arm Test (ARAT) (MD&#x2009;=&#x2009;2.04, 95%CI 0.25-3.82, P&#x2009;=&#x2009;0.03, high-quality evidence). Additionally, BCI-based training was effective on FMA-UE for both subacute (MD&#x2009;=&#x2009;4.24, 95%CI 1.81-6.67, P&#x2009;=&#x2009;0.0006) and chronic patients (MD&#x2009;=&#x2009;2.63, 95%CI 1.50-3.76, P&#x2009;<&#x2009;0.00001). BCI combined with functional electrical stimulation (FES) (MD&#x2009;=&#x2009;4.37, 95%CI 3.09-5.65, P&#x2009;<&#x2009;0.00001), robots (MD&#x2009;=&#x2009;2.87, 95%CI 0.69-5.04, P&#x2009;=&#x2009;0.010), and visual feedback (MD&#x2009;=&#x2009;4.46, 95%CI 0.24-8.68, P&#x2009;=&#x2009;0.04) exhibited significant effects on FMA-UE. BCI combined with FES significantly improved FMA-UE for both subacute (MD&#x2009;=&#x2009;5.31, 95%CI 2.58-8.03, P&#x2009;=&#x2009;0.0001) and chronic patients (MD&#x2009;=&#x2009;3.71, 95%CI 2.44-4.98, P&#x2009;<&#x2009;0.00001), and BCI combined with robots was effective for chronic patients (MD&#x2009;=&#x2009;1.60, 95%CI 0.15-3.05, P&#x2009;=&#x2009;0.03). Better results may be achieved with daily training sessions ranging from 20 to 90&#xa0;min, conducted 2-5 sessions per week for 3-4 weeks.<br><br>CONCLUSIONS: BCI-based training may be a reliable rehabilitation program to improve upper-limb motor impairment and function.<br><br>TRIAL REGISTRATION: PROSPERO registration ID: CRD42022383390.}, }
@article {pmid40033324, year = {2025}, author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J}, title = {Correction: On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {46}, pmid = {40033324}, issn = {1743-0003}, }
@article {pmid40033273, year = {2025}, author = {Zhang, B and Liu, S and Chen, S and Liu, X and Ke, Y and Qi, S and Wei, X and Ming, D}, title = {Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression.}, journal = {BMC psychiatry}, volume = {25}, number = {1}, pages = {193}, pmid = {40033273}, issn = {1471-244X}, support = {2023YFF1203700//National Key Research and Development Program of China/ ; 81925020//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Universities ; *Students/psychology ; *Depression/diagnostic imaging/physiopathology/psychology ; Case-Control Studies ; *Magnetic Resonance Imaging/methods ; *Neuroimaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; Cerebral Cortex/diagnostic imaging/physiopathology ; *Default Mode Network/diagnostic imaging/physiopathology ; }, abstract = {BACKGROUND: Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.<br><br>METHODS: Functional networks of individuals with ScD (n&#x2009;=&#x2009;26) and healthy controls (HCs) (n&#x2009;=&#x2009;33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.<br><br>RESULTS: Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.<br><br>CONCLUSIONS: Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.}, }
@article {pmid40033004, year = {2025}, author = {Li, XY and Rao, Y and Li, GH and He, L and Wang, Y and He, W and Fang, P and Pei, C and Xi, L and Xie, H and Lu, YR}, title = {Single-nucleus RNA sequencing uncovers metabolic dysregulation in the prefrontal cortex of major depressive disorder patients.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {7418}, pmid = {40033004}, issn = {2045-2322}, support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024SSYS0016//Key Research and Development Program of Zhejiang Province/ ; }, mesh = {Humans ; *Depressive Disorder, Major/metabolism/genetics ; Male ; Female ; Adult ; *Prefrontal Cortex/metabolism ; Middle Aged ; Sequence Analysis, RNA ; Dorsolateral Prefrontal Cortex/metabolism ; Lactic Acid/metabolism/blood ; Oxidative Phosphorylation ; Single-Cell Analysis ; Case-Control Studies ; }, abstract = {Major depressive disorder (MDD) is a widespread psychiatric condition, recognized as the third leading cause of global disease burden in 2008. In the context of MDD, alterations in synaptic transmission within the prefrontal cortex (PFC) are associated with PFC hypoactivation, a key factor in cognitive function and mood regulation. Given the high energy demands of the central nervous system, these synaptic changes suggest a metabolic imbalance within the PFC of MDD patients. However, the cellular mechanisms underlying this metabolic dysregulation remain not fully elucidated. This study employs single-nucleus RNA sequencing (snRNA-seq) data to predict metabolic alterations in the dorsolateral PFC (DLPFC) of MDD patients. Our analysis revealed cell type-specific metabolic patterns, notably the disruption of oxidative phosphorylation and carbohydrate metabolism in the DLPFC of MDD patients. Gene set enrichment analysis based on human phenotype ontology predicted alterations in serum lactate levels in MDD patients, corroborated by the observed decrease in lactate levels in MDD patients compared to 47 age-matched healthy controls (HCs). This transcriptional analysis offers novel insights into the metabolic disturbances associated with MDD and the energy dynamics underlying DLPFC hypoactivation. These findings are instrumental for comprehending the pathophysiology of MDD and may guide the development of innovative therapeutic strategies.}, }
@article {pmid40032982, year = {2025}, author = {Wu, F and Chen, Y and Chen, X and Tong, D and Zhou, J and Du, Z and Yao, C and Yang, Y and Du, A and Ma, G}, title = {Nematode serine protease inhibitor SPI-I8 negatively regulates host NF-κB signalling by hijacking MKRN1-mediated polyubiquitination of RACK1.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {356}, pmid = {40032982}, issn = {2399-3642}, support = {32473050//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32202829//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32172877//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23C180006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Receptors for Activated C Kinase/metabolism/genetics ; *NF-kappa B/metabolism ; Mice ; *Ubiquitination ; *Signal Transduction ; Humans ; *Helminth Proteins/metabolism/genetics ; Colitis/parasitology/metabolism/genetics/chemically induced ; Mice, Inbred BALB C ; }, abstract = {Parasitic roundworms are remarkable for their ability to manipulate host immune systems and ameliorate inflammatory diseases. Although much is known about the nature of nematode effectors in immune modulation, little is known about the action mode of these molecules. Here, we report that a serine protease inhibitor SPI-I8 in the extracellular vesicles of blood-feeding nematodes like Ancylostoma ceylanicum, Haemonchus contortus and Nippostrongylus brasiliensis, effectively halts excessive inflammatory responses in vitro and in vivo. We demonstrate that H. contortus SPI-I8 promotes the role of a negative regulator of RACK1 and enhances the effects of RACK1 on tumor necrosis factor (TNF)-α-IκB kinases (IKKs)-nuclear factor kappa beta (NF-κB) axis in mammalian cells, by hijacking E3 ubiquitin protein ligase MKRN1-mediated polyubiquitination of RACK1. Administration of recombinant N. brasiliensis SPI-I8 effectively protects mice from dextran sulfate sodium (DSS)-induced colitis and lipopolysaccharide (LPS)-induced sepsis. Considering the structural and functional conservation of SPI-I8s among Strongylida nematodes and the conservation of interactive mediators (i.e., MKRN1 and RACK1) among mammals, our findings provide insights into the host-parasite interface where parasitic roundworms secret molecules to suppress host inflammatory responses. Harnessing these findings should underpin the exploitation of nematode's immunomodulators to relief excessive inflammation associated diseases in animals and humans.}, }
@article {pmid40032521, year = {2025}, author = {Sharma, D and Lupkin, SM and McGinty, VB}, title = {Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.0789-24.2025}, pmid = {40032521}, issn = {1529-2401}, abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decisions. While much is known about how OFC neurons represent values, far less is known about information encoded in OFC local field potentials (LFPs). LFPs are important because they can reflect subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded neural activity in the OFC of male macaques performing a two-option value-based decision task. We compared the value- and decision-coding properties of high-gamma LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. HG and MUA both represented the values of decision targets, but HG signals had value-coding features that were distinct from concurrently-measured MUA. On average HG amplitude increased monotonically with value, whereas in MUA the value encoding was net neutral on average. HG encoded a signal consistent with a comparison between target values, a signal which was negligible in MUA. In individual channels, HG could predict choice outcomes more accurately than MUA; however, when channels were combined in a population-based decoder, MUA was more accurate than HG. In summary, HG signals reveal value-coding features in OFC that could not be observed from spiking activity, including representation of value comparisons and more accurate behavioral predictions. These results have implications for the role of OFC in value-based decisions, and suggest that high-frequency LFPs may be a viable - or even preferable - target for BMIs to assist cognitive function.Significance statement High-frequency LFPs are often assumed to be a mere proxy for local spiking activity. This study finds evidence to the contrary in the OFC of monkeys making value-based decisions. With respect to decision mechanisms, the results challenge previous findings by suggesting a role for OFC in computing value comparisons, evident in a comparison signal encoded in HG but not spiking. More broadly, the results add to the growing evidence for spike/LFP dissociations in prefrontal cortex, and support the idea that HG signals are an important but overlooked resource for identifying neural computations in cognitive tasks. In addition, single-channel HG signals furnished more accurate predictions about choice behavior, supporting the potential use of HG signals in cognitive neural prosthetics.}, }
@article {pmid40031838, year = {2025}, author = {Lopez-Gordo, MA and Geirnaert, S and Bertrand, A}, title = {Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3542253}, pmid = {40031838}, issn = {1558-2531}, abstract = {OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communication via brain-computer interfaces (BCI). Recently, it has been shown that it is possible to train such AAD decoders based on stimulus reconstruction in an unsupervised setting, where no ground truth is available regarding which sound source is attended. In many practical scenarios, such ground-truth labels are absent, making it, moreover, difficult to quantify the accuracy of the decoders. In this paper, we aim to develop a completely unsupervised algorithm to estimate the accuracy of correlation-based AAD algorithms during a competing talker listening task.<br><br>METHODS: We use principles of digital communications by modeling the AAD decision system as a binary phase-shift keying channel with additive white gaussian noise.<br><br>RESULTS: We show that the proposed unsupervised performance estimation technique can accurately determine the AAD accuracy in a transparent-for-the-user way, for different amounts of training and estimation data and decision window lengths. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the minimal amount of training required for any given target accuracy.<br><br>CONCLUSION: Our proposed estimation technique accurately predicts the performance of a correlation-based AAD algorithm without access to ground-truth labels.<br><br>SIGNIFICANCE: In neuro-steered hearing aids, the accuracy estimates provided by our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback. In BCIs, it could support a robust communication paradigm with accuracy feedback for caregivers.}, }
@article {pmid40031638, year = {2025}, author = {Ma, Z and Yang, X and Meng, J and Wang, K and Xu, M and Ming, D}, title = {Decoding Arm Movement Direction Using Ultra-High-Density EEG.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3545856}, pmid = {40031638}, issn = {2168-2208}, abstract = {Detecting arm movement direction is significant for individuals with upper-limb motor disabilities to restore independent self-care abilities. It involves accurately decoding the fine movement patterns of the arm, which has become feasible using invasive brain-computer interfaces (BCIs). However, it is still a significant challenge for traditional electroencephalography (EEG) based BCIs to decode multi-directional arm movements effectively. This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in different directions. To extract discriminative features from UHD EEG, we proposed a spatial filtering method combining principal component analysis (PCA) and discriminative spatial pattern (DSP). We collected EEG signals from five healthy subjects (two left-handed and three right-handed) to verify the system's feasibility. The movement-related cortical potentials (MRCPs) showed a certain degree of separability both in waveforms and spatial patterns for arm movements in different directions. This study achieved an average classification accuracy of 63.15 (8.71)% for both arms (eight-class task) with a peak accuracy of 77.24%. For the dominant arm (four-class task), we obtained an average accuracy of 75.31 (9.21)% with a peak accuracy of 85.00%. For the first time, this study simultaneously decodes multi-directional movements of both arms using UHD EEG. This study provides a promising approach for detecting information about arm movement directions, which is significant for the development of BCIs.}, }
@article {pmid40031623, year = {2025}, author = {Li, M and Chen, S and Zhang, X and Wang, Y}, title = {Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1014-1025}, doi = {10.1109/TNSRE.2025.3545206}, pmid = {40031623}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Rats ; Animals ; *Bayes Theorem ; *Algorithms ; *Neural Networks, Computer ; *Models, Neurological ; Normal Distribution ; Computer Simulation ; Action Potentials/physiology ; Neurons/physiology ; Reproducibility of Results ; Nerve Net/physiology ; }, abstract = {Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.}, }
@article {pmid40031582, year = {2025}, author = {Berg, GLWV and Rohr, V and Platt, D and Blankertz, B}, title = {A New Canonical Log-Euclidean Kernel for Symmetric Positive Definite Matrices for EEG Analysis (Oct 2024).}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {3}, pages = {1000-1007}, doi = {10.1109/TBME.2024.3483936}, pmid = {40031582}, issn = {1558-2531}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Brain-Computer Interfaces ; Brain/physiology/diagnostic imaging ; }, abstract = {OBJECTIVE: Working with the Riemannian manifold of symmetric positive-definite (SPD) matrices has become popular in electroencephalography (EEG) analysis. Frequently selected for its speed property is the manifold geometry provided by the log-euclidean Riemannian metric. However, the kernels used in the log-euclidean framework are not canonically based on the underlying geometry. Therefore, we introduce a new canonical log-euclidean (CLE) kernel.<br><br>METHODS: We used the log-euclidean metric tensor on the SPD manifold to derive the CLE kernel. We compared it with existing kernels, namely the affine-invariant, log-euclidean, and Gaussian log-euclidean kernel. For comparison, we tested the kernels on two paradigms: classification and dimensionality reduction. Each paradigm was evaluated on five open-access brain-computer interface datasets with motor-imagery tasks across multiple sessions. Performance was measured as balanced classification accuracy using a leave-one-session-out cross-validation. Dimensionality reduction performance was measured using AUClogRNX.<br><br>RESULTS: The CLE kernel itself is simple and easily turned into code, which is provided in addition to all the analytical solutions to relevant equations in the log-euclidean framework. The CLE kernel significantly outperformed existing log-euclidean kernels in classification tasks and was several times faster than the affine-invariant kernel for most datasets.<br><br>CONCLUSION: We found that adhering to the geometrical structure significantly improves the accuracy over two commonly used log-euclidean kernels while keeping the speed advantages of the log-euclidean framework.<br><br>SIGNIFICANCE: The CLE provides a good choice as a kernel in time-critical applications and fills a gap in the kernel methods of the log-euclidean framework.}, }
@article {pmid40031574, year = {2025}, author = {Zhang, H and Xie, J and Zhao, C and Jin, Z and Du, F and Chen, Y and Xu, G and Tao, Q and Li, M}, title = {A Novel Spatial Auditory Brain-Computer Interface based on Low-Frequency Periodic Auditory Motion Stimulation Paradigm.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2025.3544646}, pmid = {40031574}, issn = {1558-2531}, abstract = {UNLABELLED: This study aims to improve the performance of auditory brain-computer interfaces (BCIs) by developing two-target and three-target paradigms based on steady-state motion auditory evoked potential (SSMAEP) using low-frequency stimuli in a spatial audio environment. SSMAEP is elicited by auditory stimuli exhibited by periodic and discrete changes in auditory spatial position.<br><br>METHODS: We designed a periodic auditory motion stimulation paradigm to evoke SSMAEP. Two-target and three-target SSMAEP-BCIs were developed. For the two-target SSMAEP-BCI, two periodic auditory motion stimuli with different motion frequencies were located on the left (2 Hz) and right (1.6 Hz) sides of the head, respectively. For the three-target SSMAEP-BCI, three periodic auditory motion stimuli with different motion frequencies were located on the front (2 Hz), left (2.4 Hz) and right (1.6 Hz) sides of the head, respectively.<br><br>RESULTS: SSMAEP amplitudes were modulated by auditory selective attention. In the two-target BCI, the offline experiments showed a peak average information transfer rate (ITR) of 7.70 bits/min, while the online experiments had a mean accuracy of 82.83% and an ITR of 4.41 bits/min. The three-target BCI achieved a peak ITR of 12.04 bits/min offline, with an online mean accuracy of 80.45% and an ITR of 7.05 bits/min.<br><br>CONCLUSION: The study confirms the feasibility and enhanced performance of spatial low-frequency SSMAEP-BCIs.<br><br>SIGNIFICANCE: This novel approach to SSMAEP-BCI offers a promising direction for enhancing auditory BCI performance, potentially improving user experience and application in complex environments.}, }
@article {pmid40031548, year = {2025}, author = {Liu, K and Xing, X and Yang, T and Yu, Z and Xiao, B and Wang, G and Wu, W}, title = {DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3546288}, pmid = {40031548}, issn = {2168-2208}, abstract = {OBJECTIVE: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods.<br><br>METHODS: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features.<br><br>MAIN RESULTS: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods.<br><br>CONCLUSION AND SIGNIFICANCE: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://github.com/xingxin-99/DMSANet.git.}, }
@article {pmid40031523, year = {2024}, author = {Wang, J and Wang, Z and Xu, T and Zhou, T and Zhao, X and Hu, H}, title = {SS-MSDA: Streamlined Sample-level Multi-source Domain Adaptation for EEG Emotion Recognition.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781531}, pmid = {40031523}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; }, abstract = {Affective EEG-based Brain-Computer Interface (BCI) offers extensive prospects. Yet, it grapples with notable challenges in consistently achieving accurate emotion recognition among new subjects. Mitigating this matter, Multi-Source Domain Adaptation (MSDA) has been advanced. However, they exhibit performance that falls short of expectations, necessitate complex preparations and lack solid theoretical underpinnings. Concerning these insufficiencies, we propose an innovative MSDA algorithm, effectively narrowing the Wasserstein Distance between identified subdomain and the target domain, thereby theoretically constraining the upper bound of emotion classification error. Compared with baseline model on the emotional EEG dataset SEED,SS-MSDA achieved an increase in recognition accuracy ranging from 1[~]14% (average improvement of 7.2%) across subjects, demonstrating superior performance over Domain Adaption (DA) benchmarks. Moreover, it significantly reduced the preparation time by over 99.8%, along with its minimal computational costs, thus being exceptionally apt for practical applications. Finally, the algorithm extends the theory of MSDA for affective BCI. The significance of this algorithm lies in its potential to improve the recognition accuracy of existing emotion recognition algorithms on new subjects, without the need for pre-training and data pre-collection. Moreover, it provides a novel theoretical perspective on the methods for constraining the error upper bound of the classifier.}, }
@article {pmid40031514, year = {2024}, author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H}, title = {Bi-Stream Adaptation Network for Motor Imagery Decoding.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782480}, pmid = {40031514}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; }, abstract = {Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information can thereby enhance network performance by having a comprehensive understanding of MI. Besides, due to the non-stationarity of EEG, the global and local distributions of cross-session EEG from an individual vary in applications. Based on these ideas, a novel Bi-Stream Adaptation Network (BSAN) is proposed to generate multi-scale context dependencies and to bridge the cross-session discrepancies in MI classification. Specifically, a Bi-attention module is proposed to cultivate multi-scale temporal dependencies and figure out the predominant brain regions. After features extraction, a Bi-discriminator is trained to implement the task of domain adaptation both globally and locally. To validate the proposed BSAN, extensive experiments were conducted based on two public MI datasets. The results revealed that the proposed BSAN improved the performance and robustness of MI classification and outperformed several state-of-the-art methods.}, }
@article {pmid40031513, year = {2024}, author = {Li, R and Zhao, X and Wang, Z and Xu, G and Hu, H and Zhou, T and Xu, T}, title = {Narrowband-Enhanced Method for Improving Frequency Recognition in SSVEP-BCIs.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782374}, pmid = {40031513}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI) provide a non-invasive and effective means for communication and control, which fundamentally rely on the feature of frequency information. However, filter banks in conventional spatial filter classification methods do not effectively utilize narrowband information. This study proposed a narrowband-enhanced filter bank canonical correlation analysis (NE-FBCCA) to integrate narrowband signal processing with a broadband filter bank analysis. By employing adaptive signal decomposition via multivariate fast iterative filtering (MvFIF), the specific component corresponding to the stimulus frequency can be strengthened separately. To validate the efficacy of this method, we conducted a performance evaluation using public SSVEP datasets. The results demonstrate a notable enhancement of reconstructed EEG signals in the signal-to-noise ratio (SNR) of stimulus frequency responses. Furthermore, there are significant improvements observed in classification accuracy and ITRs when compared to standard canonical correlation analysis (CCA) and filter bank CCA (FBCCA) approaches. This study provides a narrowband signal processing strategy for SSVEP responses and shows its potential to improve the performance of SSVEP-based BCI systems.}, }
@article {pmid40031504, year = {2024}, author = {Kang, H and Bao, N and Liu, H and Dong, C and Lei, D and Chen, X}, title = {A Method of Cross-Subject Transfer Learning for Ultra Short Time SSVEP Classification.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-7}, doi = {10.1109/EMBC53108.2024.10782593}, pmid = {40031504}, issn = {2694-0604}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Machine Learning ; }, abstract = {The steady-state visual evoked potentials (SSVEP) based brain-computer interfaces (BCIs) require extensive training data for efficient classification, but existing algorithms struggle with ultra short time inputs (less than 0.2 seconds), limiting the feasibility of real-time systems. This paper proposes a novel method CSA-GSDANN consisting of CSA and GSDANN. GSDANN improves SSVEP feature extraction performance in ultra short time input scenarios by applying cross-subject transfer learning techniques, combining a Global Attention Mechanism (GAM) and an optimized SSVEPNet and pre-training method CSA selects the most suitable source subject based on accuracy and aligns it with the target subject to address the inter-subject variability. The proposed CSA-GSDANN method adopts a Domain Adversarial Neural Network (DANN) framework, which integrates an enhanced SSVEPNet algorithm with an attention mechanism to extract features from electroencephalogram (EEG) data within and across subjects. The extracted features undergo domain-adversarial transfer learning. The final stage involves frequency signal classification using a constrained convolutional network. The evaluation of the CSA-GSDANN method on the IMUT dataset containing 12 subjects shows significant improvements. A comparative analysis against eight mainstream deep learning and traditional algorithms demonstrates an average accuracy enhancement of 4.23% and an average Information Transfer Rate (ITR) improvement of 50.482 bits/min compared to state-of-the-art classification algorithms under short time (0.2s) EEG inputs, substantially improving SSVEP classification performance.}, }
@article {pmid40031501, year = {2024}, author = {Li, Z and Shi, K and Li, W and Mu, F and Zhang, J and Huang, R and Cheng, H}, title = {A Dynamic Evaluation-Denoising Network for Motion Artifacts Removal from Single-Channel EEG.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10782860}, pmid = {40031501}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; Motion ; }, abstract = {Brain-computer interfaces (BCIs) have gained significant attention in rehabilitation research as a critical step in investigating neural remodeling techniques. However, most existing methods usually overlook the randomness and diversity of motion artifacts, thereby lacking the desired generalization ability and denoising precision, which limits their practical application. To address these limitations, we propose a Dynamic Evaluation Denoising Network (DED-Net) that incorporates an evaluation model with cross-domain feature fusion for artifact detection and classification. Then dynamically selecting Bidirectional Long Short-Term Memory (Bi-LSTM) networks with varying parameters for artifact removal, which achieves superior performance compared to state-of-the-art methods. Our experiment on a semi-simulated dataset constructed by EEGdenoiseNET demonstrates that the performance of DED-Net is advanced over the state-of-the-art method, i.e., SDNet, in terms of the signal-to-noise rate (SNR) and correlation coefficient (CC). Using our method, SNR and CC are 6.0597 dB and 95.28%, respectively increasing by 20.48% and 3.15%. Experiments on real EEG data demonstrate the superior performance of the proposed method in reconstructing EEG signals, in terms of the intent recognition tasks, achieving a remarkable accuracy of 88.89%, outperforming other methods.}, }
@article {pmid40031496, year = {2024}, author = {Tai, P and Yang, J and Qi, S and Li, G and Fu, Y and Li, Y}, title = {Exploring the Relationship Between Imitated and Associated Mechanism on Performance of Visual Imagery Brain-Computer Interface.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782491}, pmid = {40031496}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; *Imagination/physiology ; Adult ; Female ; Young Adult ; Brain/physiology ; Visual Perception/physiology ; }, abstract = {Visual Imagery (VI) can be defined as the manipulation of visual information derived from memory rather than perception. Currently, the brain responses underlying imitation and associative VI are not clear. In this study, we explore the differences from imitation to associative VI on the brain responses based on EEG signals. In this study, eight participants were instructed to observe visual cues from three predefined images or characters (tree, computer, or sphere), and then imagine the same cues. The results indicate that there is a significant difference in power intensity among electrode channels in the occipital lobe, posterior parietal lobe, and temporal lobe during the imagination phase between imitative tasks and associative tasks, as revealed by t-tests (p < 0.05, rejecting the null hypothesis). Overall, imitation mechanisms and associative mechanisms represent the short-term memory (STM) and long-term memory (LTM) features of objects. This study addresses a crucial research gap in VI, as there is currently a scarcity of formal simultaneous comparisons in the existing literature.}, }
@article {pmid40031495, year = {2024}, author = {Liu, H and Yang, B and Guan, S and Rong, F and Guo, M and Fang, Y and Liu, B and Gao, Y and Gu, Y}, title = {MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782649}, pmid = {40031495}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Animals ; Macaca mulatta ; Algorithms ; Humans ; Signal Processing, Computer-Assisted ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.}, }
@article {pmid40031494, year = {2024}, author = {Yu, S and Liang, F and Zhang, Y and Chen, L and Dong, L and Guo, Z and Jie, J and Wang, X and Yin, M}, title = {A Three-stage Strategy Significantly Improves Hand Movement Direction Decoding of a Single Neural Unit.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781633}, pmid = {40031494}, issn = {2694-0604}, mesh = {*Hand/physiology ; *Brain-Computer Interfaces ; *Movement/physiology ; Animals ; Macaca mulatta ; Neurons/physiology ; Algorithms ; }, abstract = {Invasive brain-computer interfaces (iBCI) can record multiple neural signals with the highest temporal and spatial resolution. However, the number of available neural units decreases with the increase in implantation time, which affects the stability of the iBCI system's control. Meanwhile, most current studies utilize a population of neural units to decode a single instruction, which limits the ability to decode multi-tasks simultaneously in complex scenarios. Using a few number of neural units, possibly even a single one, to perform single-task decoding is expected to enable the simultaneous control of multitasks. Herein, a three-stage strategy is proposed to accurately decode the direction of a monkey's hand movement in a Center-out task using spiking activities from a single neural unit. First, the optimal decoding window was selected based on the time course of decoding performance. Second, a firing rate variance ratio is proposed to choose the optimal neural unit from all units. Last, hard voting is employed to classify hand movements based on a single neural unit. The results indicated that decoding with a chosen neural unit and an optimal decoding window leads to a classification accuracy of 82.0%, which is nearly equivalent to that of multi-unit decoding (84.41%). This study provides insights into controlling multiple degrees of freedom with fewer neural units in iBCI control.}, }
@article {pmid40031491, year = {2024}, author = {Ye, Y and Mu, X and Pan, T and Li, Y and Wei, L and Fan, X and Wei, L}, title = {Cross-subject EEG-based Motor Imagery Recognition for Patient's Rehabilitation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-5}, doi = {10.1109/EMBC53108.2024.10782932}, pmid = {40031491}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Motor imagery (MI), a kind of psychological representation without actual action, has garnered increasing attention in rehabilitation. However, the inherent differences between patients and healthy persons hinder rehabilitation by reducing the accuracy of cross-subject MI recognition. Although unsupervised domain adaptation (UDA) methods have mitigated individual differences, they still suffer from challenges in terms of selecting confusing source domains and accurately classifying MI samples at the boundary. To address these challenges, we propose a novel UDA framework with a causal graphical model and label similarity clustering. The causal graphical model is employed to estimate the similarity of EEG signals, enabling the causal selection to effectively avoid confusing healthy persons' data. In addition, label similarity clustering mechanism is utilized to establish a distinct boundary, thereby enhancing the classification accuracy. The experimental results demonstrate that our approach outperforms baseline 10.10% and 16.27% on BCI IV-2a&amp;2b, separately. MI is expected to aid rehabilitation through precise recognition and active support.}, }
@article {pmid40031490, year = {2024}, author = {Wang, X and Lai, YH and Chen, F}, title = {Intended Speech Classification with EEG Signals Based on a Temporal Attention Mechanism: A Study of Mandarin Vowels.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782383}, pmid = {40031490}, issn = {2694-0604}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Speech/physiology ; Male ; Female ; Adult ; Young Adult ; Signal Processing, Computer-Assisted ; Language ; Attention/physiology ; }, abstract = {Speech brain-machine interfaces (BCIs) offer an effective means for patients with voice disorders to communicate, and research on decoding electroencephalography (EEG) signals related to intended speech can help to understand the mechanisms of language production in the brain. This study classified the intended speech EEG signals of four Chinese vowels, utilizing a dataset collected from 10 participants. A proposed TA-EEGNet model was employed, incorporating a temporal attention module. The model achieved an accuracy of 49.47%, surpassing other prevalent EEG classification models. The average accuracy in the binary classification of vowels was 69.83%. The vowels /u/ and /ü/ were classified with the lowest accuracy, suggesting difficulties in classifying vowels with similar articulatory movements based on intended speech EEG signals. Furthermore, the research analyzed the classification performance using data of different brain regions. The results showed that the auditory cortex, Broca's and Wernicke's areas, prefrontal cortex, and motor cortex outperformed the sensory cortex, indicating their contributions in the intended speech process of Mandarin vowels. Results also showed left hemisphere dominance. These findings contribute to the study of the neural mechanisms underlying speech production and articulatory movements, emphasizing the potential of speech BCIs to improve communication for people with speech disorders.}, }
@article {pmid40031482, year = {2024}, author = {Zhang, Y and Yang, S and Cauwenberghs, G and Jung, TP}, title = {From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781627}, pmid = {40031482}, issn = {2694-0604}, mesh = {Humans ; *Reading ; *Electroencephalography/methods ; *Eye-Tracking Technology ; Brain-Computer Interfaces ; Language ; Comprehension/physiology ; Eye Movements/physiology ; }, abstract = {Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading. The code and data are available in github.}, }
@article {pmid40031471, year = {2024}, author = {Gui, Z and Liu, Y and Qiu, S and Zhang, Y and Dong, K and Ming, D}, title = {Electrical stimulation-based paradigm to enhance lower limb motor imagery: initial validation in stroke patients.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782372}, pmid = {40031471}, issn = {2694-0604}, mesh = {Humans ; *Lower Extremity/physiopathology ; Male ; *Stroke/physiopathology/complications ; Female ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; Electric Stimulation/methods ; Aged ; Imagination/physiology ; Motor Cortex/physiopathology ; Adult ; }, abstract = {Lower limb motor dysfunction is a prevalent complication of stroke that significantly impacts patients' quality of life. Current research indicates that motor imagery-based brain-computer interface (BCI-MI) training can assist stroke patients in enhancing motor function and reconstructing neural pathways. Nevertheless, 40% of stroke patients struggle with effective motor imagery (MI), leading to challenges in applying lower limb MI in clinical settings. Electrical stimulation (ES) has demonstrated the ability to induce muscle contractions, generating a kinesthetic illusion that effectively guides subjects in performing MI. However, the existing study lacks clarity regarding the effectiveness of the ES-MI paradigm in improving lower limb MI in stroke patients. To address this gap, we recruited seven stroke patients to participate in an experiment involving the ES-MI enhancement paradigm, aiming to validate its performance in stroke patients. The results revealed that the ES-MI paradigm augmented the activation of the motor cortex in the lower limb and reactivated dormant areas, suggesting that MI training based on the ES-MI paradigm holds promise for enhancing neural remodeling effects in stroke patients. Additionally, the paradigm enhanced the classification accuracy of SVM(+1.17%), KNN(+0.93%), RF(+7.13%), LDA(+5.29%), and EEGNet(+0.96%), indicating potential improvements in the efficiency and quality of human-robot interaction in brain-controlled lower limb rehabilitation robots.}, }
@article {pmid40031469, year = {2024}, author = {Zhou, W and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H}, title = {Enhancing Detection of SSVEP-based BCIs Using Adjacent Frequencies Fusion Method.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782332}, pmid = {40031469}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interfaces (BCIs) have emerged as transformative technologies, enabling direct communication between the human brain and external devices. Steady-state visual evoked potentials (SSVEP) have gained particular attention due to their potential in BCIs. Current decoding algorithms do not take advantage of the correlation coefficients of adjacent frequencies. We propose adjacent frequencies fusion filter bank canonical correlation analysis (AFF-FBCCA), which enhances accuracy and robustness by utilizing information from adjacent frequencies. This weighted fusion aims to capitalize on the inherent similarity between electroencephalogram (EEG) signals at closely spaced frequencies. The determination of weight coefficients, incorporating dynamic adjustments based on the time window, further contributes to the adaptability of AFF-FBCCA. The proposed method is validated using public benchmark datasets. The results show that AFF-FBCCA is always superior to standard FBCCA in terms of classification accuracy and information transfer rate (ITR) in all test time windows. This method maintains the advantage of training-free and provides a more accurate and user-friendly solution for SSVEP-based BCI.}, }
@article {pmid40031462, year = {2024}, author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {A Novel SSVEP Modulation Method Utilizing VR-Based Binocular Vision.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10781783}, pmid = {40031462}, issn = {2694-0604}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Vision, Binocular/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Virtual Reality ; Algorithms ; Male ; Adult ; Young Adult ; Female ; }, abstract = {This paper presents a novel method for modulating steady-state visual evoked potentials (SSVEP) based on binocular vision in virtual reality (VR). The method involves displaying monocular frequencies in the left and right view of VR to encode nine binocular targets using only two frequencies. We constructed a VR-BCI system and validated the effectiveness of this binocular-encoded paradigm through the task-related component analysis (TRCA) algorithm, which is a supervised approach based on individual templates. The results showed a recognition accuracy of 79.05% and an information transfer rate (ITR) of 43.38 bits/min with a data length of 2 seconds. The electroencephalography (EEG) responses of the binocular combinations exhibited unique characteristics compared to traditional SSVEP, suggesting potential for further optimization in terms of performance. This proposed method reduces the frequency requirements for encoding SSVEP-speller and highlights the potential of VR-BCI in utilizing binocular characteristics, which could contribute to the practicality and high-speed implementation of SSVEP-based brain-computer interface (BCI) systems.}, }
@article {pmid40031461, year = {2024}, author = {Li, A and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782407}, pmid = {40031461}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Machine Learning ; Imagination/physiology ; Algorithms ; }, abstract = {In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer Interface (BCI). In IS BCI, the classification accuracy for imagining specific words is relatively low due to the inherent complexity in high-level feature variations. However, the binary classification accuracy for IS/rest is significantly higher. FSFTL leverages the refined feature focusing capability of the binary IS/Rest classification task to effectively locate relevant features for the word-level task. The feature extractor in the IS/Rest model demonstrates robust decoding ability for low-level IS features in EEG signals. We applied this high-performance yet low-resolution feature extractor to a public dataset for five-word IS task. The classifier was retrained to handle an increased number of classification categories, and the feature extractor was further fine-tuned to accommodate higher-level classification tasks. Before the experiment, we aligned the data from the two datasets to maintain the versatility of the feature extractor. Our proposed FSFTL approach was compared with existing EEG models, showing a significant improvement. The FSFTL approach outperformed the backbone strategy with a 6% increase in mean accuracy across all fifteen subjects. This study highlights the commonality of features in EEG data of IS and their transferability across various datasets and tasks, which is beneficial for improving the decoding ability of word-level IS BCI.}, }
@article {pmid40031460, year = {2024}, author = {Yang, J and Kulwa, F and Liu, X and Lu, Y and Fu, Y and Li, G and Huai, Y and Zhang, X and Li, Y}, title = {CEBRA Method: Decoding Brain Activity for Advanced Brain-Computer Interface Technology.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-6}, doi = {10.1109/EMBC53108.2024.10782428}, pmid = {40031460}, issn = {2694-0604}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Brain/physiology/physiopathology ; Male ; Support Vector Machine ; Female ; Adult ; Algorithms ; Stroke/physiopathology ; Stroke Rehabilitation/methods ; }, abstract = {The emerging neurorehabilitation technology, Brain-Computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients' brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.}, }
@article {pmid40031451, year = {2024}, author = {Luo, H and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H}, title = {EEG Emotion Recognition Based on 3D-CTransNet.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2024}, number = {}, pages = {1-4}, doi = {10.1109/EMBC53108.2024.10782401}, pmid = {40031451}, issn = {2694-0604}, mesh = {*Emotions/physiology ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Deep Learning ; }, abstract = {Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structural constraints, and it is difficult to recognize the complex features in EEG signals with long-term dynamic changes. To solve this issue, a hybrid CNN-Transformer structure using 3D data input is proposed and named 3D-CTransNet in this paper, which solves the problem of performance degradation of the traditional CNN-LSTM hybrid structure in the recognition of long sequence signals. At the same time, the self attention mechanism and parallel mode introduced by Transformer improve the recognition accuracy and processing speed. In addition, the 3D data feature map based on electrode position mapping effectively retains the spatial characteristics of EEG signals, which makes CNN better combine the time domain and spatial domain. Finally, the Valence-Arousal classification training of emotion is carried out on the public dataset DEAP, and the classification accuracy is 97.04%, which is about 5% higher than that of the hybrid CNN-LSTM model.}, }
@article {pmid40031446, year = {2025}, author = {Wei, Z and Lin, Y and Chen, J and Pan, S and Gao, X}, title = {Effects of 3D Stimuli With Frequency Ranges, Patterns, and Shapes on SSVEP-BCI Performance in Virtual Reality.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {890-899}, doi = {10.1109/TNSRE.2025.3544308}, pmid = {40031446}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Young Adult ; Adult ; *Electroencephalography ; *Photic Stimulation ; Healthy Volunteers ; Algorithms ; Depth Perception/physiology ; }, abstract = {Traditional steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer stability and simplicity in evoking brain responses, but their practical utility is limited by immovable screens for visual stimuli. Virtual Reality (VR) technology provides a more natural and immersive environment to evoke SSVEP signals. However, the design methods for visual stimuli in VR environments remain to be explored, especially under the stereoscopic vision conditions. This study investigated the effects of 3D stimuli with frequency ranges, patterns, and shapes on the performance and user experiences of VR-SSVEP. There were four patterns including three-dimensional (3D) flicker, two-dimensional (2D) flicker, 3D checkerboard, and 3D quick response (QR) code with four shapes comprising cube, sphere, cylinder, and cone at low (9-15Hz), medium (18-24Hz), and high frequencies (30-36Hz). Both offline and online experiments were conducted to analyze the effects of different parameter combinations on SSVEP-BCI performance, and a questionnaire was exploited to evaluate user experiences. Compared to high frequency range, the low and medium frequency ranges had better performance and lower user experiences. 3D checkerboard and 3D QR code patterns showed significantly better user experiences than 3D and 2D flickers for all frequency ranges. With a high level of classification performance, 3D checkerboard and 3D QR code patterns in medium frequency range could synthetically enhance the system performance and user experiences. These results could provide significant value for SSVEP-BCI application in VR environments.}, }
@article {pmid40031345, year = {2025}, author = {Liu, G and Zhang, R and Tian, L and Zhou, W}, title = {Fine-Grained Spatial-Frequency-Time Framework for Motor Imagery Brain-Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3536212}, pmid = {40031345}, issn = {2168-2208}, abstract = {The Motor Imagery Brain-Computer Interfaces (MI-BCIs) have shown considerable promise for applications in neural rehabilitation. However, improving the practicality and interpretability of MI-BCIs remains a critical challenge. Unlike previous methods that focus generally on either spatial, frequency, or temporal domains with coarse-grained segmentation schemes, this study proposes a novel fine-grained spatialfrequency- time (FGSFT) framework, aiming to enhance the efficiency and reliability of MI-BCIs. Multi-channel MI EEG recordings are firstly processed through multiscale time-frequency segmentation and spatial segmentation schemes, yielding finegrained spatial-frequency-time segments (SFTSs). The key SFTSs are then selected with a tailored wrapper-based feature selection approach. Discriminative MI EEG features are extracted using a divergence-based common spatial pattern algorithm with intraclass regularization and classified using an efficient linear support vector machine (SVM). The proposed framework was evaluated on the BCI IV IIa and SDU-MI datasets, demonstrating stateof- the-art performance in terms of information transfer rate (ITR) Meanwhile, the proposed spatial segmentation strategy can significantly improve the performance of MI-BCIs when using a larger number of electrodes. Additionally, the fine-grained Motor Imagery Time-Frequency Reaction Map (MI-TFRM) and time-frequency topographical map can be obtained with the proposed framework enabling visualization of the subject-specific dynamic neural process during motor imagery tasks, facilitating the devising of personalized MI-BCIs. The FGSFT framework significantly advances the accuracy, ITR, and interoperability of MI-BCIs, paving the way for future neuroscientific research and clinical applications in neural rehabilitation and assistive technologies.}, }
@article {pmid40031268, year = {2025}, author = {Gao, X and Gui, K and Wu, X and Metcalfe, B and Zhang, D}, title = {Effects of Different Preprocessing Pipelines on Motor Imagery-Based Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3532771}, pmid = {40031268}, issn = {2168-2208}, abstract = {In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been significantly improved by a lot of cutting-edge methods. The exploration of effective preprocessing in brain-computer interfaces, particularly in terms of identifying suitable preprocessing methods and determining the optimal sequence for their application, remains an area ripe for further investigation. To address this gap, this study explores a range of preprocessing techniques, including but not limited to independent component analysis, surface Laplacian, bandpass filtering, and baseline correction, examining their potential contributions and synergies in the context of BCI applications. In this extensive research, a variety of preprocessing pipelines were rigorously tested across four EEG data sets, all of which were pertinent to motor imagery-based BCIs. These tests incorporated five EEG machine learning models, working in tandem with the preprocessing methods discussed earlier. The study's results highlighted that baseline correction and bandpass filtering consistently provided the most beneficial preprocessing effects. From the perspective of online deployment, after testing and time complexity analysis, this study recommends baseline correction, bandpass filtering and surface Laplace as more suitable for online implementation. An interesting revelation of the study was the enhanced effectiveness of the surface Laplacian algorithm when used alongside algorithms that focus on spatial information. Using appropriate processing algorithms, we can even achieve results (92.91% and 88.11%) that exceed the SOTA feature extraction methods in some cases. Such findings are instrumental in offering critical insights for the selection of effective preprocessing pipelines in EEG signal decoding. This, in turn, contributes to the advancement and refinement of brain-computer interface technologies.}, }
@article {pmid40031262, year = {2025}, author = {Chen, P and Liu, X and Ma, C and Wang, H and Yang, X and Grebogi, C and Gu, X and Gao, Z}, title = {Unsupervised Domain Adaptation With Synchronized Self-Training for Cross-Domain Motor Imagery Recognition.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2025.3525577}, pmid = {40031262}, issn = {2168-2208}, abstract = {Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.}, }
@article {pmid40031240, year = {2025}, author = {Kim, CU and Park, S and Im, CH}, title = {Performance Enhancement of an SSVEP-Based Brain-Computer Interface in Augmented Reality through Adaptive Color Adjustment of Visual Stimuli for Optimal Background Contrast.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3530421}, pmid = {40031240}, issn = {1558-0210}, abstract = {The aim of this study is to develop a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system with enhanced performance in an augmented reality (AR) environment by dynamically adjusting colors of visual stimuli to contrast with the background seen through the transparent display. Our proposed method extracts the average color value from the area surrounding the visual stimulus location. It then calculates the contrast value using the HSV color model and applies this to the stimulus color. In an offline experiment, we determined the optimal visual stimulus presentation strategy by comparing the performances of three different methods for determining the colors of visual stimuli in an AR environment. We then evaluated the feasibility of the proposed strategy through online experiments conducted in both indoor and outdoor conditions. The classification performance of the SSVEP-BCI system in an AR environment based on our proposed stimulus presentation strategy was 95.0% for a window size of 3.5 s in offline experiments performed with 17 participants. This was significantly higher than the performance of the conventional black-and-white color strategy. Additionally, it was confirmed by the online experiments that there was no large performance degradation between indoor and outdoor uses.}, }
@article {pmid40031192, year = {2025}, author = {Li, Y and Wang, Y and Lei, B and Wang, S}, title = {SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs.}, journal = {IEEE transactions on medical imaging}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TMI.2025.3532480}, pmid = {40031192}, issn = {1558-254X}, abstract = {Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To address this issue, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. To our knowledge, it is the first work that an end-to-end framework is proposed to achieve cross-modal generation from EEG to fNIRS. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.}, }
@article {pmid40031046, year = {2025}, author = {Li, R and Wang, Z and Zhao, X and Xu, G and Hu, H and Zhou, T and Xu, T}, title = {Amplitude Modulation Depth Coding Method for SSVEP-based Brain-computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3528409}, pmid = {40031046}, issn = {1558-0210}, abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of 81.7 ± 12.6% with an average information transfer rate (ITR) of 45.4 ± 11.5 bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.}, }
@article {pmid40030956, year = {2025}, author = {Wu, J and He, F and Xiao, X and Gao, R and Meng, L and Liu, X and Xu, M and Ming, D}, title = {SSVEP enhancement in mixed reality environment for brain-computer interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3526950}, pmid = {40030956}, issn = {1558-0210}, abstract = {Expanding the application possibilities of brain-computer interfaces (BCIs) is possible through their implementation in mixed reality (MR) environments. However, visual stimuli are displayed against a realistic scene in the MR environment, which degrades BCI performance. The purpose of this study was to optimize stimulus colors in order to improve the MR-BCI system's performance. In the MR environment, a 10-command SSVEP-BCI was deployed. Various stimulus colors and background colors for the BCI system were tested and optimized in offline and online experiments. Color contrast ratios (CCRs) between stimulus and background colors were introduced to assess the performance difference among all conditions. Additionally, we proposed a cross-correlation task-related component analysis based on simulated annealing (SA-xTRCA), which can increase the signal-to-noise ratio (SNR) and detection accuracy by aligning SSVEP trials. The results of an offline experiment showed that the background and stimulus colors had a significant interaction effect that can impact system performance. A possible nonlinear relationship between CCR value and SSVEP detection accuracy exists. Online experiment results demonstrated that the system performed best with polychromatic stimulus on the colored background. The proposed SA-xTRCA significantly outperformed the other four traditional algorithms. The online average information transfer rate (ITR) achieved 57.58 ± 5.31 bits/min. This study proved that system performance can be effectively enhanced by optimizing stimulus color based on background color. In MR environments, CCR can be used as a quantitative criterion for choosing stimulus colors in BCI system design.}, }
@article {pmid40030957, year = {2025}, author = {Zhang, Y and Zhang, C and Jiang, R and Qiu, S and He, H}, title = {A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3527629}, pmid = {40030957}, issn = {1558-0210}, abstract = {A brain-computer interface (BCI) based on motor imagery (MI) can translate users' subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems.}, }
@article {pmid40030935, year = {2025}, author = {Meng, J and Li, X and Li, S and Fan, X and Xu, M and Ming, D}, title = {High-Frequency Power Reflects Dual Intentions of Time and Movement for Active Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3529997}, pmid = {40030935}, issn = {1558-0210}, abstract = {Active brain-computer interface (BCI) provides a natural way for direct communications between the brain and devices. However, its detectable intention is very limited, let alone of detecting dual intentions from a single electroencephalography (EEG) feature. This study aims to develop time-based active BCI, and further investigate the feasibility of detecting time-movement dual intentions using a single EEG feature. A time-movement synchronization experiment was designed, which contained the intentions of both time (500 ms vs. 1000 ms) and movement (left vs. right). Behavioural and EEG data of 22 healthy participants were recorded and analyzed in both the before (BT) and after (AT) timing prediction training sessions. Consequently, compared to the BT sessions, AT sessions led to substantially smaller absolute deviation time behaviourally, along with larger high-frequency event-related desynchronization (ERD) in frontal-motor areas, and significantly improved decoding accuracy of time. Moreover, AT sessions achieved enhanced motor-related contralateral dominance of event-related potentials (ERP) and ERDs than the BT, which illustrated a synergistic relationship between the two intentions. The feature of 20-60 Hz power can simultaneously reflect the time and movement intentions, achieving a 73.27% averaged four-classification accuracy (500 ms-left vs. 500 ms-right vs. 1000 ms-left vs.1000 ms-right), with the highest up to 93.81%. The results initiatively verified the dual role of high-frequency (20-60 Hz) power in representing both the time and movement intentions. It not only broadens the detectable intentions of active BCI, but also enables it to read user's mind concurrently from two information dimensions.}, }
@article {pmid40030934, year = {2025}, author = {Mahmoudi, A and Khosrotabar, M and Gramann, K and Rinderknecht, S and Sharbafi, MA}, title = {Using passive BCI for personalization of assistive wearable devices: a proof-of-concept study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2025.3530154}, pmid = {40030934}, issn = {1558-0210}, abstract = {Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in δ and θ activity and decreases in α and β activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.}, }
@article {pmid40030668, year = {2025}, author = {Perna, A and Orban, G and Berdondini, L and Ribeiro, JF}, title = {Force Measurements to Advance the Design and Implantation of CMOS-Based Neural Probes.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {5}, pages = {1731-1739}, doi = {10.1109/TBME.2024.3519763}, pmid = {40030668}, issn = {1558-2531}, mesh = {*Electrodes, Implanted/adverse effects ; Equipment Design ; Microelectrodes ; Animals ; *Brain-Computer Interfaces ; Brain/physiology ; Semiconductors ; Male ; Rats ; Foreign-Body Reaction ; }, abstract = {OBJECTIVE: Tissue penetrating active neural probes provide large and densely packed microelectrode arrays for the fine-grained investigation of brain circuits and for advancing brain-machine interfaces (BMIs). To improve the electrical interfacing performances of such stiff silicon devices, which typically elicit a vigorous foreign body reaction (FBR), here we perform insertion force measurements and derive probe layout and implantation procedure optimizations.<br><br>METHODS: We performed in-vivo insertion force measurements to evaluate the impact of probe design and implantation speed on mechanically induced trauma and iatrogenic injury. Because acute damage constitutes the initial trigger of FBR, these experiments allow to characterize and minimize device invasiveness.<br><br>RESULTS: Probe sharpness outweighs cross-sectional dimensions during the dimpling stage of the implantation, when the device compresses the brain before penetration. Insertion speed does not display a major effect on dimpling magnitude. A slow speed, however, significantly increases dimpling duration.<br><br>CONCLUSION: It is crucial to use sharp devices to reduce mechanical and ischemic damage. Although slow insertion speeds typically improve the quality of acute electrophysiological recordings, we show that slow speeds should only be used upon penetration in the brain parenchyma and not during the dimpling stage. A closed-loop implantation procedure can be used to set the appropriate speed in the different insertion stages.<br><br>SIGNIFICANCE: We provide new evidence on the impact of probe layout and insertion speed on insertion force, with implications on the design and implantation procedure for minimally invasive CMOS neural probes. A novel closed-loop methodology to optimize device implantation and reduce FBR is proposed.}, }
@article {pmid40030619, year = {2024}, author = {Tantawanich, P and Phunruangsakao, C and Izumi, SI and Hayashibe, M}, title = {A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3522168}, pmid = {40030619}, issn = {1558-0210}, abstract = {Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. Growing interest exists in decoding bimanual motor tasks, crucial for activities of daily living. This stems from the need to restore motor function, especially in individuals with deficits. This review aims to summarize neurological advancements in bimanual BCIs, encompassing neuroimaging techniques, experimental paradigms, and analysis algorithms. Thirty-six articles were reviewed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search result revealed diverse experimental paradigms, protocols, and research directions, including enhancing the decoding accuracy, advancing versatile prosthesis robots, and enabling real-time applications. Notably, within BCI studies on bimanual movement coordination, a shared objective is to achieve naturalistic movement and practical applications with neurorehabilitation potential.}, }
@article {pmid40030617, year = {2024}, author = {Jiang, Y and Li, K and Liang, Y and Chen, D and Tan, M and Li, Y}, title = {Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3520984}, pmid = {40030617}, issn = {1558-0210}, abstract = {Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.}, }
@article {pmid40030607, year = {2025}, author = {Gao, Z and Xu, B and Wang, X and Zhang, W and Ping, J and Li, H and Song, A}, title = {Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {5}, pages = {1708-1719}, doi = {10.1109/TBME.2024.3519348}, pmid = {40030607}, issn = {1558-2531}, mesh = {Humans ; *Hand/physiology ; Biomechanical Phenomena/physiology ; Male ; Adult ; Female ; Movement/physiology ; Young Adult ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Hand Strength/physiology ; }, abstract = {Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics. Based on grasp taxonomy, we selected four natural movements for our study: Large Diameter (LD), Sphere 3-Finger (SF), Precision Disk (PD), and Parallel Extension (PE), each incorporating two levels of speed and force parameters. The results demonstrate that a combination of MRCPs features and MBNs metrics can successfully decode not only the types of movements and kinematic parameters but also differentiate between different grasp taxonomy characteristics, such as the number of fingers exerting force and the type of grasp. In terms of movement type, we achieved a peak four-class accuracy of 60.56%. For grasp type and number of fingers exerting force, binary classification peak accuracies reached 79.25% and 79.28%, respectively. In the case of kinematic parameters, the Precision Disk movement exhibited the highest binary classification peak accuracy at 84.65%. Moreover, our research also found the changes and patterns in brain region connectivity across both time and frequency domains. We believe that our research highlights the potential of MBNs to enhance the functionality of Brain-Computer Interface (BCI) systems for more intuitive control mechanisms and contributes valuable insights into the brain's operational mechanisms.}, }
@article {pmid40030575, year = {2025}, author = {Xu, L and Jiang, X and Wang, R and Lin, P and Yang, Y and Leng, Y and Zheng, W and Ge, S}, title = {Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {4}, pages = {2400-2412}, doi = {10.1109/JBHI.2024.3510740}, pmid = {40030575}, issn = {2168-2208}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Adult ; Algorithms ; Male ; Female ; Young Adult ; Deep Learning ; }, abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning techniques, particularly convolutional neural network (CNN) architectures, have gained prominence in EEG (e.g., SSVEP) decoding because of their nonlinear modeling capabilities and autonomy from manual feature extraction. However, most studies using CNNs employ temporal signals as the input and cannot directly mine the implicit frequency information, which may cause crucial frequency details to be lost and challenges in decoding. By contrast, the prevailing supervised recognition algorithms rely on a lengthy calibration phase to enhance algorithm performance, which could impede the popularization of SSVEP based BCIs. To address these problems, this study proposes the Time-Frequency Attention Network (TFA-Net), a novel CNN model tailored for SSVEP signal decoding without the calibration phase. Additionally, we introduce the Frequency Attention and Channel Recombination modules to enhance ability of TFA-Net to infer finer frequency-wise attention and extract features efficiently from SSVEP in the time-frequency domain. Classification results on a public dataset demonstrated that the proposed TFA-Net outperforms all the compared models, achieving an accuracy of 79.00% $\pm$ 0.27% and information transfer rate of 138.82 $\pm$ 0.78 bits/min with a 1-s data length. TFA-Net represents a novel approach to SSVEP identification as well as time-frequency signal analysis, offering a calibration-free solution that enhances the generalizability and practicality of SSVEP based BCIs.}, }
@article {pmid40030518, year = {2025}, author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X}, title = {Improving Reliability of Life Applications Using Model-Based Brain Switches via SSVEP.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {5}, pages = {1636-1644}, doi = {10.1109/TBME.2024.3516733}, pmid = {40030518}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Reproducibility of Results ; Young Adult ; *Brain/physiology ; *Models, Neurological ; Signal-To-Noise Ratio ; }, abstract = {The brain switch improves the reliability of asynchronous brain-computer interface (aBCI) systems by switching the control state of the BCI system. Traditional brain switch research focuses on extracting advanced electroencephalography (EEG) features. However, a low signal-to-noise ratio (SNR) of EEG signals resulted in limited feature information and low performance of brain switches. Here, we design a virtual physical system to build the brain switch, allowing users to trigger the system through periodic brainwave modulation, fully integrating limited feature information and improving reliability. Furthermore, we designed multiple experiments to validate the effectiveness of the proposed brain switch based on steady-state visual evoked potentials (SSVEP). The results verified the performance of SSVEP brain switches based on virtual physical systems, improving the reliability of brain switches to 0.1 FP/h or even better with acceptable triggering time and calibration-free for most subjects. This represents that the proposed virtual physical model-based brain switch can utilize SSVEP features and output the reliable commands required to control external devices, promoting BCI real applications.}, }
@article {pmid40030472, year = {2025}, author = {Ke, Y and Chen, X and Xu, W and Wang, T and Shen, S and Ming, D}, title = {High-Frequency SSVEP-BCI With Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {3}, pages = {1897-1908}, doi = {10.1109/JBHI.2024.3514794}, pmid = {40030472}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Young Adult ; *Signal Processing, Computer-Assisted ; Female ; Algorithms ; Photic Stimulation/methods ; }, abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance. In this study, we proposed a row-column dual-frequency encoding and decoding method using HF stimulation to develop a comfortable BCI system that supports multiple commands and reduces training costs. We arranged 20 targets in a matrix of five rows and four columns, with each target modulated by left-and-right field stimulation using two frequency-phase combinations. Targets in each row or column share a unique frequency-phase combination, allowing EEG data from the same row or column to be used collectively to train a row/column index decoding model for target identification. To evaluate the performance of our method, we constructed a 20-target asynchronous robotic arm control system with the adaptive window method. With only four training trials per target, the online system achieved an ITR of 105.14 ± 14.15 bits/min, a true positive rate of 98.18 ± 2.87%, a false positive rate of 7.39 ± 6.73%, and a classification accuracy of 91.88 ± 5.75%, with an average data length of 925.70 ± 45.44 ms. These results indicate that the proposed protocol can deliver accurate and rapid command outputs for a comfortable SSVEP-based BCI with minimal training data and fewer frequencies.}, }
@article {pmid40030451, year = {2024}, author = {Zhang, X and Wei, W and Qiu, S and Li, X and Wang, Y and He, H}, title = {Enhancing SSVEP-Based BCI Performance via Consensus Information Transfer Among Subjects.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2024.3506998}, pmid = {40030451}, issn = {2162-2388}, abstract = {The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has received considerable attention for its high communication speed. While large datasets provide an important opportunity to enhance decoding accuracies, the key challenge lies in the exploration of existing data to extract valuable information based on the distinctive characteristics of brain responses. In this study, we introduce ConsenNet, a framework designed to enhance SSVEP classification performance by leveraging information from the diverse perspectives of existing subjects. First, this study exploits the diversity of existing subjects to generate new samples, which retain both task-related components and variability. This effectively enhances the network generalization capability on new subjects. Second, the structured knowledge that encapsulates the interrelationships between categories has been constructed and then transferred from the teacher network to the student network, guiding the student network to extract invariant features across subjects. Finally, our model incorporates a small amount of new subject data for model calibration in the final stage. Offline experiments conducted on three public datasets demonstrate the superiority of ConsenNet over 19 methods compared in this study, while online experiments validate its feasibility for real-world applications.}, }
@article {pmid40030403, year = {2024}, author = {Tan, J and Zhang, X and Wu, S and Song, Z and Wang, Y}, title = {Hidden Brain State-based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-machine Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3503713}, pmid = {40030403}, issn = {1558-0210}, abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly impacts the learning efficiency of the RL-based decoders. Existing reward designs in the RL-based BMI framework rely on external rewards or manually labeled internal rewards, unable to accurately extract subjects' internal evaluation. In this paper, we propose a hidden brain state-based kernel inverse reinforcement learning (HBS-KIRL) method to accurately infer the subject-specific internal evaluation from neural activity during the BMI task. The state-space model is applied to project the neural state into low-dimensional hidden brain state space, which greatly reduces the exploration dimension. Then the kernel method is applied to speed up the convergence of policy, reward, and Q-value networks in reproducing kernel Hilbert space (RKHS). We tested our proposed algorithm on the data collected from the medial prefrontal cortex (mPFC) of rats when they were performing a two-lever-discrimination task. We assessed the state-value estimation performance of our proposed method and compared it with naïve IRL and PCA-based IRL. To validate that the extracted internal evaluation could contribute to the decoder training, we compared the decoding performance of decoders trained by different reward models, including manually designed reward, naïve IRL, PCA-IRL, and our proposed HBS-KIRL. The results show that the HBS-KIRL method can give a stable and accurate estimation of state-value distribution with respect to behavior. Compared with other methods, the decoder guided by HBS-KIRL achieves consistent and better decoding performance over days. This study reveals the potential of applying the IRL method to better extract subject-specific evaluation and improve the BMI decoding performance.}, }
@article {pmid40030380, year = {2025}, author = {Chen, D and Cao, C and Gong, J and Huang, J and Xiao, J and Huang, Q and Guo, Y and Li, Y}, title = {Decoding Single-Pellet Retrieval Task From Local Field Potentials in Pre- and Post-Stroke Motor Areas: Insights Into Interhemispheric Connectivity Difference.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {4}, pages = {1316-1327}, doi = {10.1109/TBME.2024.3499319}, pmid = {40030380}, issn = {1558-2531}, mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Motor Cortex/physiopathology/physiology ; *Stroke/physiopathology ; Male ; Signal Processing, Computer-Assisted ; Forelimb/physiology/physiopathology ; Electrodes, Implanted ; }, abstract = {OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) hold promise for restoring communication and movement in stroke-paralyzed individuals. Recent studies have demonstrated the potential of using local field potentials (LFPs) for decoding single-pellet retrieval (SPR) tasks in iBMIs. However, most research has relied on LFPs from healthy rats rather than those affected by stroke. This study aimed to investigate the feasibility of utilizing LFPs from both the right and left (stroke) cortical forelimb areas (CFAs) for the SPR tasks decoding under both pre- and post-stroke conditions.<br><br>METHODS: LFPs were recorded via microelectrode arrays implanted into CFAs of eight rats trained to perform the SPR tasks. The relative spectral power method was used to represent frequency information, and random forest classification differentiated SPR tasks from resting states. We also assessed interhemispheric connectivity, including correlation, coherence, and phase-amplitude coupling (PAC), to compare differences between the SPR tasks and the resting states under both pre- and post-stroke conditions.<br><br>RESULTS: Our findings indicated that the relative PS method with LFPs achieves 87.10% 9.2% accuracy in post-stoke SPR decoding, where high gamma is crucial. Additionally, we observed changes in PACs from the right to the left sensorimotor cortex post-stroke during the SPR tasks compared to the resting states.<br><br>SIGNIFICANCE: Our work provides a comprehensive insight into the role of different frequency band from LFPs in motor function recovery mechanisms, highlighting the importance of the high gamma in motor function. This research lays the foundation for developing post-stoke SPR-related BMIs.}, }
@article {pmid40030325, year = {2025}, author = {Saadatmand, H and Akbarzadeh-T, MR}, title = {Multiobjective Evolutionary Sequential Channel/ Feature Selection for EEG Motor Imagery Analysis.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {4}, pages = {2546-2556}, doi = {10.1109/JBHI.2024.3508277}, pmid = {40030325}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Fuzzy Logic ; }, abstract = {Motor imagery (MI) analysis from EEG signals constitutes a class of emerging brain-computer interface (BCI) applications that face EEG's predominant complexities arising from the multitude of channels and the vast number of possible features. This study presents a two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) for efficient EEG-based MI signal analysis. The two-step process is a non-dominant wrapper strategy that sequentially identifies the optimal channels and the minimal set of features, thereby reducing MI's combinatorial search complexity. We also employ a reptile-based search algorithm (RSA), a recent metaheuristic for efficient search in multimodal continuous domains, to optimize the classifier's hyper-parameters. The proposed MOSIFE-RSA algorithm is benchmarked against 12 representative algorithms on four standard BCI Competition databases, including IV-I, III-IVa, III-IIIa, and II. The results show that MOSIFE-RSA improves accuracy by 20%, with channel selection contributing as much as 15% and feature selection as much as 5% towards these results. Furthermore, it reduces computational complexity by 81% through channel selection and 16% through feature selection, demonstrating its effectiveness in advancing EEG-based MI signal analysis. This research has practical implications for developing more accurate and efficient brain-computer interface systems.}, }
@article {pmid40030277, year = {2025}, author = {Ding, Y and Li, Y and Sun, H and Liu, R and Tong, C and Liu, C and Zhou, X and Guan, C}, title = {EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {3}, pages = {1909-1918}, doi = {10.1109/JBHI.2024.3504604}, pmid = {40030277}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; Adult ; Male ; Young Adult ; Algorithms ; }, abstract = {Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasksâcognitive attention, driving fatigue, and mental workload detectionâconsistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks.}, }
@article {pmid40030249, year = {2024}, author = {Kokorin, K and Zehra, SR and Mu, J and Yoo, P and Grayden, DB and John, SE}, title = {Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3500217}, pmid = {40030249}, issn = {1558-0210}, abstract = {Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully-autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected α<0.05) mean task success rate (p<0.0001, μ=36.1%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (p<0.0001, μ=-26.8%, 95% CI [-36.0%, -17.7%]), and participant workload (p=0.02, μ=-11.6, 95% CI [-21.1, -2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.}, }
@article {pmid40030248, year = {2024}, author = {Ke, S and Yang, B and Qin, Y and Rong, F and Zhang, J and Zheng, Y}, title = {FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3499998}, pmid = {40030248}, issn = {1558-0210}, abstract = {Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy, real-time processing, and deployment. To overcome these challenges, we propose FACT-Net, an innovative deep-learning network for the fast and accurate decoding of MI-EEG signals. FACT-Net incorporates a Frequency Adapter (FA) module designed for processing the frequency features of MI-EEG data, as well as a Temporal-Periodicity Inception (TPI) module specifically for handling global periodic signals in MI. To evaluate the proposed model, we conduct the experiments on the cross-day dataset collected from 67 subjects and the BCIC-IV-2a dataset. The FACT-Net achieved an accuracy of 48.32% and 80.67% higher than the state-of-the-art (SOTA) approaches, demonstrating excellent performance in MI decoding. Additionally, it exhibits exceptional memory efficiency and inference time, indicating significant potential for practical applications. We anticipate that FACT-Net will set a new baseline for MI-EEG decoding. The code is available in https://github.com/Ktn1ga/EEG_FACT.}, }
@article {pmid40030247, year = {2024}, author = {Rao, Z and Zhu, J and Lu, Z and Zhang, R and Li, K and Guan, Z and Li, Y}, title = {A Wearable Brain-Computer Interface with Fewer EEG Channels for Online Motor Imagery Detection.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3502135}, pmid = {40030247}, issn = {1558-0210}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have significant potential for neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel electroencephalogram (EEG) recording devices, during which the pre-experimental preparation and post-experimental hair cleaning are time-consuming and inconvenient for stroke patients, and potentially affect their motivation for rehabilitation training. In this paper, we introduced a wearable MI-BCI system for online MI classification using a wireless headband device with four EEG channels to reduce setup time while enhancing portability. To validate the performance of the system in decoding MI-EEG signals, extensive experiments and comparisons were performed on sixty-six healthy subjects. Specifically, an offline and an online experiment with forty-six subjects were conducted, with the system achieving average offline and online accuracies of 85.21% and 76.54%, respectively. Furthermore, a comparison experiment involving another twenty subjects showed that the online performance of our headband device (77.84%) was comparable to that of a mature commercial Neuroscan device (76.50%). Compared to several existing portable systems, our wearable system achieved superior performance with fewer channels and was validated on a larger number of subjects. These results demonstrated that our wearable BCI system can reduce preparation time, enhance portability, and meet the classification performance requirements for BCI-based rehabilitation intervention, indicating its substantial potential for large-scale clinical applications in enhancing motor recovery of stroke patients.}, }
@article {pmid40030217, year = {2025}, author = {Xu, M and Jiao, J and Chen, D and Ding, Y and Chen, Q and Wu, J and Gu, P and Pan, Y and Peng, X and Xiao, N and Yang, B and Li, Q and Guo, J}, title = {REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {3}, pages = {2136-2147}, doi = {10.1109/JBHI.2024.3498916}, pmid = {40030217}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Electrodes ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Brain/physiology/diagnostic imaging ; }, abstract = {High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.}, }
@article {pmid40030141, year = {2025}, author = {Pankka, H and Lehtinen, J and Ilmoniemi, RJ and Roine, T}, title = {Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.}, journal = {Neural computation}, volume = {37}, number = {4}, pages = {793-814}, doi = {10.1162/neco_a_01743}, pmid = {40030141}, issn = {1530-888X}, mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Brain/physiology ; Neural Networks, Computer ; Forecasting ; }, abstract = {Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 ± 1.1 µV (theta) and 0.9 ± 1.1 µV (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.}, }
@article {pmid40027274, year = {2024}, author = {Cai, Y and Li, Q and Wesselmann, U and Zhao, C}, title = {Exosomal Bupivacaine: Integrating Nerve Barrier Penetration Capability and Sustained Drug Release for Enhanced Potency in Peripheral Nerve Block and Reduced Toxicity.}, journal = {Advanced functional materials}, volume = {34}, number = {42}, pages = {}, pmid = {40027274}, issn = {1616-301X}, support = {R61 NS123196/NS/NINDS NIH HHS/United States ; }, abstract = {Peripherally injected local anesthetics exhibit limited ability to penetrate peripheral nerve barriers (PNBs), which limits their effectiveness in peripheral nerve block and increases the risk of adverse effects. In this work, we demonstrated that exosomes derived from Human Embryo Kidney (HEK) 293 cells can effectively traverse the perineurium, which is the rate-limiting barrier within PNBs that local anesthetics need to cross before acting on axons. Based on this finding, we use these exosomes as a carrier for bupivacaine (BUP), a local anesthetic commonly used in clinical settings. The in vitro assessments revealed that the prepared exosomal bupivacaine (BUP@EXO) achieves a BUP loading capacity of up to 82.33% and sustained release of BUP for over 30 days. In rats, a single peripheral injection of BUP@EXO, containing 0.75 mg of BUP, which is ineffective for BUP alone, induced a 2-hour sensory nerve blockade without significant motor impairments. Increasing the BUP dose in BUP@EXO to 2.5 mg, a highly toxic dose for BUP alone, extended the sensory nerve blockade to 12 hours without causing systemic cardiotoxicity and local neurotoxicity and myotoxicity.}, }
@article {pmid40026891, year = {2025}, author = {Moreno-Alcayde, Y and Ruotsalo, T and Leiva, LA and Traver, VJ}, title = {Brainsourcing for temporal visual attention estimation.}, journal = {Biomedical engineering letters}, volume = {15}, number = {2}, pages = {311-326}, pmid = {40026891}, issn = {2093-985X}, abstract = {The concept of temporal visual attention in dynamic contents, such as videos, has been much less studied than its spatial counterpart, i.e., visual salience. Yet, temporal visual attention is useful for many downstream tasks, such as video compression and summarisation, or monitoring users' engagement with visual information. Previous work has considered quantifying a temporal salience score from spatio-temporal user agreements from gaze data. Instead of gaze-based or content-based approaches, we explore to what extent only brain signals can reveal temporal visual attention. We propose methods for (1) computing a temporal visual salience score from salience maps of video frames; (2) quantifying the temporal brain salience score as a cognitive consistency score from the brain signals from multiple observers; and (3) assessing the correlation between both temporal salience scores, and computing its relevance. Two public EEG datasets (DEAP and MAHNOB) are used for experimental validation. Relevant correlations between temporal visual attention and EEG-based inter-subject consistency were found, as compared with a random baseline. In particular, effect sizes, measured with Cohen's d, ranged from very small to large in one dataset, and from medium to very large in another dataset. Brain consistency among subjects watching videos unveils temporal visual attention cues. This has relevant practical implications for analysing attention for visual design in human-computer interaction, in the medical domain, and in brain-computer interfaces at large.}, }
@article {pmid40026370, year = {2025}, author = {Pišot, R and Marušič, U and Šlosar, L}, title = {Addressing the Paradox of Rest with Innovative Technologies.}, journal = {Zdravstveno varstvo}, volume = {64}, number = {2}, pages = {68-72}, pmid = {40026370}, issn = {0351-0026}, abstract = {The paradox of rest lies in its dual nature: essential for recovery yet potentially harmful when prolonged. Prolonged physical inactivity (PI) significantly contributes to non-communicable diseases (NCDs). Studies show nearly a third of adults worldwide were insufficiently active in 2022, with the economic costs of PI projected to reach INT$520 billion by 2030. Bedrest models have illuminated the rapid onset of insulin resistance, general functional decline and muscle atrophy associated with PI, particularly in hospitalised older adults. Innovative technologies, such as extended reality (XR), offer promising solutions for mitigating the effects of PI and can enhance non-physical rehabilitation techniques such as motor imagery and action observation. These technologies provide immersive, personalised therapeutic experiences that engage multiple senses, transforming passive recovery into an active process and addressing both the physical and cognitive consequences of inactivity. Results of bedrest study showed significant preservation of muscle mass, improved strength and enhanced insulin sensitivity in the intervention group compared to controls. These findings highlight the potential of XR-based strategies in addressing structural and functional declines during inactivity. As part of the Interreg VI-A Italia-Slovenija project X-BRAIN.net, advanced XR-equipped active rooms were developed to aid post-stroke rehabilitation in acute care settings. XR technologies, particularly VR, have shown promise in providing dynamic and adaptable therapeutic environments that facilitate early and targeted interventions. Future advancements focus on integrating XR with brain-computer interfaces (BCIs) and synchronised visual-haptic neurofeedback, enhancing sensorimotor cortical activation and improving rehabilitation outcomes. Comprehensive multimodal approaches, including nutritional, physical and non-physical interventions, are emerging as effective strategies to personalise and optimise patient recovery.}, }
@article {pmid40026248, year = {2025}, author = {Ge, Q and Yang, J and Huang, F and Dai, X and Chen, C and Guo, J and Wang, M and Zhu, M and Shao, Y and Xia, Y and Zhou, Y and Peng, J and Deng, S and Shi, J and Hu, Y and Zhang, H and Wang, Y and Wang, X and Li, XM and Chen, Z and Shu, Y and Zhu, JM and Zhang, J and Shen, Y and Duan, S and Xu, S and Shen, L and Chen, J}, title = {Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy.}, journal = {The Journal of clinical investigation}, volume = {135}, number = {5}, pages = {}, pmid = {40026248}, issn = {1558-8238}, mesh = {Humans ; *Single-Cell Analysis ; *Cellular Senescence ; Mice ; Animals ; *Drug Resistant Epilepsy/metabolism/pathology/genetics ; Male ; Pyramidal Cells/metabolism/pathology ; Female ; Biomarkers/metabolism ; Cyclin-Dependent Kinase Inhibitor p21/metabolism/genetics ; }, abstract = {The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult to determine the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA-seq approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2, and NFKBIA, which associate with mTOR pathway, inflammatory response, and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, γ-H2AX, and β-Gal, and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of patients with drug-resistant epilepsy with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent marker expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and what we believe to be new insights into the pathophysiological mechanisms of drug-resistant epilepsy.}, }
@article {pmid40025635, year = {2025}, author = {Chen, J and Cheng, Y and Chen, L and Yang, B}, title = {Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off-the-shelf features: A dual-dataset study.}, journal = {Journal of applied clinical medical physics}, volume = {}, number = {}, pages = {e70061}, doi = {10.1002/acm2.70061}, pmid = {40025635}, issn = {1526-9914}, abstract = {BACKGROUND: This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image-based HPV prediction methods are hindered by high computational demands or suboptimal performance.<br><br>METHODS: To address these issues, we propose a methodology that employs a Siamese Neural Network architecture, integrating multi-modality off-the-shelf features-handcrafted features and 3D deep features-to enhance the representation of information. We assessed the incremental benefit of combining 3D deep features from various networks and introduced manufacturer normalization. Our method was also designed for computational efficiency, utilizing transfer learning and allowing for model execution on a single-CPU platform. A substantial dataset comprising 1453 valid samples was used as internal validation, a separate independent dataset for external validation.<br><br>RESULTS: Our proposed model achieved superior performance compared to other methods, with an average area under the receiver operating characteristic curve (AUC) of 0.791 [95% (confidence interval, CI), 0.781-0.809], an average recall of 0.827 [95% CI, 0.798-0.858], and an average accuracy of 0.741 [95% CI, 0.730-0.752], indicating promise for clinical application. In the external validation, proposed method attained an AUC of 0.581 [95% CI, 0.560-0.603] and same network architecture with pure deep features achieved an AUC of 0.700 [95% CI, 0.682-0.717]. An ablation study confirmed the effectiveness of incorporating manufacturer normalization and the synergistic effect of combining different feature sets.<br><br>CONCLUSION: Overall, our proposed model not only outperforms existing counterparts for HPV status prediction but is also computationally accessible for use on a single-CPU platform, which reduces resource requirements and enhances clinical usability.}, }
@article {pmid40025160, year = {2025}, author = {Ye, Z and Ai, Q and Liu, Y and de Rijke, M and Zhang, M and Lioma, C and Ruotsalo, T}, title = {Generative language reconstruction from brain recordings.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {346}, pmid = {40025160}, issn = {2399-3642}, support = {CHIST-ERA-20-BCI-001//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; }, mesh = {Humans ; *Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Language ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; }, abstract = {Language reconstruction from non-invasive brain recordings has been a long-standing challenge. Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. While an LLM can already generate high-quality content, our approach produces results more closely aligned with the visual or auditory language stimuli in response to which brain recordings are sampled, especially for content deemed "surprising" for the LLM. Furthermore, we show that the proposed approach can be used in an auto-regressive manner to reconstruct a 10 min-long language stimulus. Our method outperforms or is comparable to previous classification-based methods under different task settings, with the added benefit of estimating the likelihood of generating any semantic content. Our findings demonstrate the effectiveness of employing brain language interfaces in a generative setup and delineate a powerful and efficient means for mapping functional representations of language perception in the brain.}, }
@article {pmid40022809, year = {2025}, author = {Luo, TJ and Wu, T}, title = {Sum of similarity-regularized squared correlations for enhancing SSVEP detection.}, journal = {Artificial intelligence in medicine}, volume = {162}, number = {}, pages = {103100}, doi = {10.1016/j.artmed.2025.103100}, pmid = {40022809}, issn = {1873-2860}, mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Calibration ; Brain/physiology ; }, abstract = {A brain-computer interface (BCI) provides a direct control pathway between human brain and external devices. Steady-state visual evoked potential based BCI (SSVEP-BCI) has been proven to be a valuable solution due to its advantages of high information transfer rate (ITR) and minimal calibration requirement. Recently, some methods have been proposed based on calibration-training techniques to compute optimal spatial filters from covariances, and have achieved good detection performance. However, these methods ignore the temporally-varying and spatially-coupled characteristics of the EEG signals, which is essentially an important clue for enhancing ITR. More importantly, existing methods cannot well deal with intrinsic noise components of electroencephalogram (EEG) signals, greatly affecting their detection performance. In this paper, we propose a novel method, termed as Sum of Similarity-Regularized Squared Correlations (SSRSC), which is extended and regularized from the sum of squared correlations. We simultaneously compute the squared correlations for both calibration data and sine-cosine harmonics templates, and mitigate variations by the similarity regularization. Moreover, we extend the SSRSC by adopting the ranking weighted ensemble strategy, termed as weSSCOR. Extensive experiments have been conducted on two benchmark SSVEP datasets, and the results demonstrated that the proposed SSRSC/weSSRSC can significantly improve accuracy and ITR of SSVEP detection with less calibration data, which has great potential in designing high ITR SSVEP-BCIs with less calibration efforts.}, }
@article {pmid40020404, year = {2025}, author = {Wang, R and Fang, T and Zhang, Y and Cheng, Y and Wang, C and Chen, Y and Fan, Q and Zhao, X and Ming, D}, title = {The overgrowth of structure-function coupling in premature brain during infancy.}, journal = {Developmental cognitive neuroscience}, volume = {72}, number = {}, pages = {101535}, pmid = {40020404}, issn = {1878-9307}, mesh = {Humans ; *Infant, Premature/physiology ; *Magnetic Resonance Imaging/methods ; *Brain/growth &amp; development/physiology ; Male ; Female ; Infant ; Infant, Newborn ; Child Development/physiology ; Structure-Activity Relationship ; Default Mode Network/physiology/diagnostic imaging ; }, abstract = {Although the rapid growth of brain structure and function during infancy has been well documented, relatively little is known about how these two developmental processes couple-an aspect that exhibits distinct patterns in adult brain. In this study, the multimodal MRI data from the dHCP database were used to investigate the coupling between brain structure and function in infants, with a particular focus on how prematurity influences this relationship. A similar pattern of the coupling distribution between preterm and full-term infants was identified with coupling index varying across unimodal cortices such as visual and sensorimotor regions and transmodal cortices including default mode network. Notably, a widespread overgrowth of structure-function coupling and a slow developmental trajectory towards full-term infants in preterm infants at term-equivalent age were found. Collectively, the study quantified the development of structure-function relationships in preterm infants, offering new insights into the information transmission processes and developmental patterns of the early-life brain.}, }
@article {pmid40014925, year = {2025}, author = {Dold, M and Pereira, J and Sajonz, B and Coenen, VA and Thielen, J and Janssen, MLF and Tangermann, M}, title = {Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adbb20}, pmid = {40014925}, issn = {1741-2552}, mesh = {*Deep Brain Stimulation/methods/instrumentation ; *Brain-Computer Interfaces ; Humans ; *Software ; *Parkinson Disease/therapy/physiopathology ; Electroencephalography/methods/instrumentation ; }, abstract = {Objective.This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface (BCI) research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address.Approach.The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python-based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy-to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked potential (c-VEP).Main results.The platform is implemented and open-source accessible onhttps://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates.Significance.The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as BCIs. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable.}, }
@article {pmid40013095, year = {2025}, author = {Yuan, Z and Huang, Z and Li, C and Li, S and Ren, Q and Xia, X and Jiang, Q and Zhang, D and Zhu, Q and Meng, X}, title = {Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease.}, journal = {Frontiers in aging neuroscience}, volume = {17}, number = {}, pages = {1527323}, pmid = {40013095}, issn = {1663-4365}, abstract = {OBJECTIVES: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.<br><br>METHODS: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.<br><br>RESULTS: Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).<br><br>CONCLUSION: The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.}, }
@article {pmid40011760, year = {2025}, author = {Qin, R and Zhang, Y and Shi, J and Wu, P and An, C and Li, Z and Liu, N and Wan, Z and Hua, T and Li, X and Lou, J and Yin, W and Chen, W}, title = {TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity.}, journal = {Cell research}, volume = {35}, number = {4}, pages = {265-283}, pmid = {40011760}, issn = {1748-7838}, support = {T2394511//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971237//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12172371//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2394512//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32101052//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12102389//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272216//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32090044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272348//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31600751//National Natural Science Foundation of China (National Science Foundation of China)/ ; KJ2070000094//Chinese Academy of Sciences (CAS)/ ; KY9100000092//University of Science and Technology of China (USTC)/ ; }, mesh = {*Receptors, Antigen, T-Cell/metabolism/immunology/chemistry ; Humans ; *CD8 Antigens/metabolism/immunology/chemistry ; *CD8-Positive T-Lymphocytes/immunology/metabolism ; Animals ; Protein Binding ; Mice ; }, abstract = {Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR-pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR-pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR-pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.}, }
@article {pmid40010648, year = {2025}, author = {Spadacenta, S and Dicke, PW and Thier, P}, title = {Minimally invasive electrocorticography (ECoG) recording in common marmosets.}, journal = {Journal of neuroscience methods}, volume = {417}, number = {}, pages = {110409}, doi = {10.1016/j.jneumeth.2025.110409}, pmid = {40010648}, issn = {1872-678X}, mesh = {Animals ; *Callithrix ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; Male ; Brain Mapping/methods ; Brain/physiology/surgery ; Female ; Cerebral Cortex/physiology/surgery ; }, abstract = {BACKGROUND: Electrocorticography (ECoG) provides a valuable compromise between spatial and temporal resolution for recording brain activity with excellent signal quality, crucial for presurgical epilepsy mapping and advancing neuroscience, including brain-machine interface development. ECoG is particularly effective in the common marmoset (Callithrix jacchus), whose lissencephalic (unfolded) brain surface provides broad cortical access. One of the key advantages of ECoG recordings is the ability to study interactions between distant brain regions. Traditional methods rely on large electrode arrays, necessitating extensive trepanations and a trade-off between size and electrode spacing.<br><br>NEW METHOD: This study introduces a refined ECoG technique for examining interactions among multiple cortical areas in marmosets, combining circumscribed trepanations with high-density electrode arrays at specific sites of interest.<br><br>Standard ECoG techniques typically require large electrode arrays and extensive trepanation, which heighten surgical risks and the likelihood of infection, while potentially compromising spatial resolution. In contrast, our method facilitates detailed and stable recordings across multiple cortical areas with minimized invasiveness and reduced complication risks, all while preserving high spatial resolution.<br><br>RESULTS: Two adult marmosets underwent ECoG implantation in frontal, temporal, and parietal regions. Postoperative monitoring confirmed rapid recovery, long-term health, and stable, high-quality neural recordings during various behavioral tasks.<br><br>CONCLUSIONS: This refined ECoG method enhances the study of cortical interactions in marmosets while minimizing surgical invasiveness and complication risks. It offers potential for broader application in other species and opens new avenues for long-term data collection, ultimately advancing both neuroscience and brain-machine interface research.}, }
@article {pmid40010602, year = {2025}, author = {OuYang, Z and Yang, R and Wang, Y}, title = {Hotspots and Trends in Spinal Cord Stimulation Research for Spinal Cord Injury: A Bibliometric Analysis with Emphasis on Motor Recovery (2014-2024).}, journal = {World neurosurgery}, volume = {197}, number = {}, pages = {123832}, doi = {10.1016/j.wneu.2025.123832}, pmid = {40010602}, issn = {1878-8769}, abstract = {BACKGROUND: Spinal cord stimulation (SCS) has emerged as a key therapeutic strategy for enhancing motor recovery in spinal cord injury (SCI). This study employs bibliometric analysis to explore research trends and hotspots in SCS for motor recovery, highlighting advances and emerging directions over the past decade.<br><br>METHODS: This cross-sectional bibliometric study retrieved publications on SCS for motor recovery from the Web of Science Core Collection database (2014-2024). Key information, including annual publication trends, contributing countries, institutions, authors, journals, keywords, and highly cited references, was analyzed using CiteSpace and VOSviewer.<br><br>RESULTS: A total of 1033 publications were analyzed, demonstrating exponential growth in SCS research since 2014. The United States and Switzerland were identified as leading contributors, with prominent institutions such as the Swiss Federal Institute of Technology and the University of California System driving advancements. Key authors included Gr&#xe9;goire Courtine and Susan J. Harkema. Research themes have evolved through four phases: foundational studies on spinal cord mechanisms, exploration of neural circuits, application of electrical stimulation for motor recovery, and advancements in noninvasive therapies such as transcutaneous SCS. Highly cited journals, including Nature and Lancet, have published transformative studies, underscoring the field's clinical and academic significance.<br><br>CONCLUSIONS: This bibliometric analysis provides a comprehensive overview of SCS research for motor recovery post-SCI over the past decade. Interdisciplinary collaboration and technological innovation have positioned SCS as a cornerstone of SCI rehabilitation. Future efforts should focus on optimizing approaches, leveraging advanced imaging and artificial intelligence technologies, and broadening rehabilitation goals to improve outcomes for SCI patients.}, }
@article {pmid40009882, year = {2025}, author = {Ding, L and Zou, Q and Zhu, J and Wang, Y and Yang, Y}, title = {Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adba8d}, pmid = {40009882}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; Treatment Outcome ; Male ; *Drug Resistant Epilepsy/surgery/physiopathology ; *Seizures/physiopathology/surgery/diagnosis ; Adult ; Female ; Machine Learning ; Young Adult ; Adolescent ; Epilepsy/surgery/physiopathology/diagnosis ; Electroencephalography/methods ; Child ; }, abstract = {Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.}, }
@article {pmid40009879, year = {2025}, author = {Yan, Y and Li, J and Yin, M}, title = {EEG-based recognition of hand movement and its parameter.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adba8a}, pmid = {40009879}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Movement/physiology ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Neural Networks, Computer ; }, abstract = {Objecitve. Brain-computer interface is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage by now. There are still insufficient studies on the accuracy of ME EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-based hand movement recognition by analyzing low-frequency time-domain information.Approach. Experiments with four types of hand movements, two force parameter (picking up and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory Network (BiLSTM) model, an end-to-end serial combination of a BiLSTM and (CNN) is constructed to classify and recognize the hand movement based on the raw EEG data.Main results. According to the experimental results, the model is able to categorize four types of hand movements, picking up movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14% ± 0.49%, 99.29% ± 0.11%, 99.23% ± 0.60%, and 98.11% ± 0.23%, respectively.Significance. Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.}, }
@article {pmid40008568, year = {2025}, author = {Jelescu, IO and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Schilling, KG}, title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.}, journal = {Magnetic resonance in medicine}, volume = {93}, number = {6}, pages = {2507-2534}, pmid = {40008568}, issn = {1522-2594}, support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; U54 AG054349/AG/NIA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01 EB019980/EB/NIBIB NIH HHS/United States ; R01 NS119605/NS/NINDS NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; P41 EB017183/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01 CA160620/CA/NCI NIH HHS/United States ; P50 MH096889/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; *Diffusion Magnetic Resonance Imaging/methods ; *Image Processing, Computer-Assisted/methods ; Brain/diagnostic imaging ; Software ; Mice ; Reproducibility of Results ; }, abstract = {Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.}, }
@article {pmid40008460, year = {2025}, author = {Schilling, KG and Howard, AFD and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO}, title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography.}, journal = {Magnetic resonance in medicine}, volume = {93}, number = {6}, pages = {2561-2582}, pmid = {40008460}, issn = {1522-2594}, support = {34824//Canada Foundation for Innovation/ ; R01 EB031954/EB/NIBIB NIH HHS/United States ; PCEFP2_194260//Eccellenza Fellowship/ ; R01EB019980/NH/NIH HHS/United States ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; CIHRFDN-143263//Canadian Institute of Health Research/ ; //Research Center of Excellence of the University of Antwerp/ ; P30 DA048742/DA/NIDA NIH HHS/United States ; FDN-143263//CIHR/ ; R01 CA160620/CA/NCI NIH HHS/United States ; 12M3119N//Research Foundation Flanders/ ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; P30DA048742/DA/NIDA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; 101044180/ERC_/European Research Council/International ; 32454//Canada Foundation for Innovation/ ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; //NSERC/ ; 322736//Fonds de Recherche du Qu&#xe9;bec - Sant&#xe9;/ ; FWO//Research Foundation Flanders/ ; /SNSF_/Swiss National Science Foundation/Switzerland ; R01EB031954/NH/NIH HHS/United States ; RGPIN-2019-07244//Natural Sciences and Engineering Research Council of Canada/ ; K01EB032898/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01EB031765/EB/NIBIB NIH HHS/United States ; R56EB031765/EB/NIBIB NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; 5886,35450//Quebec BioImaging Network/ ; LCF/BQ/PR22/11920010//la Caixa/ ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; 2020 BP 00117//Government of Catalonia/ ; }, mesh = {*Diffusion Magnetic Resonance Imaging/methods ; Animals ; *Brain/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; *Diffusion Tensor Imaging/methods ; *Microscopy/methods ; Humans ; Signal-To-Noise Ratio ; }, abstract = {Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.}, }
@article {pmid40007882, year = {2025}, author = {Crell, MR and Kostoglou, K and Sterk, K and Müller-Putz, GR}, title = {A novel paradigm for fast training data generation in asynchronous movement-based BCIs.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1540155}, pmid = {40007882}, issn = {1662-5161}, abstract = {INTRODUCTION: Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collection of 300 cued movement trials in 18 min.<br><br>METHODS: By obtaining measurements from ten participants, we demonstrate that the data produced by this paradigm exhibits characteristics similar to those observed during self-paced movement.<br><br>RESULTS AND DISCUSSION: We also show that classifiers trained on this data can be used to accurately detect executed movements with an average true positive rate of 31.8% at a maximum rate of 1.0 false positives per minute.}, }
@article {pmid40007617, year = {2025}, author = {Wu, H and Feng, E and Yin, H and Zhang, Y and Chen, G and Zhu, B and Yue, X and Zhang, H and Liu, Q and Xiong, L}, title = {Biomaterials for neuroengineering: applications and challenges.}, journal = {Regenerative biomaterials}, volume = {12}, number = {}, pages = {rbae137}, pmid = {40007617}, issn = {2056-3418}, abstract = {Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.}, }
@article {pmid40007259, year = {2025}, author = {Sirakov, A and Ninov, K and Sirakova, K and Sirakov, SS}, title = {Blazing the trail! Commentary on "Intra-arterial lidocaine administration in middle meningeal artery for short-term treatment of subarachoid hemorrhage-related headaches" by Qureshi et al.}, journal = {Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences}, volume = {}, number = {}, pages = {15910199251324039}, pmid = {40007259}, issn = {2385-2011}, abstract = {In their recently published INR study, Qureshi et al. present their results on intra-arterial lidocaine administration in the middle meningeal artery for the short-term treatment of subarachnoid hemorrhage (SAH)-related headaches. The authors demonstrate that their proposed intra-arterial treatment consistently alleviates headaches in patients with SAH. The purpose of this commentary is to commend the authors on their paper and the notable results they have achieved. It is always pleasant to encounter studies that not only make it to the "Latest Online" section of neurointerventional journals but also push the boundaries, advancing our understanding and care for patients in the most meaningful ways. There is no doubt that our field has witnessed remarkable progress and an expanding spectrum of interventions that endovascular neuroservices can offer. Several therapeutic approaches have emerged from similarly constructive articles, including intra-arterial chemotherapy for malignant cerebral tumors, innovative treatments for cerebrospinal fluid-venous fistulas, hydrocephalus, and chronic subdural hematomas, as well as the implantation of brain-computer interface devices.}, }
@article {pmid40006451, year = {2025}, author = {Zaidi, SR and Khan, NA and Hasan, MA}, title = {Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {4}, pages = {}, pmid = {40006451}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Emotions/physiology ; *Machine Learning ; Adult ; *Guilt ; Young Adult ; Neurosciences/methods ; Brain/physiology ; Support Vector Machine ; Sex Factors ; }, abstract = {This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the study were developed to ignite guilt and neutral emotions. Two emotions, "guilt" and "neutral", were recorded from 16 participants after these emotions were induced using storyboards as pictorial stimuli. These storyboards were developed based on various guilt-provoking events shared by another group of participants. In the pre-processing step, collected data were de-noised using bandpass filters and ICA, then segmented into smaller sections for further analysis. Two approaches were used to feed these data to the SVM classifier. First, the novel approach employed involved feeding the data to SVM classifier without computing any features. This method provided an average accuracy of 83%. In the second approach, data were divided into Alpha, Beta, Gamma, Theta and Delta frequency bands using Discrete Wavelet Decomposition. Afterward, the computed features, including entropy, Hjorth parameters and Band Power, were fed to SVM classifiers. This approach achieved an average accuracy of 63%. The findings of both classification methodologies indicate that females are more expressive in response to depicted stimuli and that their brain cells exhibit higher feature values. Moreover, females displayed higher accuracy than males in all bands except the Delta band.}, }
@article {pmid40006375, year = {2025}, author = {Wu, Y and Cao, P and Xu, M and Zhang, Y and Lian, X and Yu, C}, title = {Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {4}, pages = {}, pmid = {40006375}, issn = {1424-8220}, support = {CEIEC-2023-ZM02-0090//Industrial Internet identification analysis System/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology ; }, abstract = {Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial-spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time-spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain-computer interface (BCI) systems.}, }
@article {pmid40003400, year = {2025}, author = {Teteh-Brooks, DK and Ericson, M and Bethea, TN and Dawkins-Moultin, L and Sarkaria, N and Bailey, J and Llanos, AAM and Montgomery, S}, title = {Validating the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) Among Black Breast Cancer Survivors.}, journal = {International journal of environmental research and public health}, volume = {22}, number = {2}, pages = {}, pmid = {40003400}, issn = {1660-4601}, support = {K01 MD018417/MD/NIMHD NIH HHS/United States ; L60 CA253971/CA/NCI NIH HHS/United States ; R01 CA217841/CA/NCI NIH HHS/United States ; R43 MD017966/MD/NIMHD NIH HHS/United States ; }, mesh = {Humans ; *Breast Neoplasms ; Female ; *Cancer Survivors/psychology/statistics &amp; numerical data ; Middle Aged ; *Hair Preparations ; Adult ; *Black or African American ; Surveys and Questionnaires ; Aged ; White ; }, abstract = {UNLABELLED: Personal care products containing toxic chemicals (e.g., endocrine-disrupting chemicals) may increase breast cancer risk, especially for Black women who use these products more than other racial groups. There are limited tools that examine the intersections of identity, behaviors, and attitudes surrounding product use, perceived safety, and breast cancer risk; thus, the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) was developed to bridge this gap. While initial validations lacked diverse survivor representation, this study seeks to validate the BHBS among Black survivors.<br><br>METHODS: This study is a part of the Bench to Community Initiative (BCI), where respondents (n = 167) completed a 41-item survey including the BHBS between 2020 and 2022. The use of Principal Component Analysis (PCA) and confirmatory factor analysis (CFA) established the underlying component structures and model fit. CFA measures used to confirm component structures included the Root Mean Square Error of Approximation, the Comparative Fit Index, and the Tucker Lewis Index.<br><br>RESULTS: Black survivors on average were diagnosed with breast cancer before age 40 (37.41 &#xb1; 8.8) with Stage 1 (45%) disease. Sixty-three percent of the total variance resulted in a two-component structure. Subscale 1 (S1) measures the sociocultural perspectives about hair and identity (28% of the total variance; &#x3b1; = 0.73; 95% CI = 0.71-0.82). Subscale 2 (S2) can be used to assess perceived breast cancer risk related to hair product use (35% of the total variance; &#x3b1; = 0.86; 95% CI = 0.81-0.94). The two-component structure was confirmed with Root Mean Square Error of Approximation = 0.034, Comparative Fit Index = 0.93, and Tucker Lewis Index = 0.89.<br><br>DISCUSSION/CONCLUSIONS: The BHBS is a valid tool to measure identity, attitudes, and behaviors about product use and breast cancer risk among survivors. Hair is a significant cultural identity expression, and the health effects of styling products should be considered in future interventions.}, }
@article {pmid40002607, year = {2025}, author = {Halkiopoulos, C and Gkintoni, E and Aroutzidis, A and Antonopoulou, H}, title = {Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {15}, number = {4}, pages = {}, pmid = {40002607}, issn = {2075-4418}, abstract = {Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.}, }
@article {pmid40002501, year = {2025}, author = {Wu, C and Yao, B and Zhang, X and Li, T and Wang, J and Pu, J}, title = {The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {15}, number = {2}, pages = {}, pmid = {40002501}, issn = {2076-3425}, support = {RS2024X007//Key Project of Construction of Drug Regulatory Science System/ ; 2021ZD0200406//STI 2030-MajorProjects under grant/ ; 2021-I2M-1-042, 2021-I2M-1-058//the Medical and Health Innovation Project/ ; 20JCJQIC00230//the Tianjin Outstanding Youth Fund Project/ ; }, abstract = {Background: In motor imagery brain-computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8-10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.}, }
@article {pmid40002462, year = {2025}, author = {Wu, P and Fei, K and Chen, B and Pan, L}, title = {MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding.}, journal = {Brain sciences}, volume = {15}, number = {2}, pages = {}, pmid = {40002462}, issn = {2076-3425}, support = {61773078//the National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Due to complex signal characteristics and distinct individual differences, the decoding of a motor imagery electroencephalogram (MI-EEG) is limited by the unsatisfactory performance of suboptimal traditional models.<br><br>METHODS: A subject-independent model named MSEI-ENet is proposed for multiple-task MI-EEG decoding. It employs a specially designed multi-scale structure EEG-inception module (MSEI) for comprehensive feature learning. The encoder module further helps to detect discriminative information by its multi-head self-attention layer with a larger receptive field, which enhances feature representation and improves recognition efficacy.<br><br>RESULTS: The experimental results on Competition IV dataset 2a showed that our proposed model yielded an overall accuracy of 94.30%, MF1 score of 94.31%, and Kappa of 0.92.<br><br>CONCLUSIONS: A performance comparison with state-of-the-art methods demonstrated the effectiveness and generalizability of the proposed model on challenging multi-task MI-EEG decoding.}, }
@article {pmid40002457, year = {2025}, author = {Gu, H and Chen, T and Ma, X and Zhang, M and Sun, Y and Zhao, J}, title = {CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.}, journal = {Brain sciences}, volume = {15}, number = {2}, pages = {}, pmid = {40002457}, issn = {2076-3425}, abstract = {BACKGROUND: Brain-computer interface (BCI) technology opens up new avenues for human-machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application.<br><br>METHODS: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer's self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer.<br><br>RESULTS: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods.<br><br>CONCLUSIONS: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.}, }
@article {pmid40002431, year = {2025}, author = {Yang, Y and Li, M and Liu, J}, title = {Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG.}, journal = {Brain sciences}, volume = {15}, number = {2}, pages = {}, pmid = {40002431}, issn = {2076-3425}, support = {62173010//Mingai Li/ ; }, abstract = {BACKGROUND/OBJECTIVES: Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL.<br><br>METHODS: First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks.<br><br>RESULTS: The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods.<br><br>CONCLUSIONS: Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.}, }
@article {pmid40001978, year = {2025}, author = {Al-Nafjan, A and Alshehri, H and Aldayel, M}, title = {Objective Pain Assessment Using Deep Learning Through EEG-Based Brain-Computer Interfaces.}, journal = {Biology}, volume = {14}, number = {2}, pages = {}, pmid = {40001978}, issn = {2079-7737}, support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority (RDIA)-Kingdom of Saudi Arabia/ ; }, abstract = {Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain-computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time-frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.}, }
@article {pmid40000219, year = {2024}, author = {He, W and Wang, D and Meng, Q and He, F and Xu, M and Ming, D}, title = {[Applications and prospects of electroencephalography technology in neurorehabilitation assessment and treatment].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {6}, pages = {1271-1278}, pmid = {40000219}, issn = {1001-5515}, mesh = {Humans ; *Electroencephalography ; *Neurological Rehabilitation/methods/instrumentation ; *Brain-Computer Interfaces ; *Transcranial Magnetic Stimulation/methods ; Transcranial Direct Current Stimulation/methods ; Nervous System Diseases/rehabilitation ; Epilepsy/rehabilitation ; }, abstract = {With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.}, }
@article {pmid40000217, year = {2024}, author = {Yang, H and Li, T and Zhao, L and Chen, X and Pan, J and Fu, Y}, title = {[An emerging major: brain-computer interface major].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {6}, pages = {1257-1264}, pmid = {40000217}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Brain/physiology ; User-Computer Interface ; }, abstract = {Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major-BCI major-has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.}, }
@article {pmid40000203, year = {2024}, author = {Wu, X and Chu, Y and Zhao, X and Zhao, Y}, title = {[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {6}, pages = {1145-1152}, pmid = {40000203}, issn = {1001-5515}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.}, }
@article {pmid40000192, year = {2025}, author = {Chen, Z and Huang, Y and Yu, H and Cao, C and Xu, M and Ming, D}, title = {[Research progress on the characteristics of magnetoencephalography signals in depression].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {1}, pages = {189-196}, pmid = {40000192}, issn = {1001-5515}, mesh = {*Magnetoencephalography ; Humans ; *Brain/physiopathology/diagnostic imaging ; *Depression/diagnosis/physiopathology ; Electroencephalography ; Magnetic Resonance Imaging ; }, abstract = {Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.}, }
@article {pmid40000170, year = {2025}, author = {Zhang, Y and Zhang, C and Sun, S and Xu, G}, title = {[Research on motor imagery recognition based on feature fusion and transfer adaptive boosting].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {42}, number = {1}, pages = {9-16}, pmid = {40000170}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; *Support Vector Machine ; Humans ; *Algorithms ; *Neural Networks, Computer ; Electroencephalography ; Imagination/physiology ; Wavelet Analysis ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; }, abstract = {This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 [th] International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.}, }
@article {pmid39999624, year = {2025}, author = {King, SE and Waddell, JT and McDonald, AE and Corbin, WR}, title = {Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.}, journal = {Drug and alcohol dependence}, volume = {270}, number = {}, pages = {112601}, pmid = {39999624}, issn = {1879-0046}, support = {F31 AA030167/AA/NIAAA NIH HHS/United States ; T32 DA039772/DA/NIDA NIH HHS/United States ; }, mesh = {Humans ; Female ; Male ; *Craving ; Young Adult ; Adult ; *Alcohol Drinking/psychology ; Adolescent ; *Ecological Momentary Assessment ; *Peer Group ; Emotions ; }, abstract = {BACKGROUND: Subjective response to alcohol is a robust predictor of alcohol outcomes. It is possible that the perceived subjective response of others may influence concurrent experiences of one's own subjective response. However, no studies have examined how the perceived subjective response of others might interact with personal subjective response and how such interactions may influence levels of craving and subsequent drinking.<br><br>METHOD: Emerging adults (ages 18-25, N&#x2009;=&#x2009;131, 53.4&#x2009;% female) completed 21 days of ecological momentary assessments. During drinking events (N&#x2009;=&#x2009;1335) both personal and perceived peer subjective response (four domains encompassing high- and low-arousal positive &amp; negative effects) were assessed at drink initiation and two subsequent surveys 60 and 120min later. Current craving and drinking quantity since last report were also collected. Three-level multilevel structural equation models with Bayesian estimation tested indirect relations between subjective response and drinking continuation via craving and whether perceived subjective response moderated such relations.<br><br>RESULTS: Levels of both personal (b=0.029,95&#x2009;%BCI:[0.012,0.053]) and perceived (b=0.027,95&#x2009;%BCI:[0.012,0.051]) experiences of alcohol's rewarding, stimulating effects indirectly predicted drinking continuation via increased craving, and relations were potentiated when perceptions of peer reward were highest (b=0.015,95&#x2009;%BCI:[0.008,0.020]). Personal experiences of alcohol's relaxing, calming effects indirectly predicted a lower likelihood of drinking continuation via decreased craving (b=-0.017,95&#x2009;%BCI:[-0.036,-0.003]) whereas perceived effects directly predicted lower likelihoods of drinking (b=-0.133,95&#x2009;%CI:[-0.239, -0.031]).<br><br>CONCLUSION: Results suggest both personal and perceived peer subjective response independently influence drinking behavior even when controlling for one another. Targeted interventions focused on altering interpretations of peer subjective effects may be effective at reducing momentary risk.}, }
@article {pmid39997141, year = {2025}, author = {Ha, J and Park, S and Han, Y and Kim, L}, title = {Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, pmid = {39997141}, issn = {2313-7673}, support = {RS-2024-00340293//the National Research Foundation of Korea (NRF)/ ; }, abstract = {Brain-computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities.}, }
@article {pmid39997117, year = {2025}, author = {Premchand, B and Zhang, Z and Ang, KK and Yu, J and Tan, IO and Lam, JPW and Choo, AXY and Sidarta, A and Kwong, PWH and Chung, LHC}, title = {A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, pmid = {39997117}, issn = {2313-7673}, support = {//Institute for Infocomm Research/ ; Research Grant RRG4/2008//Rehabilitation Research Institute of Singapore/ ; }, abstract = {Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient's specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant's respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.}, }
@article {pmid39996608, year = {2025}, author = {Hjortkjær, J and Wong, DDE and Catania, A and Märcher-Rørsted, J and Ceolini, E and Fuglsang, SA and Kiselev, I and Di Liberto, G and Liu, SC and Dau, T and Slaney, M and de Cheveigné, A}, title = {Real-time control of a hearing instrument with EEG-based attention decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad867c}, pmid = {39996608}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Attention/physiology ; *Hearing Aids ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Computer Systems ; Acoustic Stimulation/methods ; Male ; Female ; Adult ; }, abstract = {Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface system that combines a stimulus-response model based on canonical correlation analysis for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.}, }
@article {pmid39996071, year = {2025}, author = {Samadi, E and Rahatabad, FN and Nasrabadi, AM and Dabanlou, NJ}, title = {Brain analysis to approach human muscles synergy using deep learning.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {44}, pmid = {39996071}, issn = {1871-4080}, abstract = {Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.}, }
@article {pmid39994209, year = {2025}, author = {Guan, S and Dong, T and Cong, LK}, title = {Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {6601}, pmid = {39994209}, issn = {2045-2322}, support = {20220508014RC//Project supported by Jilin Provincial Science and Technology Development Plan Project/ ; }, abstract = {In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. Subsequently, kernel principal component analysis (KPCA) is employed to fuse and reduce the dimensionality of the joint features, resulting in a reduced-dimensional fused feature vector. Finally, these feature vectors are input into a Radius-incorporated multi-kernel extreme learning machine (RIO-MKELM) for classification. The experimental results indicate that through multi-domain feature fusion and the incorporation of radius in a multi-kernel extreme learning machine, feature selection can be performed more effectively, eliminating redundant or irrelevant features and retaining the most useful information for classification. This approach enhances classification accuracy and other evaluation metrics, with the final classification accuracy reaching 95.49%, sensitivity at 97.88%, specificity at 98.12%, recall at 97.88%, and F1 Score at 96.67%. The findings of this study are of significant importance for the development and practical application of brain-computer interface (BCI) systems.}, }
@article {pmid39993988, year = {2025}, author = {Guo, J and Yang, J and Li, Y}, title = {[Analysis of Brain-Computer Interface Technology in the Medical Field and the Regulation of the US FDA].}, journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation}, volume = {49}, number = {1}, pages = {96-102}, doi = {10.12455/j.issn.1671-7104.240187}, pmid = {39993988}, issn = {1671-7104}, mesh = {*Brain-Computer Interfaces ; United States ; *United States Food and Drug Administration ; Humans ; Electroencephalography ; Device Approval ; }, abstract = {Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.}, }
@article {pmid39993333, year = {2025}, author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, NG and Downey, JE and Chase, SM and Boninger, ML and Dekleva, BD and Collinger, JL}, title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adb995}, pmid = {39993333}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; *Imagination/physiology ; Adult ; Female ; *Movement/physiology ; Spinal Cord Injuries/physiopathology ; Electrodes, Implanted ; Middle Aged ; Brain Mapping/methods ; Hand/physiology ; }, abstract = {Objective:The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control.Approach:Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder.Main results:We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex.Significance:These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.}, }
@article {pmid39992333, year = {2024}, author = {Qin, HJ and Liu, YY and Fu, EH and Liu, YW and Tian, ZL and Dong, HW and Liu, TA and Zou, DH and Cheng, YB and Liu, NG}, title = {Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.}, journal = {Fa yi xue za zhi}, volume = {40}, number = {5}, pages = {419-429}, doi = {10.12116/j.issn.1004-5619.2024.440801}, pmid = {39992333}, issn = {1004-5619}, mesh = {Humans ; *Tomography, X-Ray Computed/methods ; *Neural Networks, Computer ; *Head Injuries, Closed/diagnostic imaging ; Deep Learning ; Image Processing, Computer-Assisted/methods ; Craniocerebral Trauma/diagnostic imaging ; Forensic Medicine/methods ; Skull Fractures/diagnostic imaging ; }, abstract = {OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.<br><br>METHODS: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and F1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.<br><br>RESULTS: After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the F1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.<br><br>CONCLUSIONS: Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.}, }
@article {pmid39992067, year = {2025}, author = {Wan, C and Zhang, W and Nie, Y and Qian, Y and Wang, J and Xu, H and Li, Z and Su, B and Zhang, Y and Li, Y}, title = {Impact of motor imagery-based brain-computer interface combined with virtual reality on enhancing attention, executive function, and lower-limb function in stroke: A pilot study.}, journal = {PM &amp; R : the journal of injury, function, and rehabilitation}, volume = {}, number = {}, pages = {}, doi = {10.1002/pmrj.13324}, pmid = {39992067}, issn = {1934-1563}, support = {No.303103136AA22//National Clinical Medical Research Centre Cultivation Program of Nanjing/ ; No.ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; }, abstract = {BACKGROUND: Brain-computer interface combined with virtual reality (BCI-VR) can reduce the difficulty of motor imagery execution and improve training performance. Few studies have focused on the effects of BCI-VR on attention, executive function, and lower-limb function in stroke.<br><br>OBJECTIVE: To evaluate feasibility and preliminary efficacy of BCI-VR pedaling training on the attention, executive function, and lower-extremity function in people after stroke. It will also provide data support for future research, especially sample size calculations.<br><br>DESIGN: A single group before-after trial design was used. All participants had a stable level of function over a 2-week period to ensure that their functional recovery was all attributable to BCI-VR training.<br><br>SETTING: The study was conducted in a specialized rehabilitation hospital.<br><br>PARTICIPANTS: Twelve participants with stroke, a certain level of motor imagery ability, capable of walking 10 meters continuously.<br><br>INTERVENTIONS: All participants received a 4-week BCI-VR pedaling training program, 5&#x2009;days per week, 30&#x2009;minutes each session.<br><br>OUTCOME MEASURES: Primary outcomes are feasibility and safety. Secondary outcomes were lower-extremity mobility, attention, and executive functions.<br><br>RESULTS: Twelve patients were recruited from inpatient rehabilitation and nine completed the study (six males/three females; 56.6&#x2009;&#xb1;&#x2009;11.6&#x2009;years). Recruitment and retention rates were 34% and 75%, respectively. Excellent adherence rate (97.7%) was obtained. No adverse events or equipment issues were reported. Following the intervention, significant improvements were found in the lower-extremity strength, balance, walking stability, attention, and general cognitive function (p&#x2009;<&#x2009;.05). A significant correlation was found between improved Berg balance scale change values and symbol digit modalities test change values (p&#x2009;<&#x2009;.05, r&#x2009;=&#x2009;0.677).<br><br>CONCLUSIONS: BCI-VR pedaling training provides a depth of feasibility and safety data, methodological detail, and preliminary results. This could provide a useful basis for future studies of BCI-VR pedaling training for stroke rehabilitation.<br><br>CLINICALTRIALS: gov registration number: ChiCTR2300071522 (http://www.chictr.org.cn).}, }
@article {pmid39990438, year = {2025}, author = {Lin, Z and Marin-Llobet, A and Baek, J and He, Y and Lee, J and Wang, W and Zhang, X and Lee, AJ and Liang, N and Du, J and Ding, J and Li, N and Liu, J}, title = {Spike sorting AI agent.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39990438}, issn = {2692-8205}, support = {DP1 DK130673/DK/NIDDK NIH HHS/United States ; R01 LM014465/LM/NLM NIH HHS/United States ; }, abstract = {Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.}, }
@article {pmid39990331, year = {2025}, author = {Pescatore, CRC and Zhang, H and Hadjinicolaou, AE and Paulk, AC and Rolston, JD and Richardson, RM and Williams, ZM and Cai, J and Cash, SS}, title = {Decoding semantics from natural speech using human intracranial EEG.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39990331}, issn = {2692-8205}, support = {R01 DC019653/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCIs) hold promise for restoring natural language production capabilities in patients with speech impairments, potentially enabling smooth conversation that conveys meaningful information via synthesized words. While considerable progress has been made in decoding phonetic features of speech, our ability to extract lexical semantic information (i.e. the meaning of individual words) from neural activity remains largely unexplored. Moreover, most existing BCI research has relied on controlled experimental paradigms rather than natural conversation, limiting our understanding of semantic decoding in ecological contexts. Here, we investigated the feasibility of decoding lexical semantic information from stereo-electroencephalography (sEEG) recordings in 14 participants during spontaneous conversation. Using multivariate pattern analysis, we were able to decode word level semantic features during language production with an average accuracy of 21% across all participants compared to a chance level of 10%. This semantic decoding remained robust across different semantic representations while maintaining specificity to semantic features. Further, we identified a distributed left-lateralized network spanning precentral gyrus, pars triangularis, and middle temporal cortex, with low-frequency oscillations showing stronger contributions. Together, our results establish the feasibility of extracting word meanings from neural activity during natural speech production and demonstrate the potential for decoding semantic content from unconstrained speech.}, }
@article {pmid39989959, year = {2025}, author = {Shoffstall, A and Li, L and Hartzler, A and Menendez-Lustri, D and Zhang, J and Chen, A and Lam, D and Traylor, B and Quill, E and Hoeferlin, G and Pawlowski, C and Bruckman, M and Gupta, SA and Capadona, J}, title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {39989959}, issn = {2693-5015}, support = {I01 RX003420/RX/RRD VA/United States ; R01 HL121212/HL/NHLBI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; }, abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by the IME insertions lead to the increased neuroinflammation and reduced neural recording performance. Additionally, a sustained presence of activated platelets and coagulation factors is found near the insertion site. Thus, we hypothesized that the systemic administration of dexamethasone sodium phosphate-loaded platelet-inspired nanoparticle (SPPINDEX) can improve the neural recording performance of intracortical microelectrodes (IMEs) by promoting hemostasis, facilitating blood-brain barrier (BBB) healing, and achieving implant-targeted drug delivery. Leveraging the hemostatic and coagulation factor-binding properties of the platelet-inspired nanoparticle (PIN) drug delivery platform, SPPINDEX treatment can initially attenuate the invasion of neuroinflammatory triggers into the brain parenchyma caused by insertion-induced microhemorrhages or a compromised BBB. Furthermore, targeted delivery of the anti-inflammatory drug dexamethasone sodium phosphate (DEXSP) to the implant site via these nanoparticles can attenuate ongoing neuroinflammation, enhancing overall therapeutic efficacy. Weekly treatment with SPPINDEX for 8 weeks significantly improved the recording capabilities of IMEs compared to platelet-inspired nanoparticles alone (PIN), free dexamethasone sodium phosphate (Free DEXSP), and a diluent control trehalose buffer (TH), as assessed through extracellular single-unit recordings. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggest that the improved neural recording performance may be attributed to reduced neuron degeneration, activated microglia and astrocytes at the implant interface caused by the decreased infiltration of blood-derived proteins that trigger neuroinflammation and the therapeutic effects from DEXSP. Overall, SPPINDEX treatment promotes an anti-inflammatory environment that improves neuronal density and enhances recording performance.}, }
@article {pmid39988822, year = {2025}, author = {Park, J and Ahn, J and Choi, J and Kim, J}, title = {Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.}, journal = {Journal of chemical information and modeling}, volume = {65}, number = {5}, pages = {2283-2296}, pmid = {39988822}, issn = {1549-960X}, mesh = {*Drug Discovery/methods ; Artificial Intelligence ; Machine Learning ; Reward ; }, abstract = {Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing approaches in generating molecules having the desired properties, including penalized LogP, QED, and celecoxib similarity, without any prior knowledge. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.}, }
@article {pmid39987217, year = {2025}, author = {Wang, X and Chen, S and Wang, K and Cao, L}, title = {Predicted action-effects shape action representation through pre-activation of alpha oscillations.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {275}, pmid = {39987217}, issn = {2399-3642}, support = {32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; Male ; Female ; Adult ; *Alpha Rhythm/physiology ; Young Adult ; Visual Cortex/physiology ; Attention/physiology ; Psychomotor Performance/physiology ; Feedback, Sensory/physiology ; }, abstract = {Actions are typically accompanied by sensory feedback (or action-effects). Action-effects, in turn, influence the action. Theoretical accounts of action control assume a pre-activation of action-effects prior to action execution. Here we show that when participants were asked to report the time of their voluntary keypress using the position of a fast-rotating clock hand, a predictable action-effect (i.e. a 250 ms delayed sound after keypress) led to a shift of visuospatial attention towards the clock hand position of action-effect onset, thus demonstrating an influence of action-effects on action representation. Importantly, the attention shift occurred about 1 second before the action execution, which was further preceded and predicted by a lateralisation of alpha oscillations in the visual cortex. Our results indicate that when the spatial location is the key feature of action-effects, the neural implementation of the action-effect pre-activation is achieved through alpha lateralisation.}, }
@article {pmid39986990, year = {2025}, author = {Ding, N}, title = {Sequence chunking through neural encoding of ordinal positions.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2025.01.014}, pmid = {39986990}, issn = {1879-307X}, abstract = {Grouping sensory events into chunks is an efficient strategy to integrate information across long sequences such as speech, music, and complex movements. Although chunks can be constructed based on diverse cues (e.g., sensory features, statistical patterns, internal knowledge) recent studies have consistently demonstrated that the chunks constructed by different cues are all tracked by low-frequency neural dynamics. Here, I review evidence that chunking cues drive low-frequency activity in modality-dependent networks, which interact to generate chunk-tracking activity in broad brain areas. Functionally, this work suggests that a core computation underlying sequence chunking may assign each event its ordinal position within a chunk and that this computation is causally implemented by chunk-tracking neural activity during predictive sequence chunking.}, }
@article {pmid39986550, year = {2025}, author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H}, title = {Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation.}, journal = {NeuroImage}, volume = {309}, number = {}, pages = {121097}, doi = {10.1016/j.neuroimage.2025.121097}, pmid = {39986550}, issn = {1095-9572}, mesh = {Humans ; *Sleep Deprivation/physiopathology/cerebrospinal fluid ; Male ; Female ; Adult ; Reproducibility of Results ; *Brain ; *Glymphatic System/physiology/diagnostic imaging ; *Sleep/physiology ; Young Adult ; *Cerebrospinal Fluid/physiology ; Magnetic Resonance Imaging ; }, abstract = {The glymphatic system (GS) plays a key role in maintaining brain homeostasis by clearing metabolic waste during sleep, with the coupling between global blood-oxygen-level-dependent (gBOLD) and cerebrospinal fluid (CSF) signals serving as a potential marker for glymphatic clearance function. However, the test-retest reliability and spatial heterogeneity of gBOLD-CSF coupling after different sleep conditions remain unclear. In this study, we assessed the test-retest reliability of gBOLD-CSF coupling following either normal sleep or total sleep deprivation (TSD) in 64 healthy adults under controlled laboratory conditions. The reliability was high after normal sleep (ICC = 0.763) but decreased following TSD (ICC = 0.581). Moreover, spatial heterogeneity was evident in participants with normal sleep, with lower-order networks (visual, somatomotor, and attention) showing higher ICC values compared to higher-order networks (default-mode, limbic, and frontoparietal). This spatial variation was less distinct in the TSD group. These results demonstrate the robustness of the gBOLD-CSF coupling method and emphasize the significance of considering sleep history in glymphatic function research.}, }
@article {pmid39985774, year = {2025}, author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Lu, J and Li, Y}, title = {Protocol to perform offline ECoG brain-to-text decoding for natural tonal sentences.}, journal = {STAR protocols}, volume = {6}, number = {1}, pages = {103650}, pmid = {39985774}, issn = {2666-1667}, mesh = {*Electrocorticography/methods ; Humans ; Brain/physiology ; Speech/physiology ; Language ; Brain-Computer Interfaces ; }, abstract = {Here, we present a protocol to decode Mandarin sentences from invasive neural recordings using a brain-to-text framework. We describe steps for preparing materials, including designing the sentence corpus and setting up electrocorticography (ECoG) recording systems. We then detail procedures for decoding, such as data preprocessing, selection of speech-responsive electrodes, speech detection, syllable and tone decoding, and language modeling. We also outline performance evaluation metrics. For complete details on the use and execution of this protocol, please refer to Zhang et al.[1].}, }
@article {pmid39984530, year = {2025}, author = {Liu, Y and Gui, Z and Yan, D and Wang, Z and Gao, R and Han, N and Chen, J and Wu, J and Ming, D}, title = {Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {314}, pmid = {39984530}, issn = {2052-4463}, support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Electroencephalography ; *Lower Extremity/physiopathology ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Imagination ; Neuronal Plasticity ; }, abstract = {Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.}, }
@article {pmid39983772, year = {2025}, author = {Wang, M and Zhang, Y and Wang, A and Gan, Z and Zhang, L and Kang, X}, title = {Soft neural interface with color adjusted PDMS encapsulation layer for spinal cord stimulation.}, journal = {Journal of neuroscience methods}, volume = {417}, number = {}, pages = {110402}, doi = {10.1016/j.jneumeth.2025.110402}, pmid = {39983772}, issn = {1872-678X}, mesh = {Animals ; *Dimethylpolysiloxanes/chemistry ; Mice ; *Spinal Cord Stimulation/methods/instrumentation ; Electrodes, Implanted ; Color ; Lasers ; Biocompatible Materials/chemistry ; Microtechnology/instrumentation ; Spinal Cord/physiology ; }, abstract = {BACKGROUND: Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.<br><br>NEW METHOD: Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.<br><br>RESULTS: Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.<br><br>The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.<br><br>CONCLUSIONS: The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.}, }
@article {pmid39983236, year = {2025}, author = {Phang, CR and Hirata, A}, title = {Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adb90c}, pmid = {39983236}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Sleep Stages/physiology ; Male ; Female ; Adult ; Polysomnography/methods ; Young Adult ; Middle Aged ; }, abstract = {Objective.Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilitate the tedious manual labeling process. Notably, recently proposed sleep staging algorithms lack model explainability and still require performance improvement.Approach.We implemented multiscale neurophysiology-mimicking kernels to capture sleep-related electroencephalogram (EEG) activities at varying frequencies and temporal lengths; the implemented model was named 'multiscale temporal convolutional neural network (MTCNN).' Further, we evaluated its performance using an open-source dataset (Sleep-EDF Database Expanded comprising 153 d of polysomnogram data).Main results.By investigating the learned kernel weights, we observed that MTCNN detected the EEG activities specific to each sleep stage, such as the frequencies, K-complexes, and sawtooth waves. Furthermore, regarding the characterization of these neurophysiologically significant features, MTCNN demonstrated an overall accuracy (OAcc) of 91.12% and a Cohen kappa coefficient of 0.86 in the cross-subject paradigm. Notably, it demonstrated an OAcc of 88.24% and a Cohen kappa coefficient of 0.80 in the leave-few-days-out analysis. Our MTCNN model also outperformed the existing deep learning models in sleep stage classification even when it was trained with only 16% of the total EEG data, achieving an OAcc of 85.62% and a Cohen kappa coefficient of 0.75 on the remaining 84% of testing data.Significance.The proposed MTCNN enables model explainability and it can be trained with lesser amount of data, which is beneficial to its application in the real-world because large amounts of training data are not often and readily available.}, }
@article {pmid39983235, year = {2025}, author = {T A, S and R, S and Vinod, AP and Alladi, S}, title = {On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and mild cognitive impairment patients.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {2}, pages = {}, doi = {10.1088/2057-1976/adb8ef}, pmid = {39983235}, issn = {2057-1976}, mesh = {Humans ; *Neurofeedback/methods ; *Cognitive Dysfunction/therapy ; *Memory, Short-Term ; Male ; *Brain-Computer Interfaces ; Female ; *Electroencephalography/methods ; Middle Aged ; *Attention ; Aged ; *Stroke/complications/physiopathology ; *Feasibility Studies ; Stroke Rehabilitation/methods ; Video Games ; Brain/physiopathology ; }, abstract = {Background. Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition.Methods. We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen participants (6 Stroke, 7 MCI, and 2 non-patients). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.Results. The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.Significance. The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.Conclusions. The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.}, }
@article {pmid39981822, year = {2025}, author = {Cai, Y and Li, Q and Banga, AK and Wesselmann, U and Zhao, C}, title = {Tetrodotoxin Delivery Pen Safely Uses Potent Natural Neurotoxin to Manage Severe Cutaneous Pain.}, journal = {Advanced healthcare materials}, volume = {14}, number = {9}, pages = {e2401549}, pmid = {39981822}, issn = {2192-2659}, support = {R61 NS123196/NS/NINDS NIH HHS/United States ; R01 GM144388/GM/NIGMS NIH HHS/United States ; R15GM139193/GM/NIGMS NIH HHS/United States ; R15 GM139193/GM/NIGMS NIH HHS/United States ; R61NS123196/NS/NINDS NIH HHS/United States ; R01GM144388/GM/NIGMS NIH HHS/United States ; }, mesh = {Animals ; *Tetrodotoxin/administration &amp; dosage/pharmacology/therapeutic use ; Rats ; Rats, Sprague-Dawley ; Male ; *Skin/drug effects ; *Neurotoxins/pharmacology/administration &amp; dosage ; *Drug Delivery Systems ; *Pain/drug therapy ; Sodium Dodecyl Sulfate/chemistry ; }, abstract = {Clinically available therapies often inadequately address severe chronic cutaneous pain due to short anesthetic duration, insufficient intensity, or side effects. This study introduces a pen device delivering tetrodotoxin (TTX), a potent neurotoxin targeting nerve voltage-gated sodium channels, as a safe and effective topical anesthetic to treat severe chronic cutaneous pain. Chemical permeation enhancers, such as sodium dodecyl sulfate (SDS) and limonene (LIM), are incorporated to enhance TTX skin permeability. The device ensures precise TTX dosing down to the nanogram level, essential to avoid TTX overdose. In rats, the pen device treatment produces TTX-dose-dependent anesthetic effectiveness. An administration of 900 ng of TTX with SDS and LIM to the rat back skin produces a 393.25% increase (measurement limit) in the nociceptive skin pressure threshold, and the hypoalgesia lasts for 11.25 h, outperforming bupivacaine (28 µg), of which are 25.24% and under 1 h. Moreover, the pen device provides on-demand therapy for multiple treatments, consistently achieving prolonged anesthesia over ten sessions (1 treatment per day) without noted toxicity. Furthermore, a single topical administration of 16 µg of TTX exhibits no TTX-related toxicity in rats. The TTX delivery pen paves the way for clinical trials, offering a promising solution for severe cutaneous pain.}, }
@article {pmid39981403, year = {2025}, author = {Liao, W and Miao, Z and Liang, S and Zhang, L and Li, C}, title = {A composite improved attention convolutional network for motor imagery EEG classification.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1543508}, pmid = {39981403}, issn = {1662-4548}, abstract = {INTRODUCTION: A brain-computer interface (BCI) is an emerging technology that aims to establish a direct communication pathway between the human brain and external devices. Motor imagery electroencephalography (MI-EEG) signals are analyzed to infer users' intentions during motor imagery. These signals hold potential for applications in rehabilitation training and device control. However, the classification accuracy of MI-EEG signals remains a key challenge for the development of BCI technology.<br><br>METHODS: This paper proposes a composite improved attention convolutional network (CIACNet) for MI-EEG signals classification. CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.<br><br>RESULTS: The CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. These results indicate that the CIACNet model's classification performance exceeds that of four other comparative models.<br><br>CONCLUSION: Experimental results demonstrate that the proposed CIACNet model has strong classification capabilities and low time cost. Removing one or more blocks results in a decline in the overall performance of the model, indicating that each block within the model makes a significant contribution to its overall effectiveness. These results demonstrate the ability of the CIACNet model to reduce time costs and improve performance in motor imagery brain-computer interface (MI-BCI) systems, while also highlighting its practical applicability.}, }
@article {pmid39981127, year = {2025}, author = {Ghosh, S and Yadav, RK and Soni, S and Giri, S and Muthukrishnan, SP and Kumar, L and Bhasin, S and Roy, S}, title = {Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1532783}, pmid = {39981127}, issn = {1662-5161}, abstract = {Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.}, }
@article {pmid39980021, year = {2025}, author = {Angulo-Sherman, IN and León-Domínguez, U and Martinez-Torteya, A and Fragoso-González, GA and Martínez-Pérez, MV}, title = {Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {30}, pmid = {39980021}, issn = {1743-0003}, support = {Not applicable//Universidad de Monterrey/ ; Not applicable//Universidad de Monterrey/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; *Electroencephalography ; Adult ; *Functional Laterality/physiology ; Young Adult ; Beta Rhythm/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Cortical Synchronization/physiology ; }, abstract = {BACKGROUND: Motor imagery based brain-computer interfaces (MI-BCIs) are systems that detect the mental rehearsal of movement from brain activity signals (EEG) for controlling devices that can potentiate motor neurorehabilitation. Considering the problem that MI proficiency requires training and it is not always achieved, EEG desirable features should be investigated to propose indicators of successful MI training.<br><br>METHODS: Nine healthy right-handed subjects trained with a MI-BCI for four sessions. In each session, EEG was recorded for 30 trials that consisted of a rest and a dominant-hand MI sequence, which were used for calibrating the system. Then, the subject participated in 160 trials in which a cursor was displaced on a screen by performing MI or relaxing to hit a target. The session's accuracy was calculated. For each trial from the calibration phase of the first session, the power spectral density (PSD) and the partial directed coherence (PDC) of the rest and MI EEG segments were obtained to estimate the event-related synchronization changes (ERS) and the connectivity patterns of the &#x3b8; , &#x3b1; , &#x3b2; and &#x3b3; bands that are associated with high BCI control (accuracy above 70% in at least one session). Finally, t-tests and rank-sum tests (p < 0.05 , with Benjamini-Hochberg correction) were used to compare the ERS/ERD and PDC values of subjects with high and low accuracy, respectively.<br><br>RESULTS: Proficient users showed greater &#x3b1; ERD on the right-hand motor cortex (left hemisphere). Furthermore, the &#x3b2; PDC related to the ipsilateral motor cortex is commonly weakened during motor imagery, while the contralateral motor cortex &#x3b3; PDC is enhanced.<br><br>CONCLUSIONS: Motor imagery proficiency is related to the focused and lateralized event-related &#x3b1; desynchronization patterns and the lateralization of &#x3b2; and &#x3b3; PDC. Future analysis of these features could allow complimenting the information for assessment of subject-specific BCI control and the prediction of the effectiveness of motor-imagery training.}, }
@article {pmid39979463, year = {2025}, author = {Bhadra, K and Giraud, AL and Marchesotti, S}, title = {Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.}, journal = {Communications biology}, volume = {8}, number = {1}, pages = {271}, pmid = {39979463}, issn = {2399-3642}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Learning/physiology ; Imagination/physiology ; Brain/physiology ; }, abstract = {Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.}, }
@article {pmid39979351, year = {2025}, author = {Gupta, E and Sivakumar, R}, title = {Response coupling with an auxiliary neural signal for enhancing brain signal detection.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {6227}, pmid = {39979351}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Brain/physiology ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Algorithms ; Young Adult ; }, abstract = {Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling, aimed at enhancing brain signal detection and reliability by pairing a brain signal with an artificially induced auxiliary signal and leveraging their interaction. Specifically, we use error-related potentials (ErrPs) as the primary signal and steady-state visual evoked potentials (SSVEPs) as the auxiliary signal. SSVEPs, known for their phase-locked responses to rhythmic stimuli, are selected because rhythmic neural activity plays a critical role in sensory and cognitive processes, with evidence suggesting that reinforcing these oscillations can improve neural performance. By exploring the interaction between these two signals, we demonstrate that response coupling significantly improves the detection accuracy of ErrPs, especially in the parietal and occipital regions. This method introduces a new paradigm for enhancing BCI performance, where the interaction between a primary and an auxiliary signal is harnessed to enhance the detection performance. Additionally, the phase-locking properties of SSVEPs allow for unsupervised rejection of suboptimal data, further increasing BCI reliability.}, }
@article {pmid39979293, year = {2025}, author = {Hoeferlin, GF and Grabinski, SE and Druschel, LN and Duncan, JL and Burkhart, G and Weagraff, GR and Lee, AH and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Coleman, J and Li, L and Memberg, W and Sweet, J and Hamedani, HA and Acharya, AP and Hernandez-Reynoso, AG and Donskey, C and Jaskiw, G and Ricky Chan, E and Shoffstall, AJ and Bolu Ajiboye, A and von Recum, HA and Zhang, L and Capadona, JR}, title = {Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {1829}, pmid = {39979293}, issn = {2041-1723}, support = {R01 NS131502/NS/NINDS NIH HHS/United States ; R25 CA221718/CA/NCI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; }, mesh = {Animals ; *Microelectrodes ; Mice ; *Electrodes, Implanted/adverse effects ; *Brain-Computer Interfaces ; *Brain ; *Blood-Brain Barrier ; *Mice, Inbred C57BL ; Gastrointestinal Microbiome ; Male ; Brain-Gut Axis/physiology ; Bacteria ; Anti-Bacterial Agents/pharmacology ; }, abstract = {Brain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the brains of mice with implanted microelectrodes. These sequences changed over time. Mice treated with antibiotics showed a reduced presence of these bacteria and had a different inflammatory response, which temporarily improved microelectrode recording performance. However, long-term antibiotic use worsened performance and disrupted neurodegenerative pathways. Many bacterial sequences found were not present in the gut or in unimplanted brains. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.}, }
@article {pmid39978072, year = {2025}, author = {Lim, MJR and Lo, JYT and Tan, YY and Lin, HY and Wang, Y and Tan, D and Wang, E and Naing Ma, YY and Wei Ng, JJ and Jefree, RA and Tseng Tsai, Y}, title = {The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adb88e}, pmid = {39978072}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electrodes, Implanted/trends ; }, abstract = {Objective.Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe challenges and opportunities in the iBCI field.Approach.Medline, EMBASE, PubMed and Cochrane databases were searched from inception until 13 April 2024. Primary studies reporting the use of iBCI in human subjects to restore function were included. Endpoints extracted include iBCI electrode type, iBCI implantation, decoder algorithm, iBCI effector, testing and training methodology and functional outcomes. Narrative synthesis of outcomes was done with a focus on hardware and software development trends over time. Individual patient data (IPD) was also collected and an IPD meta-analysis was done to identify factors significant to iBCI performance.Main results.93 studies involving 214 patients were included in this systematic review. The median task performance accuracy for cursor control tasks was 76.00% (Interquartile range [IQR] = 21.2), for motor tasks was 80.00% (IQR = 23.3), and for communication tasks was 93.27% (IQR = 15.3). Current advances in iBCI software include use of recurrent neural network architectures as decoders, while hardware advances such as intravascular stentrodes provide a less invasive alternative for neural recording. Challenges include the lack of standardized testing paradigms for specific functional outcomes and issues with portability and chronicity limiting iBCI usage to laboratory settings.Significance.Our systematic review demonstrated the exponential rate at which iBCIs have evolved over the past two decades. Yet, more work is needed for widespread clinical adoption and translation to long-term home-use.}, }
@article {pmid39976033, year = {2025}, author = {Li, L and Li, B and Wang, G and Li, S and Li, X and Santos, J and González, AM and Guo, L and Tu, Y and Qin, Y}, title = {Research on Precision Medicine AI Algorithm for Neuro Immune Gastrointestinal Diseases based on Quantum Biochemistry and Computational Cancer Genetics.}, journal = {Current pharmaceutical biotechnology}, volume = {}, number = {}, pages = {}, doi = {10.2174/0113892010348489241210060447}, pmid = {39976033}, issn = {1873-4316}, abstract = {OBJECTIVE: The objective of this study is to conduct network toxicology analysis based on smoking habits and develop a simpler and more effective toxicology product ingestion control system.<br><br>BACKGROUND: Smoking behavior can affect the pathogenesis and prognosis of neuroimmune gastrointestinal diseases.<br><br>AIMS: The purpose of developing tools to assist clinical practice is to avoid the harm of cigarettes to the human body.<br><br>METHODS: Molecular dynamics method was used to elucidate the biophysical mechanism of TP53 gene mutation caused by harmful ingredients, and the signaling pathway of midbrain edge excitation was determined by molecular dynamics of nicotine and dopamine receptor D3. The possible involvement of nicotine in neuronal damage was determined through the molecular interaction between nicotine and ACHE. Molecular pathways were analyzed based on the aforementioned biological principles, developed artificial intelligence systems and brain computer interface systems.<br><br>RESULTS: Several signaling pathways were elucidated, and effective AI algorithms were developed.<br><br>CONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.}, }
@article {pmid39975237, year = {2025}, author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C}, title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39975237}, issn = {2692-8205}, support = {R01 NS105132/NS/NINDS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; }, abstract = {Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.}, }
@article {pmid39974888, year = {2025}, author = {Letner, JG and Lam, JLW and Copenhaver, MG and Barrow, M and Patel, PR and Richie, JM and Lee, J and Kim, HS and Cai, D and Weiland, JD and Phillips, J and Blaauw, D and Chestek, CA}, title = {A method for efficient, rapid, and minimally invasive implantation of individual non-functional motes with penetrating subcellular-diameter carbon fiber electrodes into rat cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39974888}, issn = {2692-8205}, support = {R01 NS118606/NS/NINDS NIH HHS/United States ; RF1 MH120005/MH/NIMH NIH HHS/United States ; RF1 NS128667/NS/NINDS NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; }, abstract = {OBJECTIVE: Distributed arrays of wireless neural interfacing chips with 1-2 channels each, known as "neural dust", could enhance brain machine interfaces (BMIs) by removing the wired connection through the scalp and increasing biocompatibility with their submillimeter size. Although several approaches for neural dust have emerged, a procedure for implanting them in batches that builds upon the safety and performance of currently used electrodes remains to be demonstrated.<br><br>APPROACH: Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface with carbon fiber electrodes of subcellular diameter (6.8-8.4 &#x3bc;m) that penetrate to a target brain depth of 1 mm without insertion aids. To simulate their implantation, we assembled more than 230 mechanically equivalent motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted mote grids of multiple configurations into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface using photos and videos captured during their implantation.<br><br>MAIN RESULTS: When placing motes onto the insertion device, we found that they aggregated in molten PEG such that the array pitch was only 5% wider than the dimensions of the mote bases themselves (240 &#xd7; 240 &#x3bc;m). Overall, we found that motes with this arrangement could be inserted into rat cortex with a high success rate, as 171/186 (92%) motes in 4&#xd7;4 (N=4) and 5&#xd7;5 (N=5) square grid configurations were successfully inserted using the insertion device alone. After implantation, measurements of how much motes tilted (22&#xb1;9&#xb0;, X&#x304;&#xb1;S) and had been displaced relative to their original positions were smaller than those measured for devices implanted inside the brain in the literature.<br><br>SIGNIFICANCE: Collectively, these data establish the viability of safely implementing motes with ultrasmall electrodes and epicortically-situated chips for use in future BMIs.}, }
@article {pmid39973870, year = {2025}, author = {Kanagaluru, V and M, S}, title = {Artificial intelligence based BCI using SSVEP signals with single channel EEG.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {}, number = {}, pages = {9287329241302740}, doi = {10.1177/09287329241302740}, pmid = {39973870}, issn = {1878-7401}, abstract = {BACKGROUND: Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.<br><br>OBJECTIVE: The aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.<br><br>METHODS: SSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.<br><br>RESULTS: The proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.<br><br>CONCLUSION: This study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.}, }
@article {pmid39970032, year = {2025}, author = {M, AL and R, R}, title = {Comprehensive analysis of prefrontal cortex-directional rhythms categorization for rehabilitation.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2025.2467460}, pmid = {39970032}, issn = {1476-8259}, abstract = {Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.}, }
@article {pmid39969013, year = {2025}, author = {Magalhães, SS and Lucas-Ochoa, AM and Gonzalez-Cuello, AM and Fernández-Villalba, E and Pereira Toralles, MB and Herrero, MT}, title = {The mind-machine connection: adaptive information processing and new technologies promoting mental health in older adults.}, journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry}, volume = {}, number = {}, pages = {10738584251318948}, doi = {10.1177/10738584251318948}, pmid = {39969013}, issn = {1089-4098}, abstract = {The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.}, }
@article {pmid39968680, year = {2025}, author = {Hao, W and Yang, S and Sheng, Y and Ye, C and Han, L and Zhou, Z and Cui, W}, title = {Efficient expression of recombinant proteins in Bacillus subtilis using a rewired gene circuit of quorum sensing.}, journal = {Biotechnology progress}, volume = {}, number = {}, pages = {e70007}, doi = {10.1002/btpr.70007}, pmid = {39968680}, issn = {1520-6033}, support = {32171420//National Natural Science Foundation of China/ ; KLIB-KF202307//Open Project of Key Laboratory of Industrial Biotechnology, Ministry of Education/ ; 2023YFC3402402//National Key Research and Development Program of China/ ; }, abstract = {Bacillus subtilis is a favored chassis for high productivity of several high value-added product in synthetic biology. Efficient production of recombinant proteins is critical but challenging using this chassis because these expression systems in use, such as constitutive and inducible expression systems, demand for coordination of cell growth with production and addition of chemical inducers. These systems compete for intracellular resources with the host, eventually resulting in dysfunction of cell survival. To overcome the problem, in this study, LuxRI quorum sensing (QS) system from Aliivibrio fischeri was functionally reconstituted in B. subtilis for achieving coordinated protein overproduction with cell growth in a cell-density-dependent manner. Furthermore, the output-controlling promoter, PluxI, was engineered through two rounds of evolution, by which we identified four mutants, P22, P47, P56, and P58 that exhibited elevated activity compared to the original PluxI. By incorporating a strong terminator (TB5) downstream of the target gene further enhanced expression level. The expression level of this system surpasses commonly used promoter-based systems in B. subtilis like P43 and PylbP. The LuxRI QS system proves to be a potent platform for recombinant protein overproduction in B. subtilis.}, }
@article {pmid39965558, year = {2025}, author = {Persson, AC and Eeg-Olofsson, M and Sadeghi, A and Lepp, M}, title = {Patients' Experiences of an Active Transcutaneous Implant: The Bone Conduction Implant.}, journal = {Audiology &amp; neuro-otology}, volume = {}, number = {}, pages = {1-12}, doi = {10.1159/000544774}, pmid = {39965558}, issn = {1421-9700}, abstract = {INTRODUCTION: The aim of this qualitative study was to explore and describe patients' experiences of using and living with the bone conduction implant (BCI).<br><br>METHODS: Semi-structured interviews were conducted with 10 BCI users and analyzed according to the phenomenographic approach.<br><br>RESULTS: Four conceptual themes were formed during the analysis; (1) conceptions of the process receiving the BCI, (2) conceptions of handling the BCI on a daily basis, (3) conceptions of hearing with the BCI, and (4) conceptions of health care issues related to the BCI. The participants' statements include experiences of improved hearing and self-esteem by using the BCI. Noisy situations and not being able to hear in daily life situations causes frustrations. The participants described anxiety about consequences following an MRI examination. The audio processor is easy to handle but the fact that it is not waterproof raise concerns. Despite some frustration and concerns, participants state that the audio processor has become a part of them, and they cannot imagine being without it.<br><br>CONCLUSION: The ability to hear and communicate with other people has a great impact on the participants' daily life quality, and their statements show the importance hearing has on their lives and how they perceive themselves. The BCI seems to be a good hearing rehabilitation alternative for the participants, and they state that the audio processor is easy to use and handle.}, }
@article {pmid39963381, year = {2025}, author = {Wang, N and He, Y and Zhu, S and Liu, D and Chai, X and He, Q and Cao, T and He, J and Li, J and Si, J and Yang, Y and Zhao, J}, title = {Functional near-infrared spectroscopy for the assessment and treatment of patients with disorders of consciousness.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1524806}, pmid = {39963381}, issn = {1664-2295}, abstract = {BACKGROUND: Advances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.<br><br>AIM: This review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.<br><br>METHODS: A systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.<br><br>RESULTS: Analysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).<br><br>CONCLUSION: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.}, }
@article {pmid39957485, year = {2025}, author = {Yi, L and Jiang, T and Ren, R and Cao, J and Edel, JB and Ivanov, AP and Tang, L}, title = {Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {}, number = {}, pages = {e202501941}, doi = {10.1002/anie.202501941}, pmid = {39957485}, issn = {1521-3773}, support = {21874119//National Natural Science Foundation of China/ ; 62127818//National Natural Science Foundation of China/ ; LR22F050003//Zhejiang Provincial Outstanding Youth Science Foundation/ ; 724300//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 875525//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 600322/05//Analytical Chemistry Trust Fund grant/ ; EP/V049070/1//Engineering and Physical Sciences Research Council/ ; }, abstract = {Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN)6 [3-/4-]) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN)6 [3-/4-] concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.}, }
@article {pmid39957341, year = {2025}, author = {Zhai, H and Li, P and Wang, H and Wang, X}, title = {Temperature and steric hindrance-regulated selective synthesis of ketamine derivatives and 2-aryl-cycloketone-1-carboxamides via nucleophilic substitution and Favorskii rearrangement.}, journal = {Organic &amp; biomolecular chemistry}, volume = {23}, number = {11}, pages = {2704-2711}, doi = {10.1039/d4ob02039a}, pmid = {39957341}, issn = {1477-0539}, abstract = {A selective temperature and steric hindrance-regulated method for nucleophilic substitution or Favorskii rearrangement reactions of 2-aryl-2-bromo-cycloketones with aliphatic amines has been developed to prepare ketamine derivatives and 2-aryl-cycloketone-1-carboxamides. In the presence of secondary amines or ortho-substituted 2-aryl-2-bromocycloketones, steric hindrance directs the Favorskii rearrangement to occur. Conversely, with primary amines, the product ratio of nucleophilic substitution to Favorskii rearrangement is temperature-dependent, with higher temperatures favoring the Favorskii rearrangement. At lower temperatures (-25 °C or below), nucleophilic substitution predominates, yielding ketamine derivatives in yields of 60% to 85%. This method effectively utilizes temperature and steric hindrance to control the reaction pathway and optimize product formation.}, }
@article {pmid39957214, year = {2025}, author = {Chinta, B and Pampana, M and M, M}, title = {An efficient deep learning approach for automatic speech recognition using EEG signals.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-21}, doi = {10.1080/10255842.2025.2456982}, pmid = {39957214}, issn = {1476-8259}, abstract = {Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.}, }
@article {pmid39956854, year = {2025}, author = {Zou, W and Fan, Y and Liu, J and Cheng, H and Hong, H and Al-Sheikh, U and Li, S and Zhu, L and Li, R and He, L and Tang, YQ and Zhao, G and Zhang, Y and Wang, F and Zhan, R and Zheng, X and Kang, L}, title = {Anoctamin-1 is a core component of a mechanosensory anion channel complex in C. elegans.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {1680}, pmid = {39956854}, issn = {2041-1723}, support = {P40 OD010440/OD/NIH HHS/United States ; 31771113//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31471023//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ22C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 2021ZD0203303//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; }, mesh = {Animals ; *Caenorhabditis elegans/metabolism/genetics ; *Mechanotransduction, Cellular ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Anoctamin-1/metabolism/genetics ; Humans ; Touch/physiology ; Neurons/metabolism ; Chloride Channels/metabolism/genetics ; }, abstract = {Mechanotransduction channels are widely expressed in both vertebrates and invertebrates, mediating various physiological processes such as touch, hearing and blood-pressure sensing. While previously known mechanotransduction channels in metazoans are primarily cation-selective, we identified Anoctamin-1 (ANOH-1), the C. elegans homolog of mammalian calcium-activated chloride channel ANO1/TMEM16A, as an essential component of a mechanosensory channel complex that contributes to the nose touch mechanosensation in C. elegans. Ectopic expression of either C. elegans or human Anoctamin-1 confers mechanosensitivity to touch-insensitive neurons, suggesting a cell-autonomous role of ANOH-1/ANO1 in mechanotransduction. Additionally, we demonstrated that the mechanosensory function of ANOH-1/ANO1 relies on CIB (calcium- and integrin- binding) proteins. Thus, our results reveal an evolutionarily conserved chloride channel involved in mechanosensory transduction in metazoans, highlighting the importance of anion channels in mechanosensory processes.}, }
@article {pmid39954182, year = {2025}, author = {Jangir, G and Joshi, N and Purohit, G}, title = {Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach.}, journal = {Brain informatics}, volume = {12}, number = {1}, pages = {5}, pmid = {39954182}, issn = {2198-4018}, abstract = {Human brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is, statistically analyzed to understand the nature of data and outliers in it. Effectiveness of pre-processing algorithm is then visualized. The cleaned dataset has dual polarity and gaussian distribution nature which makes Tanh activation function suitable for the neural network BC4D4 model. BC4D4 uses Convolutional neural network for feature extraction, dense neural network for pattern identification and incorporating dropout &amp; regularization making the proposed model more resilient. Our model outperforms the state of the art work on the dataset 4 achieving 0.85 correlation value that is 1.85X (Winner of BCI competition 4, 2012) &amp; 1.25X (Finger Flex model, 2022).}, }
@article {pmid39953165, year = {2025}, author = {Jordan, S and Buchmann, M and Loss, J and Okan, O}, title = {[Health literacy and health behaviour-insights into a developing field of research and action for public health].}, journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz}, volume = {68}, number = {3}, pages = {255-263}, pmid = {39953165}, issn = {1437-1588}, mesh = {*Health Literacy ; Germany ; Humans ; *Health Behavior ; *Public Health ; *Health Promotion/methods ; Health Services Research ; Exercise/psychology ; }, abstract = {The research and action field of health literacy and health behaviour is increasingly differentiating. General health literacy is established and focuses on population-based studies. Specific health literacy for health behaviour offers topic-related starting points for interventions and public health strategies.There are various concepts, definitions and measurement instruments for general health literacy and specific health literacy in the areas of nutrition and physical activity. These differ in terms of the levels of action and areas of application of health literacy.Most studies show a positive association between health literacy and various health behaviours. Higher health literacy is more often associated with improved health-promoting behaviour. This applies to both general as well as specific health literacy regarding nutrition and exercise (physical activity). Some studies found no correlation for certain behaviours, while others only found correlations for certain groups, which may be due to the different measuring instruments and research contexts. This points to the importance of always considering the interaction between behaviour and circumstances in order to improve the fit between the individual and the everyday demands of dealing with health information.The behavioural and cultural insights (BCI) approach can provide insights into how to promote health literacy with regard to various health behaviours, individual barriers and facilitators that arise from life circumstances and conditions, and that take social practice into account. BCI and health literacy complement each other and have the potential to make strategies for improving health behaviour more effective and targeted.}, }
@article {pmid39950750, year = {2024}, author = {Jiang, E and Huang, T and Yin, X}, title = {A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.}, journal = {Journal of medical engineering &amp; technology}, volume = {48}, number = {7}, pages = {262-275}, doi = {10.1080/03091902.2025.2463577}, pmid = {39950750}, issn = {1464-522X}, mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; *Fuzzy Logic ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Algorithms ; Bayes Theorem ; }, abstract = {Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.}, }
@article {pmid39949790, year = {2025}, author = {Ding, F and Ying, Y and Jin, Y and Guo, X and Xu, Y and Yu, Z and Jiang, H}, title = {Reduced frontotemporal connectivity during a verbal fluency task in patients with anxiety, sleep, and major depressive disorders.}, journal = {Frontiers in neurology}, volume = {16}, number = {}, pages = {1542346}, pmid = {39949790}, issn = {1664-2295}, abstract = {BACKGROUND: It has been well established that psychiatric disorders are often accompanied by cognitive dysfunction. Previous studies have investigated the verbal fluency task (VFT) for detecting executive function impairment in different psychiatric disorders, but the sensitivity and specificity of this task in different psychiatric disorders have not been explored. Furthermore, clarifying the mechanisms underlying variations in executive function impairments across multiple psychiatric disorders will enhance our comprehension of brain activity alternations among these disorders. Therefore, this study combined the VFT and the functional near-infrared spectroscopy (fNIRS) to investigate the neural mechanisms underlying the impairment of executive function across psychiatric disorders including anxiety disorder (AD), sleep disorder (SD) and major depressive disorder (MDD).<br><br>METHODS: Two hundred and eight participants were enrolled including 52 AD, 52 SD, 52 MDD and 52 healthy controls (HCs). All participants completed the VFT while being monitored using fNIRS to measure changes in brain oxygenated hemoglobin (Oxy-Hb).<br><br>RESULTS: Our results demonstrated that MDD, AD and SD exhibited decreased overall connectivity strength, as well as reduced connected networks involving the frontal and temporal regions during the VFT comparing to HC. Furthermore, the MDD group showed a reduction in connected networks, specifically in the left superior temporal gyrus and precentral gyrus, compared to the AD group.<br><br>CONCLUSION: Our study offers neural evidence that the VFT combined with fNIRS could effectively detect executive function impairment in different psychiatric disorders.}, }
@article {pmid39948616, year = {2025}, author = {Gedela, NSS and Radawiec, RD and Salim, S and Richie, J and Chestek, C and Draelos, A and Pelled, G}, title = {In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.}, journal = {Bioelectronic medicine}, volume = {11}, number = {1}, pages = {4}, pmid = {39948616}, issn = {2332-8886}, abstract = {The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.}, }
@article {pmid39947717, year = {2025}, author = {Huang-Fu, HQ and Wang, L and Karmacharya, B and Koirala, UK and Ke, CW and Liang, D and Hao, C and Lai, YS}, title = {Spatial profiling of geographical accessibility to maternal healthcare and coverage of maternal health service utilisation in Nepal: a geospatial analysis based on demographic and health survey.}, journal = {BMJ global health}, volume = {10}, number = {2}, pages = {}, pmid = {39947717}, issn = {2059-7908}, mesh = {Humans ; Nepal ; *Health Services Accessibility/statistics &amp; numerical data ; *Maternal Health Services/statistics &amp; numerical data ; Female ; Pregnancy ; *Spatial Analysis ; Adult ; Bayes Theorem ; Health Surveys ; Patient Acceptance of Health Care/statistics &amp; numerical data ; Prenatal Care/statistics &amp; numerical data ; Young Adult ; Middle Aged ; Adolescent ; }, abstract = {BACKGROUND: Information on geographical accessibility to maternal healthcare (MHC) and coverage of maternal health service utilisation at high spatial resolution in Nepal are important for evidence-based health planning.<br><br>METHODS: Based on the Nepal Health Facility Registry dataset in 2022, we measured the geographical accessibility to MHC facilities across Nepal. Using data from 2022 Nepal Demographic and Health Survey and other sources, we assessed the relationships between geographical accessibility and the utilisation of the three major healthcare services (ie, four or more antenatal care (ANC) visits, institutional delivery and postnatal care (PNC) check-up), by applying Bayesian geostatistical models. High-resolution maps on coverage of the above services were produced.<br><br>RESULTS: The geographical accessibility showed high in the central and southern Terai belt but low in the northern mountains, with average travel-mode adjusted travel time for ANC, institutional delivery and PNC 26.74, 40.72 and 29.09&#x2009;min, respectively. Negative correlations were found between geographical accessibility with four or more ANC visits (OR 0.76, 95% Bayesian credible interval, BCI 0.65 to 0.90), institutional delivery (OR 0.76, 95% BCI 0.64 to 0.90) and PNC check-up (OR 0.87, 95% BCI 0.76 to 0.99), respectively. Population-weighted coverages for four or more ANC visits, institutional delivery and PNC check-up were estimated 83.25% (95% BCI 80.43% to 85.35%), 84.26% (95% BCI 81.30% to 86.08%) and 73.19% (95% BCI 69.43% to 76.09%), respectively, across Nepal. The northern mountains and southeastern Terai showed low coverage for the three healthcare services, while the central, eastern and western hilly regions exhibited good coverage.<br><br>CONCLUSION: Geographical accessibility is important in utilisation of maternal health services in Nepal. The high-resolution maps enable an evidence-based assessment for better health planning.}, }
@article {pmid39947551, year = {2025}, author = {Lyu, Y and Yu, L and Qi, L and Meng, J and Wang, Y and Liu, Y and Xue, T and Zhi, C}, title = {Construction of 3D-fabric-based triple-decker agar/sodium alginate/Ca[2+] dual-network composite for wound dressing.}, journal = {International journal of biological macromolecules}, volume = {304}, number = {Pt 2}, pages = {140883}, doi = {10.1016/j.ijbiomac.2025.140883}, pmid = {39947551}, issn = {1879-0003}, mesh = {*Alginates/chemistry ; *Bandages ; *Wound Healing/drug effects ; *Agar/chemistry ; *Calcium/chemistry ; Animals ; *Anti-Bacterial Agents/pharmacology/chemistry ; *Staphylococcus aureus/drug effects ; Escherichia coli/drug effects ; Nanofibers/chemistry ; Mice ; }, abstract = {This study designed a novel multifunctional Janus structure dressing (DNCD dressing) composed of spacer fabric, agar/sodium alginate/calcium ion dual-network aerogel, methylene blue, and AgNO3-added thermoplastic polyurethane nanofiber membrane. The unidirectional liquid transport and absorbency tests prove that the DNCD dressing can unidirectionally transport liquids within just two seconds and possesses a liquid absorption ratio of 875.3 %. The unique open structure formed by the spacer fabric and liquid transport channels provides excellent air permeability as well as a suitable water vapor transmission rate, reaching 584.96 mm/s, 10.3 L/min, and 1104.82 g/m[2]/24 h, respectively. The exceptional compressive strength (216.78 kPa) and compressive modulus (515.23 kPa) of the dressing can provide protection for the wound. Antibacterial tests demonstrate that the silver ion-added DNCD dressing can eradicate >99 % of Escherichia coli and Staphylococcus aureus, while the added methylene blue can effectively monitor the survival status of bacteria. The low BCI value and the hemolysis ratio of <5 % indicate that the DNCD dressing has a certain hemostatic ability and does not cause hemolysis. The results of cytotoxicity tests and full-thickness skin defect models show that the DNCD dressing has good cytocompatibility and the potential to promote wound healing.}, }
@article {pmid39947091, year = {2025}, author = {Zhao, H and Xu, S}, title = {Associations between panic buying and choice overload during the public health crisis in China: Testing sequential mediation models.}, journal = {Acta psychologica}, volume = {254}, number = {}, pages = {104800}, doi = {10.1016/j.actpsy.2025.104800}, pmid = {39947091}, issn = {1873-6297}, mesh = {Humans ; China/epidemiology ; *COVID-19/psychology/epidemiology ; Adult ; Male ; Female ; *Choice Behavior/physiology ; *Consumer Behavior ; Cross-Sectional Studies ; Panic ; Young Adult ; Middle Aged ; Motivation ; Surveys and Questionnaires ; Public Health ; Adolescent ; Fear ; }, abstract = {Using the COVID-19 pandemic in China as the background, the current study investigated the association between panic buying behavior and consumers' choice overload during the public health crisis, and provided the empirical evidence on the dual sequential mediating pathways from the theoretical perspective of Protection Motivation Theory and Compensatory Control Theory. Through the cross-sectional online anonymous survey method, 492 samples were collected during the COVID-19 pandemic in China when the lockdown measure and the static management were implemented. Our results identified that the fear of being infected and the perceived threat sequentially mediated the effect of the panic buying on the choice overload, and the fear of being infected and the perceived control sequentially mediated the effect of the panic buying on the choice overload during the public health crisis. Machine learning algorithms also further identified the predictive effect of all feature variables on the choice overload during the public health crisis. Our findings provided a new perspective on the understanding of consumers' behavior during the public health crisis, and further extended the application of the Protection Motivation Theory and the Compensatory Control Theory to the consumer behavior research.}, }
@article {pmid39947044, year = {2025}, author = {Zhu, L and Xin, Y and Yang, Y and Kong, W}, title = {A multi-layer EEG fusion decoding method with channel selection for multi-brain motor imagery.}, journal = {Computer methods and programs in biomedicine}, volume = {262}, number = {}, pages = {108595}, doi = {10.1016/j.cmpb.2025.108595}, pmid = {39947044}, issn = {1872-7565}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Brain/physiology ; Algorithms ; Discriminant Analysis ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain Mapping/methods ; }, abstract = {Traditional motor imagery-based single-brain computer interfaces(BCIs) face inherent limitations, such as unstable signals and low recognition accuracy. In contrast, multi-brain BCIs offer a promising solution by leveraging group electroencephalography (EEG) data. This paper presents a novel multi-layer EEG fusion method with channel selection for motor imagery-based multi-brain BCIs. We utilize mutual information convergent cross-mapping (MCCM) to identify channels that the represent causal relationships between brains; this strategy is combined with multiple linear discriminant analysis (MLDA) for decoding intentions via both data-layer and decision-layer strategies. Our experimental results demonstrate that the proposed method improves the accuracy of multi-brain motor imagery decoding by approximately 10% over that of the traditional methods, with a further 3%-5% accuracy increase due to the effective channel selection mechanism.}, }
@article {pmid39946849, year = {2025}, author = {Zhou, Z and Hu, Z and Lyu, H}, title = {A 0.53-μW/channel calibration-free spike detection IC with 98.8-%-accuracy based on stationary wavelet transforms and Teager energy operators.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adb5c4}, pmid = {39946849}, issn = {1741-2552}, mesh = {*Wavelet Analysis ; *Action Potentials/physiology ; Humans ; *Neurons/physiology ; Brain-Computer Interfaces ; Calibration ; Algorithms ; Reproducibility of Results ; }, abstract = {Objective. The brain-computer interface is currently experiencing a surge in the number of recording channels, resulting in a vast amount of raw data. It has become crucial to reliably detect neural spikes from a large population of neurons in the presence of noise, in order to constrain the transmission bandwidth.Approach. We investigate various time-frequency analysis methods for spike detection, followed by an exploration of energy operators amplifying spikes and signal statistics for adaptive thresholding. Subsequently, we introduce a precise and computationally efficient spike detection module, leveraging stationary wavelet transform (SWT), Teager energy operator, and root-mean-square calculator. This module is capable of autonomously adapting to different levels of noise. The SWT effectively eliminates high-frequency noise, enhancing the performance of the energy operators. The hardware computational process is simplified through the use of the lifting scheme and a channel-interleaving architecture.Main results. We evaluate the proposed spike detector with adaptive threshold on the publicly available WaveClus datasets. The detector achieves an average accuracy of 98.84%. The application-specific integrated circuit (ASIC) implementation results of the spike detector demonstrate an optimized interleaving channel of 8. In a 65 nm technology, the 8-channel spike detector consumes a power of 0.532μW Ch[-1]and occupies an area of 0.00645 mm[2]Ch[-1], operating at a 1.2 V supply voltage.Significance. The proposed spike detection processor offers one of the highest accuracies among state-of-the-art spike detection methods. Importantly, the ASIC explores the considerations in the scalability and hardware costs. The proposed design provides a systematic solution on spike detection with adaptive thresholding, offering a high accuracy while maintaining low power and area consumptions.}, }
@article {pmid39946786, year = {2025}, author = {Dutta, S and Goswami, S and Debnath, S and Adhikary, S and Majumder, A}, title = {MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment.}, journal = {Computers in biology and medicine}, volume = {188}, number = {}, pages = {109795}, doi = {10.1016/j.compbiomed.2025.109795}, pmid = {39946786}, issn = {1879-0534}, mesh = {Humans ; *Music ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; Male ; Female ; Adult ; Image Processing, Computer-Assisted/methods ; Brain-Computer Interfaces ; }, abstract = {Functional magnetic resonance imaging is a popular non-invasive brain-computer interfacing technique to monitor brain activities corresponding to several physical or neurological responses by measuring blood flow changes at different brain parts. Recent studies have shown that blood flow within the brain can have signature activity patterns in response to various musical genres. However, limited studies exist in the state of the art for automatized recognition of the musical genres from functional magnetic resonance imaging. This is because the feasibility of obtaining these kinds of data is limited, and currently available open-sourced data is insufficient to build an accurate deep-learning model. To solve this, we propose a prototypical model agnostic meta-learning framework for accurately classifying musical genres by studying blood flow dynamics using functional magnetic resonance imaging. A test with open-sourced data collected from 20 human subjects with consent for 6 different mental states resulted in up to 97.25 ± 1.38% accuracy by training with only 30 samples surpassing state-of-the-art methods. Further, a detailed evaluation of the performances confirms the model's reliability.}, }
@article {pmid39946752, year = {2025}, author = {Gherman, DE and Krol, LR and Klug, M and Zander, TO}, title = {An investigation of a passive BCI's performance for different body postures and presentation modalities.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {2}, pages = {}, doi = {10.1088/2057-1976/adb58b}, pmid = {39946752}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Posture ; Male ; Female ; Adult ; *Virtual Reality ; Young Adult ; Brain/physiology ; User-Computer Interface ; Movement ; Artifacts ; }, abstract = {Passive brain-computer interfaces (passive BCIs, pBCIs) enable computers to unobtrusively decipher aspects of a user's mental state in real time from recordings of brain activity, e.g. electroencephalography (EEG). When used during human-computer interaction (HCI), this allows a computer to dynamically adapt for enhancing the subjective user experience. For transitioning from controlled laboratory environments to practical applications, understanding BCI performance in real contexts is of utmost importance. Here, Virtual Reality (VR) can play a unique role: both as a fully controllable simulation of a realistic environment and as an independent, increasingly popular real application. Given the potential of VR as a dynamic and controllable environment, and the capability of pBCIs to enable novel modes of interaction, it is tempting to envision a future where pBCI and VR are seamlessly integrated. However, the simultaneous use of these two technologies-both of which are head-mounted-presents new challenges. Due to their immediate proximity, electromagnetic artifacts can arise, contaminating the EEG. Furthermore, the active movements promoted by VR can induce mechanical and muscular artifacts in the EEG. The varying body postures and display preferences of users further complicate the practical application of pBCIs. To address these challenges, the current study investigates the influence of body posture (sitting Versus standing) and display media (computer screen Versus VR) on the performance of a pBCI in assessing cognitive load. Our results show that these conditions indeed led to some changes in the EEG data; nevertheless, the ability of pBCIs to detect cognitive load remained largely unaffected. However, when a classifier trained in one context (body posture or modality) was applied to another (e.g., cross-task application), reductions in classification accuracy were observed. As HCI moves towards increasingly adaptive and more interactive designs, these findings support the expansive potential of pBCIs in VR contexts.}, }
@article {pmid39945188, year = {2025}, author = {Zhang, R and Sui, L and Shen, C and Xu, L and Cao, J}, title = {EEG-based real-time BCI system using drones for attention visualization.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-9}, doi = {10.1080/10255842.2025.2459272}, pmid = {39945188}, issn = {1476-8259}, abstract = {Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.}, }
@article {pmid39943246, year = {2025}, author = {Zare, S and Beaber, SI and Sun, Y}, title = {NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {3}, pages = {}, pmid = {39943246}, issn = {1424-8220}, support = {2135620//National Science Foundation/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Brain-Computer Interfaces ; Robotics/instrumentation/methods ; Movement/physiology ; Hand Strength/physiology ; Feasibility Studies ; Male ; Stroke Rehabilitation/methods/instrumentation ; Imagination/physiology ; }, abstract = {Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person's ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove's soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient's attempted movements using pure thinking through a non-intrusive brain-computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings.}, }
@article {pmid39941230, year = {2025}, author = {Alshehri, H and Al-Nafjan, A and Aldayel, M}, title = {Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain-Computer Interfaces.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {15}, number = {3}, pages = {}, pmid = {39941230}, issn = {2075-4418}, support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority 611 (RDIA) - Kingdom of Saudi Arabia/ ; }, abstract = {Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain-computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on pain detection based on electroencephalography (EEG) signals. It presents the findings, methodologies, and advancements reported in 20 peer-reviewed articles that utilize machine learning and deep learning (DL) approaches for EEG-based pain detection. We analyze various ML and DL techniques, support vector machines, random forests, k-nearest neighbors, and convolution neural network recurrent neural networks and transformers, and their effectiveness in decoding pain neural signals. The motivation for combining AI with BCI technology lies in the potential for significant advancements in the real-time responsiveness and adaptability of these systems. We reveal that DL techniques effectively analyze EEG signals and recognize pain-related patterns. Moreover, we discuss advancements and challenges associated with EEG-based pain detection, focusing on BCI applications in clinical settings and functional requirements for effective pain classification systems. By evaluating the current research landscape, we identify gaps and opportunities for future research to provide valuable insights for researchers and practitioners.}, }
@article {pmid39937921, year = {2025}, author = {Qiu, S and Tang, Y and Yu, H and Xie, H and Dreher, JC and Hu, Y and Zhou, X}, title = {Toward a computational understanding of bribe-taking behavior.}, journal = {Annals of the New York Academy of Sciences}, volume = {1545}, number = {1}, pages = {5-15}, doi = {10.1111/nyas.15294}, pmid = {39937921}, issn = {1749-6632}, support = {32200853//National Natural Science Foundation of China/ ; 71942001//National Natural Science Foundation of China/ ; 2021ZD0200500//STI2023-Major Projects/ ; 23ZR1418400//National Science Foundation of Shanghai/ ; 2022ECNUXWK-XK003//Fundamental Research Funds for the Central Universities/ ; ANR-16-IDEX-0005//Agence Nationale de la Recherche/ ; ANR-11-LABX-0042//Agence Nationale de la Recherche/ ; ANR-11-IDEX-007//Agence Nationale de la Recherche/ ; ANR-21-CE37-0032//Agence Nationale de la Recherche/ ; }, mesh = {Humans ; *Decision Making/physiology ; Morals ; }, abstract = {Understanding how corrupt behavior occurs is a critical issue at the intersection of behavioral ethics, social psychology, and other related social sciences, laying the foundation for establishing effective anticorruption policies. Despite a substantial body of studies focused on bribe-taking behavior-a typical form of corruption-and its modulators, its underlying psychological processes remain poorly understood. Drawing inspiration from recent literature on neuroeconomics and moral decision-making, we argue that bribe-taking decision-making involves a value-based computational process that can be characterized by a computational framework. We show how this framework advances our understanding of bribe-taking decision-making by (1) clarifying how the cost-benefit tradeoff determines the decision to accept or reject a bribe and its neural foundations, (2) improving the prediction of bribe-taking behaviors across contexts and individuals, and (3) enhancing our comprehension of individual differences in bribe-taking behaviors. Moreover, we delineate how this framework can benefit future research on bribery by examining the mechanisms through which various modulators impact the bribe-taking behaviors or the computational processes underlying more intricate forms of corrupt behaviors. We also discussed its potential fusion with artificial intelligence techniques in offering insights for understanding cognitive processes underlying bribe-taking behaviors and designing anticorruption strategies.}, }
@article {pmid39935682, year = {2025}, author = {Yasuhara, M and Nambu, I}, title = {Error-related potentials during multitasking involving sensorimotor control: an ERP and offline decoding study for brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1516721}, pmid = {39935682}, issn = {1662-5161}, abstract = {Humans achieve efficient behaviors by perceiving and responding to errors. Error-related potentials (ErrPs) are electrophysiological responses that occur upon perceiving errors. Leveraging ErrPs to improve the accuracy of brain-computer interfaces (BCIs), utilizing the brain's natural error-detection processes to enhance system performance, has been proposed. However, the influence of external and contextual factors on the detectability of ErrPs remains poorly understood, especially in multitasking scenarios involving both BCI operations and sensorimotor control. Herein, we hypothesized that the difficulty in sensorimotor control would lead to the dispersion of neural resources in multitasking, resulting in a reduction in ErrP features. To examine this, we conducted an experiment in which participants were instructed to keep a ball within a designated area on a board, while simultaneously attempting to control a cursor on a display through motor imagery. The BCI provided error feedback with a random probability of 30%. Three scenarios-without a ball (single-task), lightweight ball (easy-task), and heavyweight ball (hard-task)-were used for the characterization of ErrPs based on the difficulty of sensorimotor control. In addition, to examine the impact of multitasking on ErrP-BCI performance, we analyzed single-trial classification accuracy offline. Contrary to our hypothesis, varying the difficulty of sensorimotor control did not result in significant changes in ErrP features. However, multitasking significantly affected ErrP classification accuracy. Post-hoc analyses revealed that the classifier trained on single-task ErrPs exhibited reduced accuracy under hard-task scenarios. To our knowledge, this study is the first to investigate how ErrPs are modulated in a multitasking environment involving both sensorimotor control and BCI operation in an offline framework. Although the ErrP features remained unchanged, the observed variation in accuracy suggests the need to design classifiers that account for task load even before implementing a real-time ErrP-based BCI.}, }
@article {pmid39935311, year = {2025}, author = {Wen, X and Xue, P and Zhu, M and Zhong, J and Yu, W and Ma, S and Liu, Y and Liu, P and Jing, B and Yang, M and Mo, X and Zhang, D}, title = {Alteration in Cortical Structure Mediating the Impact of Blood Oxygen-Carrying Capacity on Gross Motor Skills in Infants With Complex Congenital Heart Disease.}, journal = {Human brain mapping}, volume = {46}, number = {3}, pages = {e70155}, pmid = {39935311}, issn = {1097-0193}, support = {62476129//National Natural Science Foundation of China/ ; 81970265//National Natural Science Foundation of China/ ; 82270310//National Natural Science Foundation of China/ ; NZ2024040//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Motor Skills/physiology ; Male ; Female ; *Cerebral Cortex/diagnostic imaging/physiopathology/growth &amp; development ; *Heart Defects, Congenital/physiopathology ; Infant ; *Magnetic Resonance Imaging ; Child, Preschool ; Oxygen/blood ; }, abstract = {Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12-26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen-carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen-carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen-carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen-carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.}, }
@article {pmid39935127, year = {2025}, author = {Xu, Q and Wang, L and Xi, Y and Ruan, T and Cao, J and Xu, M and Zheng, K and Du, Z and Wei, N and Wang, X and Yang, B and Liu, J}, title = {An Efficient MEMS Microelectrode Array with Reliable Interelectrode Insulation Processes for In Vivo Neural Recording.}, journal = {Small (Weinheim an der Bergstrasse, Germany)}, volume = {21}, number = {10}, pages = {e2407950}, doi = {10.1002/smll.202407950}, pmid = {39935127}, issn = {1613-6829}, support = {2022ZD0208601//STI 2030-Major Projects/ ; 2022ZD0208600//STI 2030-Major Projects/ ; 2022YFB3207301//National Key R&amp;D Program of China/ ; 2022YFF120301//National Key R&amp;D Program of China/ ; XDA25040100//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040200//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040300//Strategic Priority Research Program of Chinese Academy of Sciences/ ; 62474109 42127807-03//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21TQ1400203//Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University/ ; SL2023ZD205//Oceanic Interdisciplinary Program of Shanghai Jiao Tong University/ ; 21&#xd7;010301627//SJTU Trans-med Award/ ; //Fundamental Research Funds for the Central Universities/ ; }, mesh = {*Microelectrodes ; Animals ; Mice ; Micro-Electrical-Mechanical Systems/instrumentation ; Neurons/physiology ; Electrodes, Implanted ; }, abstract = {Microelectrode arrays, particularly Utah arrays, offer irreplaceable advantages in clinical applications and play a crucial role in advancing brain-computer interactions. However, the glass-fused monolithic structure of Utah arrays limits functional expansion, and the glass insulation process is complex, costly, and time-intensive. This paper presents a microelectrode array with a simple and time-saving fabrication process, utilizing low-resistance silicon and borosilicate glass wafers as electrodes and insulation substrates, respectively. The utilization of the anodic bonding process improves production efficiency and enhances process compatibility. A one-step static wet etching process is used to form microneedle morphology to further simplify the fabrication process. Sputtered iridium oxide, as the electrode interface material, significantly reduces electrochemical impedance, and cellular experiments have confirmed its non-cytotoxicity. Moreover, the implantation into the primary visual cortex of mice has demonstrated the ability of the electrode to record in vivo electrical signals within 15 days. Movement trajectory experiments demonstrate that the mice exhibit good behavior activities following electrode implantation. The bonded microelectrode array (BMEA) presented in this work provides a universal and effective tool for neural recording, with prospective applications in multi-physiological monitoring and microelectromechanical system integration.}, }
@article {pmid39935067, year = {2025}, author = {Yao, Y and Ahnood, A and Chambers, A and Tong, W and Prawer, S}, title = {Nitrogen-Doped Ultrananocrystalline Diamond - Optoelectronic Biointerface for Wireless Neuronal Stimulation.}, journal = {Advanced healthcare materials}, volume = {14}, number = {9}, pages = {e2403901}, pmid = {39935067}, issn = {2192-2659}, support = {2029454//National Health and Medical Research Council/ ; DE220100302//Australian Research Council/ ; DE210102750//Australian Research Council/ ; }, mesh = {Animals ; *Diamond/chemistry ; *Nitrogen/chemistry ; Rats ; *Neurons/physiology ; Retina ; *Wireless Technology ; Semiconductors ; Light ; }, abstract = {This study presents a semiconducting optoelectronic system for light-controlled non-genetic neuronal stimulation using visible light. The system architecture is entirely wireless, comprising a thin film of nitrogen-doped ultrananocrystalline diamond directly grown on a semiconducting silicon substrate. When immersed in a physiological medium and subjected to pulsed illumination in the visible (595 nm) or near-infrared wavelength (808 nm) range, charge accumulation at the device-medium interface induces a transient ionic displacement current capable of electrically stimulating neurons with high temporal resolution. With a measured photoresponsivity of 7.5 mA W[-1], the efficacy of this biointerface is demonstrated through optoelectronic stimulation of degenerate rat retinas using 595 nm irradiation, pulse durations of 50-500 ms, and irradiance levels of 1.1-4.3 mW mm[-2], all below the safe ocular threshold. This work presents the pioneering utilization of a diamond-based optoelectronic platform, capable of generating sufficiently large photocurrents for neuronal stimulation in the retina.}, }
@article {pmid39934334, year = {2025}, author = {Chen, Y and Hu, J and Zhao, P and Fang, J and Kuang, Y and Liu, Z and Dong, S and Yao, W and Ding, Y and Wang, X and Pan, Y and Wu, J and Zhao, J and Yang, J and Xu, Z and Liu, X and Zhang, Y and Wu, C and Zhang, L and Fan, M and Feng, S and Hong, Z and Yan, Z and Xia, H and Tang, K and Yang, B and Liu, W and Sun, Q and Mei, K and Zou, W and Huang, Y and Feng, D and Yi, C}, title = {Rpl12 is a conserved ribophagy receptor.}, journal = {Nature cell biology}, volume = {27}, number = {3}, pages = {477-492}, pmid = {39934334}, issn = {1476-4679}, support = {32122028//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92254307//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32070739//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100600//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32370822//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970919//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR21C070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Caenorhabditis elegans/metabolism/genetics ; *Drosophila melanogaster/metabolism/genetics ; *Ribosomal Proteins/metabolism/genetics ; *Drosophila Proteins/metabolism/genetics ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Autophagy ; Phosphorylation ; Ribosomes/metabolism/genetics ; RNA, Ribosomal/metabolism/genetics ; Autophagy-Related Protein 8 Family/metabolism/genetics ; Autophagy-Related Proteins/metabolism/genetics ; Longevity/genetics ; Starvation/metabolism/genetics ; Aging/metabolism/genetics ; Autophagy-Related Protein-1 Homolog ; }, abstract = {Ribophagy is a selective autophagic process that regulates ribosome turnover. Although NUFIP1 has been identified as a mammalian receptor for ribophagy, its homologues do not exist in yeast and nematodes. Here we demonstrate that Rpl12, a ribosomal large subunit protein, functions as a conserved ribophagy receptor in multiple organisms. Disruption of Rpl12-Atg8s binding leads to significant accumulation of ribosomal proteins and rRNA, while Atg1-mediated Rpl12 phosphorylation enhances its association with Atg11, thus triggering ribophagy during starvation. Ribophagy deficiency accelerates cell death induced by starvation and pathogen infection, leading to impaired growth and development and a shortened lifespan in both Caenorhabditis elegans and Drosophila melanogaster. Moreover, ribophagy deficiency results in motor impairments associated with ageing, while the overexpression of RPL12 significantly improves movement defects induced by starvation, ageing and Aβ accumulation in fly models. Our findings suggest that Rpl12 functions as a conserved ribophagy receptor vital for ribosome metabolism and cellular homeostasis.}, }
@article {pmid39933011, year = {2025}, author = {Jin, X and Jiang, M and Qian, L and Tao, L and Yang, Y and Xing, L and Qian, Z and Li, W}, title = {Effect of 433 MHz double-slot microwave antennas for double-zone ablation in ex vivo swine liver experiment.}, journal = {PloS one}, volume = {20}, number = {2}, pages = {e0315678}, pmid = {39933011}, issn = {1932-6203}, mesh = {Animals ; *Microwaves ; *Liver/surgery ; Swine ; Ablation Techniques/methods/instrumentation ; Finite Element Analysis ; Equipment Design ; Computer Simulation ; }, abstract = {PURPOSE: To evaluate the effects of axial length and slot-to-slot distance of double-slot microwave antenna (DSMA) with frequency of 433 MHz on the size and shape of ablation zones created under different input microwave powers.<br><br>MATERIALS AND METHODS: The design of double slot microwave antennas (DSMAs) with axial lengths (70&#x2009;mm, 30&#x2009;mm) and slot-to-slot distance (49&#x2009;mm, 10&#x2009;mm) were optimized by numerical simulation and ex vivo liver experiments. Finite-element method simulations and forty ablations of swine liver were employed to obtain the temperature distributions within liver tissue using DSMAs at the 433 MHz operating frequency in a range of heating powers (20, 30, 40 and 50W) for 600&#x2009;s. The dependence of the effectiveness of MWA on the axial length and slot-to-slot distance of antenna as well as the input power was further evaluated by analyzing morphologic characteristics of ablated zone.<br><br>RESULTS: Two-zone ablation was achieved by two types of double-slot antennas in our study with frequency of 433 MHz, and the observed shapes of ex vivo experimental ablation zones were in good agreement with patterns predicted by simulation models. The ablation zone exhibited a 'gourd' shape after the treatment using the antenna with longer axial length and slot-to-slot distance, while the short antenna caused a guitar-shape ablation in liver tissue after MWA.<br><br>CONCLUSION: The dedicated design of our DSMAs with a frequency of 433 MHz could enable new ablation shapes with controllable dimensions, which can be applied to the clinical treatment of MWA for gourd-shaped liver tumors and other long-shaped tumors. Furthermore, research can be conducted on how to design the antenna as flexible and use it for the treatment of pulmonary nodules or varicose veins.}, }
@article {pmid39930808, year = {2025}, author = {Cao, M and Zhu, S and Tang, E and Xue, C and Li, K and Yu, H and Zhong, T and Li, T and Chen, H and Deng, W}, title = {Neural correlates of emotional processing in trauma-related narratives.}, journal = {Psychological medicine}, volume = {55}, number = {}, pages = {e33}, doi = {10.1017/S0033291724003398}, pmid = {39930808}, issn = {1469-8978}, mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Male ; Adult ; Female ; Case-Control Studies ; *Emotions/physiology ; *Prefrontal Cortex/physiopathology/diagnostic imaging ; *Spectroscopy, Near-Infrared ; Narration ; Young Adult ; Middle Aged ; Natural Language Processing ; Psychological Trauma/physiopathology/diagnostic imaging ; }, abstract = {BACKGROUND: Post-traumatic stress disorder (PTSD) is a mental health condition caused by the dysregulation or overgeneralization of memories related to traumatic events. Investigating the interplay between explicit narrative and implicit emotional memory contributes to a better understanding of the mechanisms underlying PTSD.<br><br>METHODS: This case-control study focused on two groups: unmedicated patients with PTSD and a trauma-exposed control (TEC) group who did not develop PTSD. Experiments included real-time measurements of blood oxygenation changes using functional near-infrared spectroscopy during trauma narration and processing of emotional and linguistic data through natural language processing (NLP).<br><br>RESULTS: Real-time fNIRS monitoring showed that PTSD patients (mean [SD] Oxy-Hb activation, 0.153 [0.084], 95% CI 0.124 to 0.182) had significantly higher brain activity in the left anterior medial prefrontal cortex (L-amPFC) within 10&#xa0;s after expressing negative emotional words compared with the control group (0.047 [0.026], 95% CI 0.038 to 0.056; p&#xa0;<&#xa0;0.001). In the control group, there was a significant time-series correlation between the use of negative emotional memory words and activation of the L-amPFC (latency 3.82&#xa0;s, slope&#xa0;=&#xa0;0.0067, peak value&#xa0;=&#xa0;0.184, difference&#xa0;=&#xa0;0.273; Spearman's r&#xa0;=&#xa0;0.727, p&#xa0;<&#xa0;0.001). In contrast, the left anterior cingulate prefrontal cortex of PTSD patients remained in a state of high activation (peak value&#xa0;=&#xa0;0.153, difference&#xa0;=&#xa0;0.084) with no apparent latency period.<br><br>CONCLUSIONS: PTSD patients display overactivity in pathways associated with rapid emotional responses and diminished regulation in cognitive processing areas. Interventions targeting these pathways may alleviate symptoms of PTSD.}, }
@article {pmid39927921, year = {2025}, author = {Chen, J and Ke, Y and Ni, G and Liu, S and Ming, D}, title = {Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task.}, journal = {Neuromodulation : journal of the International Neuromodulation Society}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neurom.2025.01.003}, pmid = {39927921}, issn = {1525-1403}, abstract = {BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV).<br><br>OBJECTIVE: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task.<br><br>MATERIALS AND METHODS: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks.<br><br>RESULTS: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV.<br><br>CONCLUSIONS: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions.}, }
@article {pmid39926788, year = {2024}, author = {Sah, SK and Taksande, V and Jadhav, D and Maurya, AT}, title = {Exploring the Impact of Brain-Computer Interfaces on Health Care: Innovations, Challenges, and Future Prospects: A Review Article.}, journal = {Journal of pharmacy &amp; bioallied sciences}, volume = {16}, number = {Suppl 4}, pages = {S3037-S3040}, pmid = {39926788}, issn = {0976-4879}, abstract = {Brain-Computer Interfaces (BCIs) are an innovative technology that methods with a great possibility to revolutionize the sphere of medicine with the help of integration of human brain and external devices. In this article, we discuss how BCIs can be incorporated into hospitals and civil rehabilitation centers, possibly for rehabilitation, communication, and cognitive treatments. This review aims to discuss the advancement, usefulness, difficulties, and potential in regards to the use of BCIs in healthcare. We describe trends in the development of BCIs from simple experimental paradigms to multimedia advanced devices and their usage in clinical practice: assistive technology in patients with motor disorders, neurorehabilitation of post-stroke patients, and cognitive prosthesis for humans with neurodegenerative diseases. The article also emphasizes on present-day issues including signal quality, comfort level of the users, and the ethical parameter of the technique along with the research going on and future work streams. Thus, by evaluating the modern developments in the field and highlighting the existing problems, this article will try to give a briefing on the current stage of application of BCIs in the sphere of healthcare.}, }
@article {pmid39926014, year = {2025}, author = {Li, C and Xu, Y and Feng, T and Wang, M and Zhang, X and Zhang, L and Cheng, R and Chen, W and Chen, W and Zhang, S}, title = {Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury.}, journal = {Frontiers in neuroscience}, volume = {19}, number = {}, pages = {1532099}, pmid = {39926014}, issn = {1662-4548}, abstract = {INTRODUCTION: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.<br><br>METHODS: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG-EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.<br><br>RESULTS: Among the three functional connectivity methods investigated-coherence, Pearson correlation coefficient and mutual information (MI)-the MI-based EEG-EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p&#x202f;>&#x202f;0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p&#x202f;>&#x202f;0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.<br><br>CONCLUSION: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.}, }
@article {pmid39925722, year = {2025}, author = {Radwan, YA and Ahmed Mohamed, E and Metwalli, D and Barakat, M and Ahmed, A and Kiroles, AE and Selim, S}, title = {Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.}, journal = {Frontiers in human neuroscience}, volume = {19}, number = {}, pages = {1484470}, pmid = {39925722}, issn = {1662-5161}, abstract = {The potential and utility of inner speech is pivotal for developing practical, everyday Brain-Computer Interface (BCI) applications, as it represents a type of brain signal that operates independently of external stimuli however it is largely underdeveloped due to the challenges faced in deciphering its signals. In this study, we evaluated the behaviors of various Machine Learning (ML) and Deep Learning (DL) models on a publicly available dataset, employing popular preprocessing methods as feature extractors to enhance model training. We face significant challenges like subject-dependent variability, high noise levels, and overfitting. To address overfitting in particular, we propose using "BruteExtraTree": a new classifier which relies on moderate stochasticity inherited from its base model, the ExtraTreeClassifier. This model not only matches the best DL model, ShallowFBCSPNet, in the subject-independent scenario in our experiments scoring 32% accuracy, but also surpasses the state-of-the-art by achieving 46.6% average per-subject accuracy in the subject-dependent case. Our results on the subject-dependent case show promise on the possibility of a new paradigm for using inner speech data inspired from LLM pretraining but we also highlight the crucial need for a drastic change in data recording or noise removal methods to open the way for more practical accuracies in the subject-independent case.}, }
@article {pmid39923980, year = {2025}, author = {Ghasimi, A and Shamekhi, S}, title = {Enhanced EEG-based cognitive workload detection using RADWT and machine learning.}, journal = {Neuroscience}, volume = {569}, number = {}, pages = {231-244}, doi = {10.1016/j.neuroscience.2025.01.068}, pmid = {39923980}, issn = {1873-7544}, mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Machine Learning ; Male ; *Wavelet Analysis ; Brain/physiology ; Adult ; Female ; Workload ; Young Adult ; Support Vector Machine ; }, abstract = {Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, brain-computer interfaces, and cognitive monitoring. In this work, different levels of cognitive workload are investigated, and a classification approach based on the Rational-Dilation Wavelet Transform (RADWT) is proposed. RADWT excels at capturing the oscillatory behavior of EEG signal sub-bands, offering high precision through its ability to adaptively analyze both temporal and spectral dynamics. Different classifications of machine learning and feature selection techniques were evaluated to get optimum classification accuracy and identify the most effective combination of features for the used dataset. The analysis shows that the most relevant brain region in differentiating cognitive workload levels is the frontal region, along with alpha and theta rhythm sub-bands. Integrating RADWT with a Linear Support Vector Machine (LSVM) and minimum Redundancy Maximum Relevance (mRMR) feature selection method yields notable classification accuracy. Concretely, the model yields accuracies of 96.6% for 0-back vs.3-back, 94.9% for 0-back vs 2-back, 92.3% for 2-back vs 3-back, and 81.7% for the three-class scenario. These results confirm the validity of the method proposed for estimating cognitive workload using the RADWT- and machine learning-based approach. The results also offer insights into neural mechanisms and a foundation for advanced applications in adaptive systems, brain-computer interfaces, and cognitive monitoring.}, }
@article {pmid39921840, year = {2025}, author = {Huang, Y and Wang, J and Liu, N and Xu, H}, title = {Zona Incerta: A Bridge for Infant-Mother Interaction.}, journal = {Neuroscience bulletin}, volume = {41}, number = {5}, pages = {921-924}, pmid = {39921840}, issn = {1995-8218}, }
@article {pmid39921681, year = {2025}, author = {Liyanagedera, ND and Bareham, CA and Kempton, H and Guesgen, HW}, title = {Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.}, journal = {Brain informatics}, volume = {12}, number = {1}, pages = {4}, pmid = {39921681}, issn = {2198-4018}, support = {1839RF//Neurological Foundation of New Zealand/ ; MAU2011//Royal Society of New Zealand Marsden Fast-Start/ ; }, abstract = {This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.}, }
@article {pmid39918096, year = {2025}, author = {Chen, L and Yang, Z and Ji, S and Song, T and Li, H and Tang, Y and Chen, Y and Li, Y}, title = {Comparing the Risk of Epilepsy in Patients With Simple Congenital Heart Diseases: A Prospective Cohort Study.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {2}, pages = {e70230}, pmid = {39918096}, issn = {1755-5949}, support = {2022-I2M-C&amp;T-B-100//the Chinese Academy of Medical Sciences by Medical and Health Technology Innovation Project/ ; 2024NSFSC1652//the support from the Natural Science Foundation of Sichuan/ ; }, mesh = {Humans ; Female ; Male ; *Epilepsy/epidemiology ; *Heart Defects, Congenital/epidemiology/complications ; Prospective Studies ; Adult ; Cohort Studies ; Young Adult ; Adolescent ; Middle Aged ; Incidence ; Child ; Follow-Up Studies ; China/epidemiology ; Child, Preschool ; Risk Factors ; }, abstract = {AIMS: Simple congenital heart diseases (CHD) are associated with various central nervous system diseases, including epilepsy. This study aimed to compare the risk of epilepsy in patients with different types of simple CHD.<br><br>METHODS: In this prospective cohort study, from January 2008 to June 2022, patients with atrial septal defect (ASD), patent foramen ovale (PFO), ventricular septal defect (VSD), and patent ductus arteriosus (PDA) were recruited at the Registration Center of CHD in West China Hospital. Follow-up was conducted yearly until the diagnosis of epilepsy, loss to follow-up, or end of study. The outcomes included a comparison of epilepsy incidence according to different simple CHD types and a risk assessment of developing epilepsy. Multivariable Poisson regression was performed to adjusted factors of demographics and disease history.<br><br>RESULTS: Of 10,914 patients who met the inclusion criteria, 108 were diagnosed with epilepsy at an average follow-up of 2.19&#x2009;years. Epilepsy incidence in patients with PFO, VSD, PDA, and ASD was 8.58/1000, 4.85/1000, 3.98/1000, and 2.63/1000 person-years, respectively. Compared with ASD patients (reference group), the risk ratios (95% confidence intervals) in patients with PFO, VSD, and PDA were 3.28 (2.00-5.43), 1.47 (0.79-2.68), and 1.46 (0.70-2.82), respectively. Subgroup analyses determined that patients with simple CHD who underwent CHD surgery demonstrated a lower risk of epilepsy than those who did not.<br><br>CONCLUSION: Among the major types of simple CHD, PFO was associated with a significantly higher risk of epilepsy, while the risk was reduced in those who underwent PFO closure procedures.}, }
@article {pmid39914028, year = {2025}, author = {Cernera, S and Gemicioglu, T and Berezutskaya, J and Csaky, R and Verwoert, M and Polyakov, D and Papadopoulos, S and Spagnolo, V and Astudillo, JG and Kumar, S and Alawieh, H and Kelly, D and Keough, JRG and Minhas, A and Dold, M and Han, Y and McClanahan, A and Mustafa, M and Gonzalez-Espana, JJ and Garro, F and Vujic, A and Kacker, K and Kapeller, C and Geukes, S and Verbaarschot, C and Wimmer, M and Sultana, M and Ahmadi, S and Herff, C and Sburlea, AI and Jeunet, C and Thompson, DE and Semprini, M and Andersen, R and Stavisky, S and Kinney-Lang, E and Lotte, F and Thielen, J and Chen, X and Peterson, V and Gunduz, A and Vaughan, T and Valeriani, D}, title = {Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adb335}, pmid = {39914028}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography/methods ; }, abstract = {The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc &amp; Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.}, }
@article {pmid39914006, year = {2025}, author = {Bakas, S and Ludwig, S and Adamos, DA and Laskaris, N and Panagakis, Y and Zafeiriou, S}, title = {Latent alignment in deep learning models for EEG decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adb336}, pmid = {39914006}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/physiology ; Male ; Imagination/physiology ; Female ; Adult ; Sleep Stages/physiology ; Young Adult ; }, abstract = {Objective. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning distributions in the deep learning model's feature space is more effective for classification.Approach. We introduce the Latent Alignment method, which won the Benchmarks for EEG Transfer Learning competition. This method can be formulated as a deep set architecture applied to trials from a given subject, introducing deep sets to EEG decoding for the first time. We compare Latent Alignment to recent statistical domain adaptation techniques, carefully considering class-discriminative artifacts and the impact of class distributions on classification performance.Main results. Our experiments across motor imagery, sleep stage classification, and P300 event-related potential tasks validate Latent Alignment's effectiveness. We identify a trade-off between improved classification accuracy when alignment is performed at later modeling stages and increased susceptibility to class imbalance in the trial set used for statistical computation.Significance. Latent Alignment offers consistent improvements to subject-independent deep learning models for EEG decoding when relevant practical considerations are addressed. This work advances our understanding of statistical alignment techniques in EEG decoding and provides insights for their effective implementation in real-world BCI applications, potentially facilitating broader use of BCIs in healthcare, assistive technologies, and beyond. The model code is available athttps://github.com/StylianosBakas/LatentAlignment.}, }
@article {pmid39913287, year = {2025}, author = {Ma, Q and Tian, JL and Lou, Y and Guo, R and Ma, XR and Wu, JB and Yang, J and Tang, BJ and Li, S and Qiu, M and Duan, S and Zhao, JW and Zhang, J and Xu, ZZ}, title = {Oligodendrocytes drive neuroinflammation and neurodegeneration in Parkinson's disease via the prosaposin-GPR37-IL-6 axis.}, journal = {Cell reports}, volume = {44}, number = {2}, pages = {115266}, doi = {10.1016/j.celrep.2025.115266}, pmid = {39913287}, issn = {2211-1247}, mesh = {Animals ; *Interleukin-6/metabolism ; *Oligodendroglia/metabolism/pathology ; *Receptors, G-Protein-Coupled/metabolism ; *Parkinson Disease/metabolism/pathology/genetics ; Mice ; Humans ; *Dopaminergic Neurons/metabolism/pathology ; *Saposins/metabolism ; Mice, Inbred C57BL ; Neuroinflammatory Diseases/metabolism/pathology ; Male ; Signal Transduction ; Substantia Nigra/metabolism/pathology ; Disease Models, Animal ; Nerve Degeneration/pathology/metabolism ; }, abstract = {Parkinson's disease (PD) is a common neurodegenerative disease and is difficult to treat due to its elusive mechanisms. Recent studies have identified a striking association between oligodendrocytes and PD progression, yet how oligodendrocytes regulate the pathogenesis of PD is still unknown. Here, we show that G-protein-coupled receptor 37 (GPR37) is upregulated in oligodendrocytes of the substantia nigra and that prosaposin (PSAP) secretion is increased in parkinsonian mice. The released PSAP can induce interleukin (IL)-6 upregulation and secretion from oligodendrocytes via a GPR37-dependent pathway, resulting in enhanced neuroinflammation, dopamine neuron degeneration, and behavioral deficits. GPR37 deficiency in oligodendrocytes prevents neurodegeneration in multiple PD models. Finally, the hallmarks of the PSAP-GPR37-IL-6 axis are observed in patients with PD. Thus, our results reveal that dopaminergic neurons interact with oligodendrocytes via secreted PSAP, and our findings identify the PSAP-GPR37-IL-6 axis as a driver of PD pathogenesis and a potential therapeutic target that might alleviate PD progression in patients.}, }
@article {pmid39912868, year = {2025}, author = {Chen, P and Zhang, B and He, E and Xiao, Y and Liu, F and Lin, P and Wang, Z and Pan, G}, title = {Towards scalable memristive hardware for spiking neural networks.}, journal = {Materials horizons}, volume = {}, number = {}, pages = {}, doi = {10.1039/d4mh01676a}, pmid = {39912868}, issn = {2051-6355}, abstract = {Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks. This paper reviews the promises and challenges of memristive devices in SNN implementations, and our discussions are focused on the scaling and integration of neuronal and synaptic devices. We survey recent progress in device and circuit development, discuss possible pathways for chip-level integration, and finally probe into hardware-oriented algorithm designs. This review offers a system-level perspective on implementing scalable memristor based SNN platforms.}, }
@article {pmid39912736, year = {2025}, author = {Huang, Z and Mei, T and Zhu, X and Xiao, K}, title = {Ionic Device: From Neuromorphic Computing to Interfacing with the Brain.}, journal = {Chemistry, an Asian journal}, volume = {20}, number = {7}, pages = {e202401170}, doi = {10.1002/asia.202401170}, pmid = {39912736}, issn = {1861-471X}, support = {B2401005//Shenzhen Medical Research Fund/ ; 22275079//National Natural Science Foundation of China/ ; 22474053//National Natural Science Foundation of China/ ; KQTD20221101093559017//Shenzhen Science and Technology Program/ ; JCYJ20230807093205011//Shenzhen Science and Technology Program/ ; 2024A1515012600//Guangdong Basic and Applied Basic Research Foundation/ ; 2022B1212010003//Guangdong Provincial Key Laboratory of Advanced Biomaterials/ ; //Starting Grant from Southern University of Science and Technology/ ; G03050K002//High level of special funds/ ; }, mesh = {Humans ; *Neural Networks, Computer ; *Brain/physiology ; Ions/chemistry ; Algorithms ; }, abstract = {In living organisms, the modulation of ion conductivity in ion channels of neuron cells enables intelligent behaviors, such as generating, transmitting, and storing neural signals. Drawing inspiration from these natural processes, researchers have fabricated ionic devices that replicate the functions of the nervous system. However, this field remains in its infancy, necessitating extensive foundational research in ionic device preparation, algorithm development, and biological interaction. This review summarizes recently developed neuromorphic ionic devices into three categories based on the materials states: liquid, semi-solid, and solid. The neural network algorithms embedded in these devices for neuromorphic computing are introduced, and future directions for the development of bidirectional human-computer interaction and hybrid human-computer intelligence are discussed.}, }
@article {pmid39911854, year = {2025}, author = {Wu, X and Chu, Y and Li, Q and Luo, Y and Zhao, Y and Zhao, X}, title = {AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.}, journal = {Frontiers in neurorobotics}, volume = {19}, number = {}, pages = {1540033}, pmid = {39911854}, issn = {1662-5218}, abstract = {Recently, electroencephalogram (EEG) based on motor imagery (MI) have gained significant traction in brain-computer interface (BCI) technology, particularly for the rehabilitation of paralyzed patients. But the low signal-to-noise ratio of MI EEG makes it difficult to decode effectively and hinders the development of BCI. In this paper, a method of attention-based multiscale EEGNet (AMEEGNet) was proposed to improve the decoding performance of MI-EEG. First, three parallel EEGNets with fusion transmission method were employed to extract the high-quality temporal-spatial feature of EEG data from multiple scales. Then, the efficient channel attention (ECA) module enhances the acquisition of more discriminative spatial features through a lightweight approach that weights critical channels. The experimental results demonstrated that the proposed model achieves decoding accuracies of 81.17, 89.83, and 95.49% on BCI-2a, 2b and HGD datasets. The results show that the proposed AMEEGNet effectively decodes temporal-spatial features, providing a novel perspective on MI-EEG decoding and advancing future BCI applications.}, }
@article {pmid39909352, year = {2025}, author = {Li, X and Wei, W and Qian, L and Li, X and Li, M and Kakkos, I and Wang, Q and Yu, H and Guo, W and Ma, X and Matsopoulos, GK and Zhao, L and Deng, W and Sun, Y and Li, T}, title = {Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity.}, journal = {Brain research bulletin}, volume = {222}, number = {}, pages = {111238}, doi = {10.1016/j.brainresbull.2025.111238}, pmid = {39909352}, issn = {1873-2747}, mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnostic imaging ; Male ; Female ; Adult ; *Magnetic Resonance Imaging/methods ; *Connectome/methods ; *Intelligence/physiology ; *Intelligence Tests ; Brain/physiopathology/diagnostic imaging ; Middle Aged ; Nerve Net/physiopathology/diagnostic imaging ; Young Adult ; Rest/physiology ; Neural Pathways/physiopathology/diagnostic imaging ; }, abstract = {BACKGROUND: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.<br><br>METHODS: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.<br><br>RESULTS: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.<br><br>CONCLUSIONS: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.}, }
@article {pmid39908388, year = {2025}, author = {Xiao, Y and Liu, Y and Zhang, B and Chen, P and Zhu, H and He, E and Zhao, J and Huo, W and Jin, X and Zhang, X and Jiang, H and Ma, D and Zheng, Q and Tang, H and Lin, P and Kong, W and Pan, G}, title = {Bio-plausible reconfigurable spiking neuron for neuromorphic computing.}, journal = {Science advances}, volume = {11}, number = {6}, pages = {eadr6733}, pmid = {39908388}, issn = {2375-2548}, mesh = {*Neurons/physiology ; *Neural Networks, Computer ; *Action Potentials/physiology ; *Models, Neurological ; Humans ; }, abstract = {Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO2-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.}, }
@article {pmid39908280, year = {2025}, author = {El-Osta, A and Al Ammouri, M and Khan, S and Altalib, S and Karki, M and Riboli-Sasco, E and Majeed, A}, title = {Community perspectives regarding brain-computer interfaces: A cross-sectional study of community-dwelling adults in the UK.}, journal = {PLOS digital health}, volume = {4}, number = {2}, pages = {e0000524}, pmid = {39908280}, issn = {2767-3170}, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) represent a ground-breaking advancement in neuroscience, facilitating direct communication between the brain and external devices. This technology has the potential to significantly improve the lives of individuals with neurological disorders by providing innovative solutions for rehabilitation, communication and personal autonomy. However, despite the rapid progress in BCI technology and social media discussions around Neuralink, public perceptions and ethical considerations concerning BCIs-particularly within community settings in the UK-have not been thoroughly investigated.<br><br>OBJECTIVE: The primary aim of this study was to investigate public knowledge, attitudes and perceptions regarding BCIs including ethical considerations. The study also explored whether demographic factors were related to beliefs about BCIs increasing inequalities, support for strict regulations, and perceptions of appropriate fields for BCI design, testing and utilization in healthcare.<br><br>METHODS: This cross-sectional study was conducted between 1 December 2023 and 8 March 2024. The survey included 29 structured questions covering demographics, awareness of BCIs, ethical considerations and willingness to use BCIs for various applications. The survey was distributed via the Imperial College Qualtrics platform. Participants were recruited primarily through Prolific Academic's panel and personal networks. Data analysis involved summarizing responses using frequencies and percentages, with chi-squared tests to compare groups. All data were securely stored and pseudo-anonymized to ensure confidentiality.<br><br>RESULTS: Of the 950 invited respondents, 846 participated and 806 completed the survey. The demographic profile was diverse, with most respondents aged 36-45 years (26%) balanced in gender (52% female), and predominantly identifying as White (86%). Most respondents (98%) had never used BCIs, and 65% were unaware of them prior to the survey. Preferences for BCI types varied by condition. Ethical concerns were prevalent, particularly regarding implantation risks (98%) and costs (92%). Significant associations were observed between demographic variables and perceptions of BCIs regarding inequalities, regulation and their application in healthcare. Conclusion: Despite strong interest in BCIs, particularly for medical applications, ethical concerns, safety and privacy issues remain significant highlighting the need for clear regulatory frameworks and ethical guidelines, as well as educational initiatives to improve public understanding and trust. Promoting public discourse and involving stakeholders including potential users, ethicists and technologists in the design process through co-design principles can help align technological development with public concerns whilst also helping developers to proactively address ethical dilemmas.}, }
@article {pmid39908266, year = {2025}, author = {Liu, X and Zhi, H and Czosnyka, M and Robba, C and Czosnyka, Z and Summers, JL and Yu, H and Tong, X and Gao, G and Xiao, G and Yu, K and Xing, Y and Mao, R and Yin, S and Chao, Y and Li, H and Pu, K and Feng, K and Pang, M and Ming, D}, title = {Advancing Hydrocephalus Management: Pathogenesis Insights, Therapeutic Innovations, and Emerging Challenges.}, journal = {Aging and disease}, volume = {}, number = {}, pages = {}, doi = {10.14336/AD.2024.1434}, pmid = {39908266}, issn = {2152-5250}, abstract = {Hydrocephalus is a prevalent neurological disorder, particularly impactful in older adults, characterized by high incidence and numerous complications that impose a significant burden on healthcare systems. This review aims to provide a comprehensive description of hydrocephalus pathogenesis, focusing on cellular and molecular insights derived from animal models. We also present the latest advances in hydrocephalus research and highlight potential therapeutic targets. Lastly, the review advocates the integration of findings from both animal and human studies to achieve better outcomes and examines the potential of emerging technologies. We wish to raise public attention about this disease in an aging society. Current animal models for hydrocephalus involve acquired hydrocephalus models and genetic/congenital hydrocephalus models. Studies from animals have shown that the main mechanisms of models can be broadly classified into nine types. A variety of drug-targeted therapy methods and non-surgical treatment methods have been used in clinical practice. But current treatment approaches primarily focus on symptomatic relief and intracranial pressure control rather than addressing the underlying pathological mechanisms. We call for the development of more accurate and representative animal models to achieve better outcomes and examine the potential of emerging technologies, such as artificial intelligence and neuroimaging. In summary, this review synthesizes recent findings in hydrocephalus research, identifies promising therapeutic targets and interventions, and critically evaluates the limitations of current research paradigms, aiming to align preclinical studies with clinical endpoints. Continued studies and multidisciplinary collaboration are essential to develop effective interventions and facilitate new treatments into bedside.}, }
@article {pmid39904453, year = {2025}, author = {Liu, B and Wang, Y and Gao, L and Cai, Z}, title = {Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.}, journal = {Brain research}, volume = {1851}, number = {}, pages = {149484}, doi = {10.1016/j.brainres.2025.149484}, pmid = {39904453}, issn = {1872-6240}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Brain/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Imagination/physiology ; Attention/physiology ; }, abstract = {Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as nonlinearity, low signal-to-noise ratios, and large individual variations, present significant challenges for MI-EEG classification using traditional machine learning methods. To address these challenges, we propose an automatic feature extraction method rooted in deep learning for MI-EEG classification. First, original MI-EEG signals undergo noise reduction through discrete wavelet transform and common average reference. To reflect the regularity and specificity of brain neural activities, a convolutional neural network (CNN) is used to extract the time-domain features of MI-EEG. We also extracted spatial features to reflect the activity relationships and connection states of the brain in different regions. This process yields time series containing spatial information, focusing on enhancing crucial feature sequences through talking-heads attention. Finally, more abstract spatial-temporal features are extracted using a temporal convolutional network (TCN), and classification is done through a fully connected layer. Validation experiments based on the BCI Competition IV-2a dataset show that the enhanced EEG model achieves an impressive average classification accuracy of 85.53% for each subject. Compared with CNN, EEGNet, CNN-LSTM and EEG-TCNet, the classification accuracy of this model is improved by 11.24%, 6.90%, 11.18% and 6.13%, respectively. Our work underscores the potential of the proposed model to enhance intention recognition in MI-EEG significantly.}, }
@article {pmid39904383, year = {2025}, author = {Shi, CY and Zhang, HT and Tang, Z}, title = {Large-sized trees regulating the structural diversity-productivity relationships through shaping different productive processes in a tropical forest.}, journal = {Proceedings. Biological sciences}, volume = {292}, number = {2040}, pages = {20242202}, pmid = {39904383}, issn = {1471-2954}, support = {//National Natural Science Foundation of China/ ; }, mesh = {*Trees/physiology ; *Rainforest ; *Biodiversity ; Tropical Climate ; Biomass ; Forests ; }, abstract = {Forest structural diversity, a measurement indicating the spatial and size distribution of individual trees, is critical for forest productivity, which stems from the combination of different ecological processes, such as tree mortality, recruitment and growth. Here, we evaluated the relationship between structural diversity and productivity caused by different ecological processes, and tested the roles of different-sized trees in influencing this relationship in a Forest Global Earth Observatory (ForestGEO) rainforest site on the Barro Colorado Island between 2000 and 2015. Generally, we found a negative relationship between structural diversity and forest productivity. Specifically, tree mortality-induced productivity loss increased, while tree recruitment-induced productivity gain decreased, with structural diversity. In addition, the structural diversity-productivity relationship varied with tree size, which was negative for small trees but positive for large trees. Furthermore, we revealed the important role of large-sized trees, which significantly promoted structural diversity but decreased productivity through increasing biomass loss. By disentangling the components of productivity, our results provide insights on the mechanism of the relationship between structural diversity and productivity, and highlight the role of large trees in shaping this relationship.}, }
@article {pmid39902757, year = {2025}, author = {Wang, J and Luo, Y and Wang, H and Wang, L and Zhang, L and Gan, Z and Kang, X}, title = {FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adae34}, pmid = {39902757}, issn = {1741-2552}, mesh = {*Artifacts ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; }, abstract = {Objective.Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.Approach.In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.Main results.The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.Significance.The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.}, }
@article {pmid39901197, year = {2025}, author = {Wang, S and Ma, R and Gao, C and Tian, YN and Hu, RG and Zhang, H and Li, L and Li, Y}, title = {Unraveling the function of TSC1-TSC2 complex: implications for stem cell fate.}, journal = {Stem cell research &amp; therapy}, volume = {16}, number = {1}, pages = {38}, pmid = {39901197}, issn = {1757-6512}, support = {82073832//National Natural Science Foundation of China/ ; 82204885//National Natural Science Foundation of China/ ; 81973792//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Tuberous Sclerosis Complex 1 Protein/metabolism/genetics ; *Tuberous Sclerosis Complex 2 Protein/metabolism/genetics ; *Tuberous Sclerosis/metabolism/genetics/pathology ; *Cell Differentiation ; Animals ; Stem Cells/metabolism/cytology ; }, abstract = {BACKGROUND: Tuberous sclerosis complex is a genetic disorder caused by mutations in the TSC1 or TSC2 genes, affecting multiple systems. These genes produce proteins that regulate mTORC1 activity, essential for cell function and metabolism. While mTOR inhibitors have advanced treatment, maintaining long-term therapeutic success is still challenging. For over 20 years, significant progress has linked TSC1 or TSC2 gene mutations in stem cells to tuberous sclerosis complex symptoms.<br><br>METHODS: A comprehensive review was conducted using databases like Web of Science, Google Scholar, PubMed, and Science Direct, with search terms such as "tuberous sclerosis complex," "TSC1," "TSC2," "stem cell," "proliferation," and "differentiation." Relevant literature was thoroughly analyzed and summarized to present an updated analysis of the TSC1-TSC2 complex's role in stem cell fate determination and its implications for tuberous sclerosis complex.<br><br>RESULTS: The TSC1-TSC2 complex plays a crucial role in various stem cells, such as neural, germline, nephron progenitor, intestinal, hematopoietic, and mesenchymal stem/stromal cells, primarily through the mTOR signaling pathway.<br><br>CONCLUSIONS: This review aims shed light on the role of the TSC1-TSC2 complex in stem cell fate, its impact on health and disease, and potential new treatments for tuberous sclerosis complex.}, }
@article {pmid39900901, year = {2025}, author = {Derosiere, G and Shokur, S and Vassiliadis, P}, title = {Reward signals in the motor cortex: from biology to neurotechnology.}, journal = {Nature communications}, volume = {16}, number = {1}, pages = {1307}, pmid = {39900901}, issn = {2041-1723}, mesh = {*Motor Cortex/physiology ; *Reward ; Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Animals ; Learning/physiology ; }, abstract = {Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learning. In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. Understanding M1 reward signals may open new avenues for enhancing motor control and rehabilitation.}, }
@article {pmid39899980, year = {2025}, author = {Gusman, JT and Hosman, T and Crawford, R and Singer-Clark, T and Kapitonava, A and Kelemen, JN and Hahn, N and Henderson, JM and Hochberg, LR and Simeral, JD and Vargas-Irwin, CE}, title = {Multi-gesture drag-and-drop decoding in a 2D iBCI control task.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, pmid = {39899980}, issn = {1741-2552}, support = {R25 GM125500/GM/NIGMS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Gestures ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; *Motor Cortex/physiology ; *Psychomotor Performance/physiology ; Young Adult ; Quadriplegia/rehabilitation/physiopathology ; Hand/physiology ; Movement/physiology ; }, abstract = {Objective. Intracortical brain-computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'.Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.}, }
@article {pmid39899514, year = {2025}, author = {Chandravadia, N and Pendekanti, S and Roberts, D and Tran, R and Panchavati, S and Arnold, C and Pouratian, N and Speier, W}, title = {Comparing P300 flashing paradigms in online typing with language models.}, journal = {PloS one}, volume = {20}, number = {2}, pages = {e0303390}, pmid = {39899514}, issn = {1932-6203}, mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain-Computer Interfaces ; *Language ; Male ; Female ; Adult ; Young Adult ; }, abstract = {The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.}, }
@article {pmid39898793, year = {2025}, author = {Perkins, SM and Amematsro, EA and Cunningham, J and Wang, Q and Churchland, MM}, title = {An emerging view of neural geometry in motor cortex supports high-performance decoding.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {39898793}, issn = {2050-084X}, support = {1R01NS135240/NH/NIH HHS/United States ; T32 NS064929/NS/NINDS NIH HHS/United States ; T32 MH126036/MH/NIMH NIH HHS/United States ; R01 NS135240/NS/NINDS NIH HHS/United States ; SCGB//Simons Foundation/ ; Graduate Fellowship//National Science Foundation/ ; }, mesh = {*Brain-Computer Interfaces ; *Motor Cortex/physiology ; Humans ; Machine Learning ; Neurons/physiology ; Animals ; }, abstract = {Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT's computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT's performance and simplicity suggest it may be a strong candidate for many BCI applications.}, }
@article {pmid39898755, year = {2025}, author = {Andre, V and Abdel-Mottaleb, M and Shotbolt, M and Chen, S and Ramezini, Z and Zhang, E and Conlan, S and Telisman, O and Liang, P and Bryant, JM and Chomko, R and Khizroev, S}, title = {Foundational insights for theranostic applications of magnetoelectric nanoparticles.}, journal = {Nanoscale horizons}, volume = {10}, number = {4}, pages = {699-718}, pmid = {39898755}, issn = {2055-6764}, mesh = {Humans ; *Theranostic Nanomedicine/methods ; Drug Delivery Systems/methods ; Animals ; Neoplasms ; Nanoparticles/chemistry/therapeutic use ; Magnetite Nanoparticles/chemistry/therapeutic use ; Electroporation/methods ; }, abstract = {Reviewing emerging biomedical applications of MagnetoElectric NanoParticles (MENPs), this paper presents basic physics considerations to help understand the possibility of future theranostic applications. Currently emerging applications include wireless non-surgical neural modulation and recording, functional brain mapping, high-specificity cell electroporation for targeted cancer therapies, targeted drug delivery, early screening and diagnostics, and others. Using an ab initio analysis, each application is discussed from the perspective of its fundamental limitations. Furthermore, the review identifies the most eminent challenges and offers potential engineering solutions on the pathway to implement each application and combine the therapeutic and diagnostic capabilities of the nanoparticles.}, }
@article {pmid39896750, year = {2025}, author = {Angulo, IN and Iáñez, E and Ubeda, A}, title = {Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.}, journal = {Frontiers in neuroinformatics}, volume = {19}, number = {}, pages = {1543103}, pmid = {39896750}, issn = {1662-5196}, }
@article {pmid39896041, year = {2025}, author = {Sarikaya, MA and Ince, G}, title = {Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.}, journal = {PeerJ. Computer science}, volume = {11}, number = {}, pages = {e2649}, pmid = {39896041}, issn = {2376-5992}, abstract = {The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. We benchmark the efficacy of three generative adversarial network (GAN) architectures, such as conditional GAN (CGAN), conditional Wasserstein GAN (CWGAN), and CWGAN with gradient penalty (CWGAN-GP) within this framework. The proposed framework is rigorously tested on two conventional open sourced datasets, SEED-V and DEAP. Additionally, the framework was applied to an immersive three-dimensional (3D) dataset named GraffitiVR, which we collected to capture the emotional and behavioral reactions of individuals experiencing urban graffiti in a VR environment. This expanded application provides insights into emotion recognition frameworks in VR settings, providing a wider range of contexts for assessing our methodology. When the accuracy of emotion recognition classifiers trained with CWGAN-GP-generated EEG data combined with non-EEG sensory data was compared against those trained using a combination of real EEG and non-EEG sensory data, the accuracy ratios were 93% on the SEED-V dataset, 99% on the DEAP dataset, and 97% on the GraffitiVR dataset. Moreover, in the GraffitiVR dataset, using CWGAN-GP-generated EEG data with non-EEG sensory data for emotion recognition models resulted in up to a 30% reduction in calibration time compared to classifiers trained on real EEG data with non-EEG sensory data. These results underscore the robustness and versatility of the proposed approach, significantly enhancing emotion recognition processes across a variety of environmental settings.}, }
@article {pmid39890467, year = {2025}, author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL}, title = {Mapping Eye, Arm, and Reward Information in Frontal Motor Cortices Using Electrocorticography in Nonhuman Primates.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {45}, number = {12}, pages = {}, pmid = {39890467}, issn = {1529-2401}, support = {K12 HD073945/HD/NICHD NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; }, mesh = {Animals ; Male ; *Electrocorticography/methods ; *Motor Cortex/physiology ; *Reward ; *Macaca mulatta ; *Eye Movements/physiology ; *Brain Mapping/methods ; *Psychomotor Performance/physiology ; Arm/physiology ; Frontal Lobe/physiology ; Movement/physiology ; }, abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral prefrontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the prefrontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.}, }
@article {pmid39889306, year = {2025}, author = {Li, D and Huang, Y and Luo, R and Zhao, L and Xiao, X and Wang, K and Yi, W and Xu, M and Ming, D}, title = {Enhancing detection of SSVEPs using discriminant compacted network.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adb0f2}, pmid = {39889306}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography/methods ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; Deep Learning ; Discriminant Analysis ; Photic Stimulation/methods ; }, abstract = {Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.Approach.This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning (DL). Specifically, this study enhanced SSVEP features using global template alignment and discriminant spatial pattern, and then designed a compacted temporal-spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.Main Results.The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, DL methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset. The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.Significance.This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.}, }
@article {pmid39887443, year = {2025}, author = {Li, Z and Liang, C and He, Q and Feiweier, T and Hsu, YC and Li, J and Bai, R}, title = {Comparison of water exchange measurements between filter-exchange imaging and diffusion time-dependent kurtosis imaging in the human brain.}, journal = {Magnetic resonance in medicine}, volume = {93}, number = {6}, pages = {2357-2369}, doi = {10.1002/mrm.30454}, pmid = {39887443}, issn = {1522-2594}, support = {82111530201//National Natural Science Foundation of China (NSFC)/ ; 82222032//National Natural Science Foundation of China (NSFC)/ ; 82172050//National Natural Science Foundation of China (NSFC)/ ; 2022ZD0206000//the STI2030-Major Projects of China/ ; }, mesh = {Humans ; Male ; Adult ; *Brain/metabolism/diagnostic imaging ; Female ; *Body Water/metabolism ; *Diffusion Magnetic Resonance Imaging/methods ; Reproducibility of Results ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; *Water/metabolism ; }, abstract = {PURPOSE: Filter-exchange imaging (FEXI) and diffusion time (t)-dependent kurtosis imaging (DKI(t)) are two diffusion-based methods that have been proposed for in vivo measurements of water exchange rates. Few studies have directly compared these methods. We aimed to investigate whether FEXI and DKI(t) yield comparable water exchange measurements in the human brain in vivo.<br><br>METHODS: Eight healthy volunteers underwent multiple-direction FEXI and DKI(t) acquisitions on a 3T scanner. We performed region of interest (ROI) analysis to determine correlations between FEXI-derived apparent exchange rate (AXR) and DKI(t)-derived reciprocal of exchange time (1 / &#x3c4; ex $$ 1/{\tau}_{ex} $$).<br><br>RESULTS: In both white matter (WM) and gray matter (GM), DKI(t) revealed substantial diffusion-time dependence of diffusivity and kurtosis. However, at t&#x2009;&#x2265;&#x2009;100&#x2009;ms, the diffusivity showed weak time dependence. In WM, this time dependence may be due to water exchange between myelin water and "free" water with different T1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot be excluded. We found a significant correlation between DKI(t)-derived 1 / &#x3c4; ex $$ 1/{\tau}_{ex} $$ and FEXI-derived AXR in the axial direction within WM. No such correlation was present in GM, although both values showed similar ranges.<br><br>CONCLUSION: These results suggest that FEXI and DKI(t) could be sensitive to the same water exchange process only when the diffusion time in DKI(t) is sufficiently long, and only in WM. In both GM and WM, the restrictive effect of microstructure is non-negligible, especially at short diffusion times (<100&#x2009;ms).}, }
@article {pmid39886803, year = {2025}, author = {Ali, HS and Ismail, AI and El-Rabaie, EM and Abd El-Samie, FE}, title = {Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2025.2457122}, pmid = {39886803}, issn = {1476-8259}, abstract = {The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit. Moreover, high-dimensional, and irrelevant features can make it harder for a classifier to learn effectively. To address these challenges, exploring potential solutions is crucial. This paper introduces Regularized CSP with diagonal loading (DL-CSP) and Pearson correlation coefficient (PCC) based feature selection to extract the most discriminative motor imagery EEG (MI-EEG) features. Three classifiers in an ensemble are considered; bidirectional long short-term memory (Bi-LSTM), K-nearest neighbors (KNN) and naïve Bayes (NB). Decision level fusion through majority voting is exploited to leverage diverse perspectives and increase the overall system robustness. Experiments have been implemented using three publicly available datasets for MI classification; BCI competition IV-IIA (data-1), BCI Competition III-IVa (data-2), and a stroke patients' dataset (data-3). The accuracy achieved, according to the results, is 86.96% for data-1, 91.70% for data-2, and 85.75% for data-3. These percentages outperform the accuracy achieved by any state-of-the-art techniques.}, }
@article {pmid39886225, year = {2025}, author = {Huang, J and Zhang, L and Shao, N and Zhang, Y and Xu, Y and Zhou, Y and Zhang, D and Zhang, J and Lee, HJ}, title = {Lipid Metabolic Heterogeneity during Early Embryogenesis Revealed by Hyper-3D Stimulated Raman Imaging.}, journal = {Chemical &amp; biomedical imaging}, volume = {3}, number = {1}, pages = {15-24}, pmid = {39886225}, issn = {2832-3637}, abstract = {Studying embryogenesis is fundamental to understanding developmental biology and reproductive medicine. Its process requires precise spatiotemporal regulations in which lipid metabolism plays a crucial role. However, the spatial dynamics of lipid species at the subcellular level remains obscure due to technical limitations. To address this challenge, we developed a hyperspectral 3D imaging and analysis method based on stimulated Raman scattering microscopy (hyper-3D SRS) to quantitatively assess lipid profiles in individual embryos through submicrometer resolution (x-y), 3D optical sectioning (z), and chemical bond-selective (Ω) imaging. Using hyper-3D SRS, individual lipid droplets (LDs) in single cells were identified and quantified. Our findings revealed that the LD profiles within a single embryo are not uniform, even as early as the 2-cell stage. Notably, we also discovered a dynamic relationship between the LD size and unsaturation degree as embryos develop, indicating diverse lipid metabolism during early development. Furthermore, abnormal LDs were observed in oocytes of a progeria mouse model, suggesting that LDs could serve as a potential biomarker for assessing oocyte/embryo quality. Overall, our results highlight the potential of hyper-3D SRS as a noninvasive method for studying lipid content, composition, and subcellular distribution in embryos. This technique provides valuable insights into lipid metabolism during embryonic development and has the potential for clinical applications in evaluating oocyte/embryo quality.}, }
@article {pmid39886177, year = {2024}, author = {Murthy, V and Kashid, SR and Pal, M and Vadassery, A and Maitre, P and Arora, A and Singh, P and Joshi, A and Bakshi, G and Prakash, G}, title = {Prospective comparative study of quality of life in patients with bladder cancer undergoing cystectomy with ileal conduit or bladder preservation.}, journal = {BMJ oncology}, volume = {3}, number = {1}, pages = {e000435}, pmid = {39886177}, issn = {2752-7948}, abstract = {OBJECTIVE: To compare health-related quality of life (HRQOL) in patients undergoing radical cystectomy with ileal conduit (RC) or bladder preservation (BP) with (chemo)radiotherapy for bladder cancer.<br><br>METHODS AND ANALYSIS: Patients with bladder cancer, stage cT1-T4, cN0-N1, M0 with a minimum follow-up of 6 months from curative treatment (RC or BP) and without disease were eligible for inclusion. Two HRQOL instruments were administered: Bladder Cancer Index (BCI) for bladder cancer-specific HRQOL and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). The mean QOL scores across various domains and specific questions were compared between the two treatment groups using an independent t-test.<br><br>RESULTS: Out of the 104 enrolled patients, 56 underwent RC and 48 opted for BP, with 95 (91.3%) being male. The median time from treatment completion to QOL assessment was 22 months (IQR 10-56). The median age for the entire cohort was 62 years (IQR 55-68), 65.5 years (IQR 55-71) in BP and 59.5 years (IQR 55-66) in RC. There was no significant difference in mean BCI urinary and bowel scores in function or bother subdomains between the two groups. Overall, BCI sexual scores were low in both groups but significantly better after BP (BPmean 56.9, RCmean 41.5, p=0.01). Mean scores for sexual function subdomain were BPmean 38.4 and RCmean 25 (p=0.07) and for sexual bother were BPmean 81 RCmean 62 (p=0.02). The EORTC QLQ-C30 outcomes did not show a significant difference in either group.<br><br>CONCLUSION: The BP group showed significantly better results in the sexual domain compared with the RC group. Both groups had good QOL in terms of urinary and bowel functions.}, }
@article {pmid39883960, year = {2025}, author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, JL and Hatsopoulos, NG and Downey, JE}, title = {How different immersive environments affect intracortical brain computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, pmid = {39883960}, issn = {1741-2552}, support = {R01 NS130302/NS/NINDS NIH HHS/United States ; T32 NS121763/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Virtual Reality ; *Electrodes, Implanted ; Female ; Electroencephalography/methods ; Adult ; Environment ; }, abstract = {Objective. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device.Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor.Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802.}, }
@article {pmid39883957, year = {2025}, author = {Lutes, NA and Sriram Siddhardh Nadendla, V and Krishnamurthy, K}, title = {Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adb079}, pmid = {39883957}, issn = {1741-2552}, mesh = {Humans ; *Neural Networks, Computer ; *Intention ; Automobile Driving ; Male ; Electroencephalography/instrumentation/methods ; Adult ; Machine Learning ; Female ; Transfer, Psychology/physiology ; }, abstract = {Objective.This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention.Approach.Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: (1) an audio countdown in a nominal baseline, stress-free environment; (2) an audio countdown in an environment with added elements of physical fatigue and active cognitive distraction; (3) a visual cue given through stoplights in a stress-free environment. These datasets are then used to develop individual-level models from group-level models using a few-shot transfer learning method, which involves: (1) creating a group-level model by training a CNN on group-level data followed by quantization and recouping any performance loss using quantization-aware retraining; (2) converting the CNN to be compatible with Akida AKD1000 processor; and (3) training the final decision layer on individual-level data subsets to create individual-customized models using an online Akida edge-learning algorithm.Main results.Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a 1.3 × increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.Significance.Especially relevant to real-time applications, this work presents an energy-efficient, few-shot transfer learning method that is implemented on a neuromorphic processor capable of training a CSNN as new data becomes available, operating conditions change, or to customize group-level models to yield personalized models unique to each individual.}, }
@article {pmid39883956, year = {2025}, author = {Sultana, M and Gheorghe, L and Perdikis, S}, title = {EEG correlates of acquiring race driving skills.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adb077}, pmid = {39883956}, issn = {1741-2552}, mesh = {Humans ; *Automobile Driving ; *Electroencephalography/methods ; Male ; *Transcranial Direct Current Stimulation/methods ; Adult ; Female ; Young Adult ; Motor Skills/physiology ; Psychomotor Performance/physiology ; Learning/physiology ; }, abstract = {Objective. Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring. Second, we gather evidence on the potential role of transcranial direct current stimulation (tDCS) in enhancing the training outcome of race drivers.Approach. We collected and analyzed multimodal experimental data, including drivers' EEG and telemetry from a driving simulator to identify neuromarkers of race driving proficiency and assess the potential to improve training through anodal tDCS.Main results. Our findings indicate that theta-band EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are significant neuromarkers for acquiring racing skills. We also observed signs of a potential tDCS effect in accelerating the learning process.SignificanceThese results provide a foundation for future research to develop innovative race-driving training protocols using neurotechnology.}, }
@article {pmid39883372, year = {2025}, author = {Xiong, H and Yan, Y and Chen, Y and Liu, J}, title = {Graph convolution network-based eeg signal analysis: a review.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {39883372}, issn = {1741-0444}, abstract = {With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN. An exhaustive classification of methods and a systematic analysis of key modules, such as brain map construction, node feature extraction, and GCN architecture design, are presented. In addition, we pay special attention to several key research issues related to GCN. This review enhances the understanding of the future potential of GCN in the field of EEG signal analysis. At the same time, several valuable development directions are sorted out for researchers in related fields, such as analysing the applicability of different GCN layers, building task-oriented GCN models, and improving adaptation to limited data.}, }
@article {pmid39882377, year = {2024}, author = {Ma, Y and Huang, J and Liu, C and Shi, M}, title = {A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1502560}, pmid = {39882377}, issn = {1662-5218}, abstract = {Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.}, }
@article {pmid39880895, year = {2025}, author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A}, title = {Author Correction: Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41551-025-01355-2}, pmid = {39880895}, issn = {2157-846X}, support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; }, }
@article {pmid39880008, year = {2025}, author = {Loss, J and Betsch, C and Ellermann, C and Ewert, B and Grill, E and Jenny, MA and Jordan, S and Kubiak, T and Mata, J and Rebitschek, FG and Rehfuess, EA and Sniehotta, F}, title = {[Characteristics of the "Behavioural and cultural insights (BCI)" approach in Public Health - a consensus paper of the network "Behavioural Science Connect"].}, journal = {Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))}, volume = {87}, number = {4}, pages = {273-281}, doi = {10.1055/a-2528-8276}, pmid = {39880008}, issn = {1439-4421}, mesh = {Germany ; *Public Health ; Humans ; *Health Promotion/organization &amp; administration ; *Health Behavior ; *Behavioral Sciences/organization &amp; administration ; }, abstract = {Public health interventions are often intended to make it easier for people to adopt health-related behaviours, e. g., by changing the social environment and material living conditions. With the concept of "Behavioral and Cultural Insights" (BCI), the World Health Organization (WHO) has established an approach that aims at a better understanding of health-related behaviours, in order to be able to develop corresponding public health interventions in a more targeted manner. The focus is on the empirical identification of individuals as well as cultural, social and environmental barriers to and facilitators of health behaviour.The BCI approach can be used to plan preventive measures in a more evidence-based and needs-oriented manner. To this end, the current article outlines some basic features of BCI which should be taken into account for integrating the concept into a contemporary understanding of "new public health". This includes social and individual factors influencing health as well as social inequalities in health.First, the article distinguishes the BCI concept from the approach of behavioural economics (e. g., nudging). To illustrate its potential for population health and health equity, the article then explains that BCI-based measures that (a) focus on both behaviour and environment, (b) aim at health equity, (c) are developed and implemented in a participatory manner, and (d) follow the logic of the well-established Public Health Action Cycle. For BCI, it is crucial to systematically identify and analyse the factors influencing human behaviour in everyday life. BCI-based interventions must also consider the characteristics of complex interventions and be tailored to local conditions and the cultural diversity of specific population groups.The BCI approach has many similarities with other approaches of quality-assured and needs-oriented prevention measures. The focus on a systematic identification of barriers and facilitators offers an important added value in the planning of public health measures. Research into BCI and their use in prevention should be expanded in Germany.}, }
@article {pmid39878111, year = {2025}, author = {Badr, Y and AlSawaftah, N and Husseini, G}, title = {User-Centered Design of Neuroprosthetics: Advancements and Limitations.}, journal = {CNS &amp; neurological disorders drug targets}, volume = {}, number = {}, pages = {}, doi = {10.2174/0118715273335487250102093150}, pmid = {39878111}, issn = {1996-3181}, abstract = {Neurological conditions resulting from severe spinal cord injuries, brain injuries, and other traumatic incidents often lead to the loss of essential bodily functions, including sensory and motor capabilities. Traditional prosthetic devices, though standard, have limitations in delivering the required dexterity and functionality. The advent of neuroprosthetics marks a paradigm shift, aiming to bridge the gap between prosthetic devices and the human nervous system. This review paper explores the evolution of neuroprosthetics, categorizing devices into sensory and motor neuroprosthetics and emphasizing their significance in addressing specific challenges. The discussion section delves into long-term challenges in clinical practice, encompassing device durability, ethical considerations, and issues of accessibility and affordability. Furthermore, the paper proposes potential solutions with a specific focus on enhancing sensory experiences and the importance of user-friendly interfaces. In conclusion, this paper offers a comprehensive overview of the current state of neuroprosthetics, outlining future research and development directions to guide advancements in the field.}, }
@article {pmid39875198, year = {2025}, author = {Chen, S and Jiang, D and Li, M and Xuan, X and Li, H}, title = {Brain-Computer Interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection.}, journal = {ACS sensors}, volume = {10}, number = {2}, pages = {868-880}, doi = {10.1021/acssensors.4c02461}, pmid = {39875198}, issn = {2379-3694}, mesh = {*Graphite/chemistry ; *Dopamine/analysis ; *Microelectrodes ; *Electroencephalography/instrumentation/methods ; *Nitrogen/chemistry ; *Boron/chemistry ; *Electrochemical Techniques/methods/instrumentation ; Humans ; *Brain-Computer Interfaces ; Biosensing Techniques/methods/instrumentation ; }, abstract = {The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 μm for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.}, }
@article {pmid39875073, year = {2025}, author = {Géraudie, A and De Rossi, P and Canney, M and Carpentier, A and Delatour, B}, title = {Effects of blood-brain barrier opening using ultrasound on tauopathies: A systematic review.}, journal = {Journal of controlled release : official journal of the Controlled Release Society}, volume = {379}, number = {}, pages = {1029-1044}, doi = {10.1016/j.jconrel.2025.01.056}, pmid = {39875073}, issn = {1873-4995}, mesh = {*Blood-Brain Barrier/metabolism ; Humans ; Animals ; *Tauopathies/diagnostic imaging/metabolism ; *tau Proteins/metabolism ; Ultrasonic Therapy/methods ; }, abstract = {UNLABELLED: Blood-brain barrier opening with ultrasound can potentiate drug efficacy in the treatment of brain pathologies and also provides therapeutic effects on its own. It is an innovative tool to transiently, repeatedly and safely open the barrier, with studies showing beneficial effects in both preclinical models for Alzheimer's disease and recent clinical studies. The first preclinical and clinical work has mainly shown a decrease in amyloid burden in mice models and in patients. However, Alzheimer's disease pathology also encompasses tauopathy, which is closely related to cognitive decline, making it a crucial therapeutic target. The effects of blood-brain barrier opening with ultrasound have been rarely assessed on tau and are still unclear.<br><br>METHODS: This systematic review, conducted through searches using Pubmed, Embase, Web of Science and Cochrane Central databases, extracted results of 15 studies reporting effects of blood-brain barrier opening using ultrasound on tau proteins.<br><br>RESULTS: This review of the literature indicates that blood-brain barrier opening using ultrasound can decrease the extent of the tau pathology or potentialize the effect of a therapeutic drug. However, selected studies report paradoxically that blood-brain barrier opening can increase tau pathology burden and induce brain damage.<br><br>DISCUSSION: Apparent discrepancies between reports could originate from the variability in protocols or analytical methods that may impact the effects of blood-brain barrier opening with ultrasound on tauopathies, glial populations, tissue integrity and functional outcomes.<br><br>CONCLUSION: This calls for a better standardization effort combined with improved methodologies allowing between-studies comparisons, and for further understanding of the effects of blood-brain barrier opening on tau pathology as an essential prerequisite before translation to clinic.}, }
@article {pmid39874948, year = {2025}, author = {Tian, F and Liu, Y and Chen, M and Schriver, KE and Roe, AW}, title = {Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI.}, journal = {Cell reports methods}, volume = {5}, number = {1}, pages = {100961}, pmid = {39874948}, issn = {2667-2375}, mesh = {*Magnetic Resonance Imaging/methods ; *Visual Cortex/physiology/diagnostic imaging ; Humans ; Animals ; Photic Stimulation/methods ; Cats ; Infrared Rays ; Brain Mapping/methods ; }, abstract = {To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.}, }
@article {pmid39874664, year = {2025}, author = {Qin, Y and Li, B and Wang, W and Shi, X and Peng, C and Wang, X and Wang, H}, title = {ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaf58}, pmid = {39874664}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Movement/physiology ; }, abstract = {Objective. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.Approach. In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.Main results. We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.Significance. ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.}, }
@article {pmid39874652, year = {2025}, author = {Ziebell, P and Modde, A and Roland, E and Eidel, M and Vansteensel, MJ and Mrachacz-Kersting, N and Vaughan, TM and Kübler, A}, title = {Designing an online BCI forum: insights from researchers and end-users.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaf57}, pmid = {39874652}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Male ; Female ; Adult ; Online Systems ; Research Personnel ; Middle Aged ; Persons with Disabilities/rehabilitation ; Internet ; }, abstract = {Objective.Brain-computer interfaces (BCIs) can support non-muscular communication and device control for severely paralyzed people. However, efforts that directly involve potential or actual end-users and address their individual needs are scarce, demonstrating a translational gap. An online BCI forum supported by the BCI Society could initiate and sustainably strengthen interactions between BCI researchers and end-users to bridge this gap.Approach.We interviewed six severely paralyzed individuals and surveyed 121 BCI researchers to capture their opinions and wishes concerning an online BCI forum. Data were analyzed with a mixed-method quantitative and qualitative content analysis.Main results.All end-users and most researchers (83%) reported an interest in participating in an online BCI forum. Rating questions and open comments to identify design aspects included what should be featured most prominently, how people would get engaged in the online BCI forum, and which pitfalls should be considered.Significance.Responses support establishing an online BCI forum to serve as a meaningful resource for the entire BCI community.}, }
@article {pmid39874276, year = {2025}, author = {Longo, L and Reilly, RB}, title = {onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0313076}, pmid = {39874276}, issn = {1932-6203}, mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Signal-To-Noise Ratio ; Male ; Brain/physiology ; Adult ; Female ; }, abstract = {Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance. However, they often require multi-channel information and additional reference signals, are not fully automated, require human intervention and are mostly offline. With the popularity of Brain-Computer Interfaces and the application of Electroencephalography in daily activities and other ecological settings, there is an increasing need for robust, online, near real-time denoising techniques, without additional reference signals, that is fully automated and does not require human supervision nor multi-channel information. This research contributes to the body of knowledge by introducing onEEGwaveLAD, a novel, fully automated, ONline, EEG wavelet-based Learning Adaptive Denoiser pipeline for artefact identification and reduction. It is a specific framework that can be instantiated for various types of artefacts paving the path towards real-time denoising. As the first of its kind, it is described and instantiated for the particular problem of blink detection and reduction, and evaluated across a general and a specific analysis of the signal to noise ratio across 30 participants.}, }
@article {pmid39873797, year = {2025}, author = {Mohan, A and Anand, RS}, title = {Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.}, journal = {Brain topography}, volume = {38}, number = {2}, pages = {25}, pmid = {39873797}, issn = {1573-6792}, mesh = {Humans ; *Machine Learning ; *Speech/physiology ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; }, abstract = {EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.}, }
@article {pmid39872211, year = {2025}, author = {Wang, L and Zhang, C and Hao, Z and Yao, S and Bai, L and Oliveira, JM and Wang, P and Zhang, K and Zhang, C and He, J and Reis, RL and Li, D}, title = {Bioaugmented design and functional evaluation of low damage implantable array electrodes.}, journal = {Bioactive materials}, volume = {47}, number = {}, pages = {18-31}, pmid = {39872211}, issn = {2452-199X}, abstract = {Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. In this study, we proposed a new concept for the design and bioaugmentation of implantable electrodes (bio-array electrodes) featuring a heterogeneous gradient structure. Different composite polyaniline-gelatin-alginate based conductive hydrogel formulations were developed for electrode surface coating. In addition, the design, materials, and performance of the developed electrode was optimized through a combination of numerical simulations and physio-chemical characterizations. The long-term biological performance of the bio-array electrodes were investigated in vivo using a C57 mouse model. It was found that compared to metal array electrodes, the surface charge of the bio-array electrodes increased by 1.74 times, and the impedance at 1 kHz decreased by 63.17 %, with a doubling of the average capacitance. Long-term animal experiments showed that the bio-array electrodes could consistently record 2.5 times more signals than those of the metal array electrodes, and the signal-to-noise ratio based on action potentials was 2.1 times higher. The study investigated the mechanisms of suppressing the scarring effect by the bioaugmented design, revealing reduces brain damage as a result of the interface biocompatibility between the bio-array electrodes and brain tissue, and confirmed the long-term in vivo stability of the bio-array electrodes.}, }
@article {pmid39870740, year = {2025}, author = {Hu, D and Sato, T and Rockland, KS and Tanifuji, M and Tanigawa, H}, title = {Relationship between functional structures and horizontal connections in macaque inferior temporal cortex.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {3436}, pmid = {39870740}, issn = {2045-2322}, support = {31872776//National Natural Science Foundation of China/ ; 2018YFA0701402//National Key Research and Development Program of China/ ; }, mesh = {Animals ; *Temporal Lobe/physiology ; Visual Cortex/physiology ; Male ; Macaca mulatta ; Axons/physiology ; Photic Stimulation ; Brain Mapping/methods ; Macaca ; Visual Pathways/physiology ; }, abstract = {Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity. While some axon terminal patches shared responsiveness to specific visual features with the injection site, many connected to regions with different selectivity. Our results suggest that horizontal connections in anterior ITC exhibit diverse functional connectivity, potentially supporting flexible integration of visual information for advanced object recognition processes.}, }
@article {pmid39870272, year = {2025}, author = {Abedi, M and Arbabi, M and Gholampour, R and Amini, J and Barandeh, Z and Hosseini, S and Abedi, A and Gholibegloo, E and Zomorrodian, H and Raoufi, M}, title = {Zinc oxide nanoparticle-embedded tannic acid/chitosan-based sponge: A highly absorbent hemostatic agent with enhanced antimicrobial activity.}, journal = {International journal of biological macromolecules}, volume = {300}, number = {}, pages = {140337}, doi = {10.1016/j.ijbiomac.2025.140337}, pmid = {39870272}, issn = {1879-0003}, mesh = {*Chitosan/chemistry/pharmacology ; *Zinc Oxide/chemistry/pharmacology ; Animals ; *Hemostatics/pharmacology/chemistry ; Rats ; *Tannins/chemistry/pharmacology ; Anti-Infective Agents/pharmacology/chemistry ; Nanoparticles/chemistry ; Blood Coagulation/drug effects ; Male ; Epichlorohydrin/chemistry ; Escherichia coli/drug effects ; Staphylococcus aureus/drug effects ; Polyphenols ; }, abstract = {This study reports the development of a highly absorbent Chitosan (CS)/Tannic Acid (TA) sponge, synthesized via chemical cross-linking with Epichlorohydrin (ECH) and integrated with zinc oxide nanoparticles (ZnO NPs) as a novel hemostatic anti-infection agent. The chemical properties of the sponges were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and zeta potential measurements. Morphological and elemental analyses conducted through scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) revealed a uniform distribution of ZnO NPs, with particle sizes below 20 nm. Compression tests indicated that the incorporation of ECH enhanced the compressive strength of the TA/CS sample, increasing from 0.614 MPa to 1.03 MPa for TA/CS-ECH and 1.16 MPa for ZnO@TA/CS-ECH, while preserving its flexibility. ZnO@TA/CS-ECH sponges exhibited high swelling ratios, consistent with their mesoporous structure revealed by porosity analysis. MTT assays confirmed that the addition of ECH did not adversely affect the biocompatibility of the final ZnO@TA/CS-ECH sample. Hemostatic performance was assessed through prothrombin time (PT), activated partial thromboplastin time (aPTT), blood clotting index (BCI), blood clotting time (BCT) assays, and platelet adhesion imaging. ZnO@TA/CS-ECH significantly reduced the BCT of untreated blood from 349 to 49 s, outperforming Celox™ (182 s). This performance was further confirmed using a rat liver hemostatic model. Moreover, ZnO@TA/CS-ECH demonstrated substantial antimicrobial activity against E. coli, S. aureus, and C. albicans, comparable to standard antibiotics and antifungals. These findings suggest that the three-dimensional ZnO@TA/CS-ECH sponge holds promise in managing infected bleeding and inspiring the next-generation of hemostatic agents.}, }
@article {pmid39870046, year = {2025}, author = {Zhang, L and Zhang, H and Yan, S and Li, R and Yao, D and Hu, Y and Zhang, R}, title = {Improving pre-movement patterns detection with multi-dimensional EEG features for readiness potential decrease.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaef2}, pmid = {39870046}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Movement/physiology ; Male ; *Brain-Computer Interfaces ; Adult ; Female ; Young Adult ; Algorithms ; Support Vector Machine ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; }, abstract = {Objective.The readiness potential (RP) is an important neural characteristic in motor preparation-based brain-computer interface. In our previous research, we observed a significant decrease of the RP amplitude in some cases, which severely affects the pre-movement patterns detection. In this paper, we aimed to improve the accuracy (Acc) of pre-movement patterns detection in the condition of RP decrease.Approach.We analyzed multi-dimensional EEG features in terms of time-frequency, brain networks, and cross-frequency coupling (CFC). And, a multi-dimensional Electroencephalogram feature combination (MEFC) algorithm was proposed. The features used include: (1) waveforms of the RP; (2) energy in alpha and beta bands; (3) brain network in alpha and beta bands; and (4) CFC value between 2 and 10 Hz.Main results.By employing support vector machines, the MEFC method achieved an average recognition rate of 88.9% and 85.5% under normal and RP decrease conditions, respectively. Compared to classical algorithm, the average Acc for both tasks improved by 7.8% and 8.8% respectively.Significance.This method can effectively improve the Acc of pre-movement patterns decoding in the condition of RP decrease.}, }
@article {pmid39870043, year = {2025}, author = {Candelori, B and Bardella, G and Spinelli, I and Ramawat, S and Pani, P and Ferraina, S and Scardapane, S}, title = {Spatio-temporal transformers for decoding neural movement control.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaef0}, pmid = {39870043}, issn = {1741-2552}, mesh = {Animals ; *Movement/physiology ; *Motor Cortex/physiology ; *Macaca mulatta ; Neural Networks, Computer ; Neurons/physiology ; Male ; Action Potentials/physiology ; Models, Neurological ; Deep Learning ; Psychomotor Performance/physiology ; }, abstract = {Objective. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activityin vivoremains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results.Approach. To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex of non-human primates performing a motor inhibition task.Main results. The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses.Significance. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.}, }
@article {pmid39870041, year = {2025}, author = {Konrad, P and Gelman, KR and Lawrence, J and Bhatia, S and Jacqueline, D and Sharma, R and Ho, E and Byun, YW and Mermel, CH and Rapoport, BI}, title = {First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1024 electrodes.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/adaeed}, pmid = {39870041}, issn = {1741-2552}, mesh = {Humans ; *Microelectrodes ; *Brain Mapping/methods/instrumentation ; Male ; Female ; Adult ; Middle Aged ; Electrocorticography/methods/instrumentation ; Cerebral Cortex/physiology ; Aged ; Electrodes, Implanted ; Brain Neoplasms/surgery ; Brain-Computer Interfaces ; }, abstract = {Objective.Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways.Approach. Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study.Main results. Eight patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Four patients underwent motor mapping using somatosensory evoked potentials (SSEPs) while under general anesthesia, and four underwent awake language mapping, using both standard paradigms and the novel microelectrode array. SSEP phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event.Significance. These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.}, }
@article {pmid39869167, year = {2025}, author = {Sun, L and Duan, S}, title = {The Paraventricular Hypothalamus: A Sorting Center for Visceral and Somatic Pain.}, journal = {Neuroscience bulletin}, volume = {41}, number = {4}, pages = {731-733}, pmid = {39869167}, issn = {1995-8218}, }
@article {pmid39866662, year = {2025}, author = {Chen, L and Hu, Y and Wang, Z and Zhang, L and Jian, C and Cheng, S and Ming, D}, title = {Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {35}, pmid = {39866662}, issn = {1871-4080}, abstract = {Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.}, }
@article {pmid39866661, year = {2025}, author = {Mathumitha, R and Maryposonia, A}, title = {Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {32}, pmid = {39866661}, issn = {1871-4080}, abstract = {Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.}, }
@article {pmid39866660, year = {2025}, author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y}, title = {Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {31}, pmid = {39866660}, issn = {1871-4080}, abstract = {The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.}, }
@article {pmid39866658, year = {2025}, author = {Zhou, Y and Song, Y and Song, X and He, F and Xu, M and Ming, D}, title = {Review of directional leads, stimulation patterns and programming strategies for deep brain stimulation.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {33}, pmid = {39866658}, issn = {1871-4080}, abstract = {Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.}, }
@article {pmid39862529, year = {2025}, author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z}, title = {RMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {185}, number = {}, pages = {107133}, doi = {10.1016/j.neunet.2025.107133}, pmid = {39862529}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Evoked Potentials, Visual/physiology ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; }, abstract = {Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals. Specifically, the long-length EEG signals and short-length EEG signals are decoded into the frequency representation by the teacher and student models, respectively. At the feature-level, the frequency-masked generation distillation is designed to improve the representation ability of student features by forcing the randomly masked student features to generate full teacher features. At the logit-level, the non-target class knowledge distillation and the inter-class relation distillation are combined to mitigate loss conflicts by imitating the distribution of non-target classes and preserve the inter-class relation in the prediction vectors of the teacher and student models. We conduct comprehensive experiments on two public SSVEP datasets in the subject-independent scenario with six different signal lengths. The extensive experimental results demonstrate that the proposed RMKD method has significantly improved the decoding performance of short-length EEG signals in SSVEP-based BCI systems.}, }
@article {pmid39860813, year = {2025}, author = {Mallat, S and Hkiri, E and Albarrak, AM and Louhichi, B}, title = {A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {2}, pages = {}, pmid = {39860813}, issn = {1424-8220}, support = {IMSIU-RG23077//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Algorithms ; }, abstract = {Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities.}, }
@article {pmid39860606, year = {2025}, author = {Baruch, Y and Barba, M and Cola, A and Frigerio, M}, title = {The Role of Anterior Vaginal Prolapse in Co-Existent Underactive Overactive Bladder Syndrome-A Retrospective Cohort Study.}, journal = {Journal of clinical medicine}, volume = {14}, number = {2}, pages = {}, pmid = {39860606}, issn = {2077-0383}, abstract = {Background: CUOB (co-existent underactive overactive bladder) syndrome is a clinical entity that embraces storage and emptying symptoms, not strictly correlated with urodynamic findings. We assessed the differences between patients diagnosed with CUOB with/without cystocele. Methods: The study group was allocated from 2000 women who underwent urodynamic studies between 2008 and 2016. The demographic and clinical data of 369 patients with complaints consistent with CUOB were retrieved. The study group was subdivided using the Pelvic Organ Prolapse Quantification System. The International Consultation on Incontinence Questionnaire Short Form (ICIQ-UI SF) was used to quantify LUTS severity. Results: A total of 185 women had no or grade I cystocele (group 1), and 185 had grade II or III cystocele (group 2). No difference in mean age was computed. Patients from group 1 had a higher BMI (27 vs. 25, p = 0.02). Risk factors for prolapse, such as parity (1.7 vs. 2.1, p = 0.001) and maximal birthweight (3460 g vs. 3612 g, p = 0.049), were higher in group 2. Pelvic Organ Prolapse symptoms were 4.5 times more frequent in group 2 [n = 36/185 (19.5%) vs. n = 162/184 (88%) p < 0.001]. The rate of stress (70.8% vs. 55.4%, p = 0.002) and urge (64.9% vs. 50%, p = 0.04), urinary incontinence, and ICIQ-UI-SF scores (8 vs. 5, p < 0.001) were higher in group 1. Qmax measured lower in group 2 (17 vs. 15 mL/s, p = 0.008). Detrusor pressure at maximum flow was identical (24 cm H2O). The Bladder Contractility Index (BCI) was higher in group 1 (108 vs. 96.5, p = 0.017), and weak contraction (BCI < 100) was more common in group 2 (73/185; 39.5% vs. 95/184; 52.7%, p = 0.011). Conclusions: Based on our results, we assume that CUOB could be further subdivided based on its association with cystocele. The effect of prolapse repair in women with CUOB and cystocele remains to be evaluated in order to afford better counseling in the future.}, }
@article {pmid39860555, year = {2025}, author = {Onciul, R and Tataru, CI and Dumitru, AV and Crivoi, C and Serban, M and Covache-Busuioc, RA and Radoi, MP and Toader, C}, title = {Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.}, journal = {Journal of clinical medicine}, volume = {14}, number = {2}, pages = {}, pmid = {39860555}, issn = {2077-0383}, abstract = {The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.}, }
@article {pmid39858677, year = {2024}, author = {Wen, B and Shen, L and Kang, X}, title = {Laser Welding of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface.}, journal = {Micromachines}, volume = {16}, number = {1}, pages = {}, pmid = {39858677}, issn = {2072-666X}, support = {61904038//National Natural Science Foundation of China/ ; 2021YFC0122700//National Key Research and Development Program of China/ ; 2021SHZDZX0103//Shanghai Municipal Science and Technology Major Project/ ; }, abstract = {Minimally invasive endovascular stent electrodes are an emerging technology in neural engineering, designed to minimize the damage to neural tissue. However, conventional stent electrodes often rely on resistive welding and are relatively bulky, restricting their use primarily to large animals or thick blood vessels. In this study, the feasibility is explored of fabricating a laser welding stent electrode as small as 300 μm. A high-precision laser welding technique was developed to join micro-wire electrodes without compromising structural integrity or performance. To ensure consistent results, a novel micro-wire welding with platinum pad method was introduced during the welding process. The fabricated electrodes were integrated with stent structures and subjected to detailed electrochemical performance testing to evaluate their potential as neural interface components. The laser-welded endovascular stent electrodes exhibited excellent electrochemical properties, including low impedance and stable charge transfer capabilities. At the same time, in this study, a simulation is conducted of the electrode distribution and arrangement on the stent structure, optimizing the utilization of the available surface area for enhanced functionality. These results demonstrate the potential of the fabricated electrodes for high-performance neural interfacing in endovascular applications. The approach provided a promising solution for advancing endovascular neural engineering technologies, particularly in applications requiring compact electrode designs.}, }
@article {pmid39856396, year = {2025}, author = {Qin, Y and Zhang, L and Yu, B}, title = {A cross-domain-based channel selection method for motor imagery.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {39856396}, issn = {1741-0444}, support = {51977020//National Natural Science Foundation of China/ ; }, abstract = {Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.}, }
@article {pmid39856240, year = {2025}, author = {Zhou, Y and Tang, X and Zhang, D and Lee, HJ}, title = {Machine learning empowered coherent Raman imaging and analysis for biomedical applications.}, journal = {Communications engineering}, volume = {4}, number = {1}, pages = {8}, pmid = {39856240}, issn = {2731-3395}, support = {82372011//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12074339//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.}, }
@article {pmid39856041, year = {2025}, author = {Lai, QL and Cai, MT and Li, EC and Fang, GL and Shen, CH and Xu, YF and Qiao, S and Wang, JJ and Weng, QJ and Zhang, YX}, title = {Neurofilament light chain levels in neuronal surface antibody-associated autoimmune encephalitis: a systematic review and meta-analysis.}, journal = {Translational psychiatry}, volume = {15}, number = {1}, pages = {25}, pmid = {39856041}, issn = {2158-3188}, support = {LQ23H090004//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82222069//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073857//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Neurofilament Proteins/blood/cerebrospinal fluid/immunology ; *Autoantibodies/blood/cerebrospinal fluid ; *Hashimoto Disease/immunology/blood/cerebrospinal fluid ; *Encephalitis/immunology/blood/cerebrospinal fluid ; Biomarkers/blood/cerebrospinal fluid ; }, abstract = {BACKGROUND: Neuronal surface antibody-associated autoimmune encephalitis (NSAE) is a group of neuro-inflammatory disorders that is mediated by autoantibodies against the cell-surface and synaptic antigens. Studies have explored the role of neurofilament light chain (NfL) in NSAE and provided inconsistent data. We performed a systematic review and meta-analysis to evaluate the NfL levels in the serum and cerebrospinal fluid (CSF) of patients with NSAE.<br><br>METHODS: The National Center for Biotechnology Information (NCBI, PubMed), Web of Knowledge, and the Cochrane Library databases were searched for studies reporting NfL levels in patients with NSAE. Random-effects meta-analysis was used to pool results across studies.<br><br>RESULTS: Thirteen studies were included in the final systematic review and meta-analysis. The serum NfL levels were significantly higher in patients with NSAE compared to unaffected controls (standardized mean difference [SMD]&#x2009;=&#x2009;0.909, 95% confidence interval [CI]: 0.536-1.282). Similarly, the CSF NfL levels were elevated in patients with NSAE (SMD&#x2009;=&#x2009;0.897, 95% CI: 0.508-1.286). The serum and CSF NfL levels were not significantly correlated with disease severity, prognosis, and abnormalities in magnetic resonance imaging, electroencephalography, and CSF.<br><br>CONCLUSIONS: NfL levels in the serum and CSF were higher in patients with NSAE compared to unaffected controls. However, the NfL levels were not shown to be significantly associated with clinical or paraclinical features.}, }
@article {pmid39855567, year = {2025}, author = {Wei, Y and Zhao, L and Wei, J and Yu, X and Wei, L and Ni, R and Li, T}, title = {Hippocampal transcriptome analysis in ClockΔ19 mice identifies pathways associated with glial cell differentiation and myelination.}, journal = {Journal of affective disorders}, volume = {376}, number = {}, pages = {280-293}, doi = {10.1016/j.jad.2025.01.039}, pmid = {39855567}, issn = {1573-2517}, mesh = {Animals ; *Hippocampus/metabolism ; Mice ; *Bipolar Disorder/genetics ; *Cell Differentiation ; *Neuroglia/metabolism ; *CLOCK Proteins/genetics ; *Gene Expression Profiling ; *Disease Models, Animal ; Myelin Sheath/genetics ; Transcriptome ; Mutation ; }, abstract = {BACKGROUND: ClockΔ19 mice demonstrate behavioral characteristics and neurobiological changes that closely resemble those observed in bipolar disorder (BD). Notably, abnormalities in the hippocampus have been observed in patients with BD, yet direct molecular investigation of human hippocampal tissue remains challenging due to its limited accessibility.<br><br>METHODS: To model BD, Clock&#x394;19 mice were employed. Weighted gene co-expression network analysis (WGCNA) was utilized to identify mutation-related modules, and changes in cell populations were determined using the computational deconvolution CIBERSORTx. Furthermore, GeneMANIA and protein-protein interactions (PPIs) were leveraged to construct a comprehensive interaction network.<br><br>RESULTS: 174 differentially expressed genes (DEGs) were identified, revealing abnormalities in rhythmic processes, mitochondrial metabolism, and various cell functions including morphology, differentiation, and receptor activity. Analysis identified 5 modules correlated with the mutation, with functional enrichment highlighting disturbances in rhythmic processes and neural cell differentiation due to the mutation. Furthermore, a decrease in neural stem cells (NSC), and an increase in astrocyte-restricted precursors (ARP), ependymocytes (EPC), and hemoglobin-expressing vascular cells (Hb-VC) in the mutant mice were observed. A network comprising 12 genes that link rhythmic processes to neural cell differentiation in the hippocampus was also identified.<br><br>LIMITATIONS: This study focused on the hippocampus of mice, hence the applicability of these findings to human patients warrants further exploration.<br><br>CONCLUSION: The Clock&#x394;19 mutation may disrupt circadian rhythm, myelination, and the differentiation of neural stem cells (NSCs) into glial cells. These abnormalities are linked to altered expression of key genes, including DPB, CIART, NR1D1, GFAP, SLC20A2, and KL. Furthermore, interactions between SLC20A2 and KL might provide a connection between circadian rhythm regulation and cell type transitions.}, }
@article {pmid39854845, year = {2025}, author = {Bellicha, A and Struber, L and Pasteau, F and Juillard, V and Devigne, L and Karakas, S and Chabardes, S and Babel, M and Charvet, G}, title = {Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adae36}, pmid = {39854845}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/instrumentation/methods ; *Wheelchairs ; *Quadriplegia/rehabilitation ; Self-Help Devices ; Male ; Adult ; Electrocorticography/methods/instrumentation ; }, abstract = {Objective.Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. This article describes assist-as-needed sensor-based shared control (SC) methods relying on the blending of BCI and depth-sensor-based control.Approach.The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram recording implant named WIMAGINE.Main results.Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided.Significance.The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed SC methods improved the performance of the two assistive devices.Clinical trial, registration number: NCT02550522.}, }
@article {pmid39854835, year = {2025}, author = {Choi, BJ and Liu, J}, title = {A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adae35}, pmid = {39854835}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods/economics/instrumentation ; *Gestures ; *Artificial Limbs ; Male ; *Machine Learning ; Prosthesis Design ; Head/physiology ; Female ; Adult ; Neural Networks, Computer ; Algorithms ; }, abstract = {Objective.Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.Approach.To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor modeldiversitywithin these new neural network ensembles, as opposed to individual modelperformance. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.Main results.The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.Significance.These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.}, }
@article {pmid39854573, year = {2025}, author = {Eckert, AL and Fuehrer, E and Schmitter, C and Straube, B and Fiehler, K and Endres, D}, title = {Modelling sensory attenuation as Bayesian causal inference across two datasets.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0317924}, pmid = {39854573}, issn = {1932-6203}, mesh = {Humans ; *Bayes Theorem ; Male ; Female ; Adult ; Movement/physiology ; Markov Chains ; Touch/physiology ; Young Adult ; Touch Perception/physiology ; }, abstract = {INTRODUCTION: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.<br><br>METHODS: Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.<br><br>RESULTS: A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.<br><br>DISCUSSION: BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).}, }
@article {pmid39851820, year = {2024}, author = {Yu, W and Wu, C}, title = {Development and Validation of the Interpreting Learning Engagement Scale (ILES).}, journal = {Behavioral sciences (Basel, Switzerland)}, volume = {15}, number = {1}, pages = {}, pmid = {39851820}, issn = {2076-328X}, support = {20CG40//Shanghai Chenguang Talent Program/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//open project of Key Laboratory of Multilingual Education with AI/ ; C2022411//Shanghai Education and Science Research Project/ ; 2019114002//Shanghai International Studies University Planned Project/ ; }, abstract = {This study developed and validated the Interpreting Learning Engagement Scale (ILES), which was designed to measure the engagement of students in the interpreting learning context. Recognizing the crucial role of learning engagement in academic success and the acquisition of interpreting skills, which demands considerable cognitive effort and active involvement, this research addresses the gap in empirical studies on engagement within the field of interpreting. The ILES, comprising 18 items across four dimensions (behavioral, emotional, cognitive, and agentic engagement), was validated with data collected from a cohort of 306 students from five universities in China. The study employed exploratory and confirmatory factor analyses to establish the scale's theoretical underpinnings and provided further reliability and validity evidence, demonstrating its adequate psychometric properties. Additionally, the scale's scores showed a significant correlation with grit, securing the external validity of the ILES. This study not only contributes a validated instrument for assessing student engagement in interpreting learning but also provides implications for promoting engagement through potential interventions, with the ultimate aim of achieving high levels of interpreting competence.}, }
@article {pmid39851633, year = {2024}, author = {Ding, X and Zhang, Z and Wang, K and Xiao, X and Xu, M}, title = {A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.}, journal = {Entropy (Basel, Switzerland)}, volume = {27}, number = {1}, pages = {}, pmid = {39851633}, issn = {1099-4300}, abstract = {Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.}, }
@article {pmid39851418, year = {2025}, author = {Zhu, X and Meng, M and Yan, Z and Luo, Z}, title = {Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble.}, journal = {Brain sciences}, volume = {15}, number = {1}, pages = {}, pmid = {39851418}, issn = {2076-3425}, support = {62271181 and 62171171//National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.<br><br>OBJECTIVES: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.<br><br>METHODS: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification.<br><br>RESULTS: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively.<br><br>CONCLUSIONS: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness.}, }
@article {pmid39851397, year = {2024}, author = {Suresh, RE and Zobaer, MS and Triano, MJ and Saway, BF and Grewal, P and Rowland, NC}, title = {Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.}, journal = {Brain sciences}, volume = {15}, number = {1}, pages = {}, pmid = {39851397}, issn = {2076-3425}, support = {P20 GM109040/GM/NIGMS NIH HHS/United States ; 5 P20 GM109040//Center of Biomedical Research Excellence (COBRE) in Stroke Recovery-Junior Investigator Research Project. Source: National Institutes of Health/ ; }, abstract = {BACKGROUND/OBJECTIVES: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation.<br><br>METHODS: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning.<br><br>RESULTS: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants.<br><br>CONCLUSIONS: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.}, }
@article {pmid39851395, year = {2024}, author = {Pan, S and Shen, T and Lian, Y and Shi, L}, title = {A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder.}, journal = {Brain sciences}, volume = {15}, number = {1}, pages = {}, pmid = {39851395}, issn = {2076-3425}, support = {23CJL006//National Social Science Fund of China/ ; }, abstract = {BACKGROUND: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.<br><br>METHODS: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals.<br><br>RESULTS: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates.<br><br>CONCLUSIONS: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.}, }
@article {pmid39850117, year = {2025}, author = {Mahheidari, N and Alizadeh, M and Kamalabadi Farahani, M and Arabpour, Z and Rezaei Kolarijani, N and Djalilian, AR and Salehi, M}, title = {Regeneration of the skin wound by two different crosslinkers: In vitro and in vivo studies.}, journal = {Iranian journal of basic medical sciences}, volume = {28}, number = {2}, pages = {194-208}, pmid = {39850117}, issn = {2008-3866}, abstract = {OBJECTIVES: For designing a suitable hydrogel, two crosslinked Alginate/ Carboxymethyl cellulose (Alg/CMC) hydrogel, using calcium chloride (Ca[2+]) and glutaraldehyde (GA) as crosslinking agents were synthesized and compared.<br><br>MATERIALS AND METHODS: All samples were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Blood compatibility (BC), Blood clotting index (BCI), weight loss (WL), water absorption (WA), pH, and Electrochemical Impedance Spectroscopy (EIS). Cell viability and cell migration were investigated using the MTT assay and the wound scratch test, respectively. Besides, the wound healing potential of prepared hydrogels was evaluated on the rat models with full-thickness skin excision. To further investigation, TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were also reported by RT-PCR.<br><br>RESULTS: Water absorption and weight loss properties were compared between different crosslinker agents, and the most nontoxic crosslinker concentration was determined. We have shown that GA (20 &#xb5;l/ml) and Ca[2+] (50 or 75 mM) enhanced the physical stability of Alg-CMC hydrogel, and they are nontoxic and suitable crosslinkers for wound dressing applications. Although in vivo assessments indicated that the GA (20 &#xb5;l/ml) had a cytotoxic effect on tissue repair, Ca[2+] (75 mM) boosted the wound healing process. Further, RT-PCR results revealed that TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were increased in GA (20 &#xb5;l/ml). Moreover, this trend is the opposite in the Ca[2+] (75 mM) treatment groups.<br><br>CONCLUSION: This research shows that Ca[2+] (75 mM) boosts tissue regeneration and wound healing process.}, }
@article {pmid39850073, year = {2024}, author = {He, J and Huang, Z and Li, Y and Shi, J and Chen, Y and Jiang, C and Feng, J}, title = {Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1481493}, pmid = {39850073}, issn = {1662-5161}, abstract = {INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.<br><br>METHODS: The proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to preserve critical EEG components. The norm-constrained ELM employs L1/L2 regularization to improve generalization and classification performance. Experimental data were collected using a Schulte Grid paradigm and TGAM sensors, along with publicly available datasets for validation.<br><br>RESULTS: The robust Kalman filter demonstrated superior denoising performance, achieving an average AUC of 0.8167 and a maximum AUC of 0.8678 on self-collected datasets, and an average AUC of 0.8344 with a maximum of 0.8950 on public datasets. The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM's eSense algorithm in both noise reduction and attention classification accuracy.<br><br>DISCUSSION: The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback.}, }
@article {pmid39846559, year = {2025}, author = {Pantaleo, A and Curatoli, L and Cavallaro, G and Auricchio, D and Murri, A and Quaranta, N}, title = {Unilateral Versus Bilateral Cochlear Implants in Adults: A Cross-Sectional Questionnaire Study Across Multiple Hearing Domains.}, journal = {Audiology research}, volume = {15}, number = {1}, pages = {}, pmid = {39846559}, issn = {2039-4330}, abstract = {AIM: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.<br><br>METHODS: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale. Statistical analyses were performed to compare the subjective experiences of the three groups.<br><br>RESULTS: Patients reported that understanding speech in noise and tinnitus perception were their main concerns. BCI users experienced fewer difficulties with understanding speech in both quiet (p < 0.001) and noisy (p = 0.008) environments and with perceiving non-vocal sounds (p = 0.038) compared to UCI and BM users; no significant differences were found for music perception (p = 0.099), tinnitus perception (p = 0.397), or voice naturalness (p = 0.157). BCI users also reported less annoyance in quiet (p = 0.004) and noisy (p = 0.047) environments, and in the perception of voices (p = 0.009) and non-vocal sounds (p = 0.019). Tinnitus-related psychological impact showed no significant differences between groups (p = 0.090).<br><br>CONCLUSIONS: Although speech perception in noise and tinnitus remain major problems for CI users, the results of our study suggest that bilateral cochlear implantation offers significant subjective advantages over unilateral implantation and bimodal stimulation in adults, particularly in difficult listening environments.}, }
@article {pmid39844431, year = {2025}, author = {Guo, M and Han, X and Liu, H and Zhu, J and Zhang, J and Bai, Y and Ni, G}, title = {MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba's global scanning.}, journal = {Annals of the New York Academy of Sciences}, volume = {1544}, number = {1}, pages = {242-253}, doi = {10.1111/nyas.15288}, pmid = {39844431}, issn = {1749-6632}, support = {2023YFF1203500//National Key Research and Development Program of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences. Mamba, a state space model-based method, excels in modeling long sequences. To overcome the limitations of existing EEG decoding models and exploit Mamba's potential in EEG analysis, we propose MI-Mamba, a model integrating CNN with Mamba for motor imagery (MI) data decoding. MI-Mamba processes multi-channel EEG signals through a single convolutional layer to capture spatial features in the local temporal domain, followed by a Mamba module that processes global temporal features. A fully connected, layer-based classifier is used to derive classification results. Evaluated on two public MI datasets, MI-Mamba achieves 80.59% accuracy in the four-class MI task of the BCI Competition IV 2a dataset and 84.42% in the two-class task of the BCI Competition IV 2b dataset, while reducing parameter count by nearly six times compared to the most advanced previous models. These results highlight MI-Mamba's effectiveness in MI decoding and its potential as a new backbone for general EEG decoding.}, }
@article {pmid39843750, year = {2025}, author = {O'Connell, KS and Koromina, M and van der Veen, T and Boltz, T and David, FS and Yang, JMK and Lin, KH and Wang, X and Coleman, JRI and Mitchell, BL and McGrouther, CC and Rangan, AV and Lind, PA and Koch, E and Harder, A and Parker, N and Bendl, J and Adorjan, K and Agerbo, E and Albani, D and Alemany, S and Alliey-Rodriguez, N and Als, TD and Andlauer, TFM and Antoniou, A and Ask, H and Bass, N and Bauer, M and Beins, EC and Bigdeli, TB and Pedersen, CB and Boks, MP and Børte, S and Bosch, R and Brum, M and Brumpton, BM and Brunkhorst-Kanaan, N and Budde, M and Bybjerg-Grauholm, J and Byerley, W and Cabana-Domínguez, J and Cairns, MJ and Carpiniello, B and Casas, M and Cervantes, P and Chatzinakos, C and Chen, HC and Clarence, T and Clarke, TK and Claus, I and Coombes, B and Corfield, EC and Cruceanu, C and Cuellar-Barboza, A and Czerski, PM and Dafnas, K and Dale, AM and Dalkner, N and Degenhardt, F and DePaulo, JR and Djurovic, S and Drange, OK and Escott-Price, V and Fanous, AH and Fellendorf, FT and Ferrier, IN and Forty, L and Frank, J and Frei, O and Freimer, NB and Fullard, JF and Garnham, J and Gizer, IR and Gordon, SD and Gordon-Smith, K and Greenwood, TA and Grove, J and Guzman-Parra, J and Ha, TH and Hahn, T and Haraldsson, M and Hautzinger, M and Havdahl, A and Heilbronner, U and Hellgren, D and Herms, S and Hickie, IB and Hoffmann, P and Holmans, PA and Huang, MC and Ikeda, M and Jamain, S and Johnson, JS and Jonsson, L and Kalman, JL and Kamatani, Y and Kennedy, JL and Kim, E and Kim, J and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Kranz, TM and Krebs, K and Kushner, SA and Lavebratt, C and Lawrence, J and Leber, M and Lee, HJ and Liao, C and Lucae, S and Lundberg, M and MacIntyre, DJ and Maier, W and Maihofer, AX and Malaspina, D and Manchia, M and Maratou, E and Martinsson, L and Mattheisen, M and McGregor, NW and McInnis, MG and McKay, JD and Medeiros, H and Meyer-Lindenberg, A and Millischer, V and Morris, DW and Moutsatsou, P and Mühleisen, TW and O'Donovan, C and Olsen, CM and Panagiotaropoulou, G and Papiol, S and Pardiñas, AF and Park, HY and Perry, A and Pfennig, A and Pisanu, C and Potash, JB and Quested, D and Rapaport, MH and Regeer, EJ and Rice, JP and Rivera, M and Schulte, EC and Senner, F and Shadrin, A and Shilling, PD and Sigurdsson, E and Sindermann, L and Sirignano, L and Siskind, D and Slaney, C and Sloofman, LG and Smeland, OB and Smith, DJ and Sobell, JL and Soler Artigas, M and Stein, DJ and Stein, F and Su, MH and Sung, H and Świątkowska, B and Terao, C and Tesfaye, M and Tesli, M and Thorgeirsson, TE and Thorp, JG and Toma, C and Tondo, L and Tooney, PA and Tsai, SJ and Tsermpini, EE and Vawter, MP and Vedder, H and Vreeker, A and Walters, JTR and Winsvold, BS and Witt, SH and Won, HH and Ye, R and Young, AH and Zandi, PP and Zillich, L and ,  and Adolfsson, R and Alda, M and Alfredsson, L and Backlund, L and Baune, BT and Bellivier, F and Bengesser, S and Berrettini, WH and Biernacka, JM and Boehnke, M and Børglum, AD and Breen, G and Carr, VJ and Catts, S and Cichon, S and Corvin, A and Craddock, N and Dannlowski, U and Dikeos, D and Etain, B and Ferentinos, P and Frye, M and Fullerton, JM and Gawlik, M and Gershon, ES and Goes, FS and Green, MJ and Grigoroiu-Serbanescu, M and Hauser, J and Henskens, FA and Hjerling-Leffler, J and Hougaard, DM and Hveem, K and Iwata, N and Jones, I and Jones, LA and Kahn, RS and Kelsoe, JR and Kircher, T and Kirov, G and Kuo, PH and Landén, M and Leboyer, M and Li, QS and Lissowska, J and Lochner, C and Loughland, C and Luykx, JJ and Martin, NG and Mathews, CA and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Medland, SE and Melle, I and Milani, L and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Müller-Myhsok, B and Myers, RM and Myung, W and Neale, BM and Nievergelt, CM and Nordentoft, M and Nöthen, MM and Nurnberger, JI and O'Donovan, MC and Oedegaard, KJ and Olsson, T and Owen, MJ and Paciga, SA and Pantelis, C and Pato, CN and Pato, MT and Patrinos, GP and Pawlak, JM and Ramos-Quiroga, JA and Reif, A and Reininghaus, EZ and Ribasés, M and Rietschel, M and Ripke, S and Rouleau, GA and Roussos, P and Saito, T and Schall, U and Schalling, M and Schofield, PR and Schulze, TG and Scott, LJ and Scott, RJ and Serretti, A and Smoller, JW and Squassina, A and Stahl, EA and Stefansson, H and Stefansson, K and Stordal, E and Streit, F and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Waldman, ID and Weickert, CS and Weickert, TW and Werge, T and Whiteman, DC and Zwart, JA and Edenberg, HJ and McQuillin, A and Forstner, AJ and Mullins, N and Di Florio, A and Ophoff, RA and Andreassen, OA and , }, title = {Genomics yields biological and phenotypic insights into bipolar disorder.}, journal = {Nature}, volume = {639}, number = {8056}, pages = {968-975}, pmid = {39843750}, issn = {1476-4687}, support = {001/WHO_/World Health Organization/International ; }, mesh = {*Bipolar Disorder/genetics ; Humans ; *Phenotype ; *Genome-Wide Association Study ; *Genomics ; Male ; Female ; White People/genetics ; Genetic Predisposition to Disease ; GABAergic Neurons/metabolism ; Asian People/genetics ; Hispanic or Latino/genetics ; Black or African American/genetics ; White ; }, abstract = {Bipolar disorder is a leading contributor to the global burden of disease[1]. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown[2]. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings[3], and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder[4], highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.}, }
@article {pmid39842626, year = {2025}, author = {Xu, X and Gong, Q and Wang, XD}, title = {MK-801 attenuates one-trial tolerance in the elevated plus maze via the thalamic nucleus reuniens.}, journal = {Neuropharmacology}, volume = {268}, number = {}, pages = {110318}, doi = {10.1016/j.neuropharm.2025.110318}, pmid = {39842626}, issn = {1873-7064}, mesh = {Animals ; *Dizocilpine Maleate/pharmacology ; Male ; Mice ; *Anxiety/drug therapy ; *Midline Thalamic Nuclei/drug effects/physiology ; Drug Tolerance/physiology ; Diazepam/pharmacology ; Excitatory Amino Acid Antagonists/pharmacology ; Elevated Plus Maze Test ; Anti-Anxiety Agents/pharmacology ; Proto-Oncogene Proteins c-fos/metabolism ; Mice, Inbred C57BL ; Maze Learning/drug effects/physiology ; Neurons/drug effects/physiology ; }, abstract = {Anxiety, a future-oriented negative emotional state, is characterized by heightened arousal and vigilance. The elevated plus maze (EPM) test is a widely used assay of anxiety-related behaviors in rodents and shows a phenomenon where animals with prior test experience tend to avoid open arms in retest sessions. While this one-trial tolerance (OTT) phenomenon limits the reuse of the EPM test, the potential mechanisms remain unsolved. Here, we found that neither anxiogenic factors like acute restraint stress nor anxiolytic factors like diazepam (2 mg/kg) influenced the emergence of the OTT phenomenon in mice in the EPM test. In contrast, OTT was markedly attenuated by MK-801 (0.1 mg/kg), a non-competitive N-methyl-D-aspartate receptor antagonist. Through the use of c-fos mapping, MK-801 was found to increase neuronal activation in the thalamic nucleus reuniens (Re). Moreover, chemogenetic inactivation of Re neurons could prevent the effects of MK-801. Our findings suggest the Re as a crucial brain region in emotional adaptation in the EPM and shed light on the experimental design optimization and mechanistic investigation of anxiety-related behaviors.}, }
@article {pmid39841068, year = {2025}, author = {Zhang, J and Yang, X and Liang, Z and Lou, H and Cui, T and Shen, C and Gao, Z}, title = {A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.}, journal = {The Review of scientific instruments}, volume = {96}, number = {1}, pages = {}, doi = {10.1063/5.0232481}, pmid = {39841068}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; Humans ; *Exoskeleton Device ; *Lower Extremity/physiology ; Support Vector Machine ; Algorithms ; Imagination/physiology ; Electroencephalography/instrumentation ; Male ; }, abstract = {Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms. Additionally, an online experimental protocol that integrates visual and proprioceptive feedback is developed. To enhance decoding precision, we proposed a novel classification algorithm based on the stacking technique, termed weighted random forests-support vector machines (WRF-SVM). In this algorithm, WRFs function as the base learning models, while SVMs act as the meta-learning layer. To assess the efficacy of the BCI system and the classification algorithm, eight subjects were recruited for testing. The outcomes of both online and offline experiments exhibit high classification accuracy, confirming the viability and utility of the BCI system. We are confident that our approach holds significant promise for practical applications in the field of LLE technology.}, }
@article {pmid39840009, year = {2024}, author = {Yang, XY and Huang, S and Fu, QJ and Galvin, J and Chen, B and Liu, JS and Tao, DD}, title = {Preliminary evaluation of the FastCAP for users of the Nurotron cochlear implant.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1523212}, pmid = {39840009}, issn = {1662-4548}, abstract = {BACKGROUND: Electrically evoked compound action potential (ECAP) can be used to measure the auditory nerve's response to electrical stimulation in cochlear implant (CI) users. In the Nurotron CI system, extracting the ECAP waveform from the stimulus artifact is time-consuming.<br><br>METHOD: We developed a new paradigm ("FastCAP") for use with Nurotron CI devices. In electrically evoked compound action potential in fast mode (FastCAP), N recordings are averaged directly on the CI hardware before data transmission, significantly reducing data transmission time. FastCAPs and ECAPs were measured across five electrodes and four stimulation levels per electrode. The FastCAP stimulation rate (33.3 Hz) is also faster than the ECAP rate (2.5 Hz).<br><br>RESULTS: Results showed strong correlations between ECAPs and FastCAPs for N1 latency (r = 0.84, p < 0.001) and N1 amplitude (r = 0.97, p < 0.001). Test-retest reliability for FastCAPs was also high, with intraclass correlation coefficients of r = 0.87 for N1 latency (p < 0.001) and r = 0.96 for N1 amplitude (p < 0.001). The mean test time was 46.9 &#xb1; 1.4 s for the FastCAP and 340.3 &#xb1; 6.3 s for the ECAP. The FastCAP measurement time was significantly shorter than the ECAP measurement time (W = -210.0, p < 0.001). FastCAP thresholds were significantly correlated with behavioral thresholds in 7/20 participants and with comfortable loudness levels in 11/20 participants. The time required to measure FastCAPs was significantly lower than that for ECAPs. The FastCAP paradigm maintained the accuracy and reliability the ECAP measurements while offering a significant reduction in time requirements.<br><br>CONCLUSION: This preliminary evaluation suggests that the FastCAP could be an effective clinical tool to optimize CI processor settings (e.g., threshold stimulation levels) in users of the Nurotron CI device.}, }
@article {pmid39839676, year = {2024}, author = {Kusano, K and Hayashi, M and Iwama, S and Ushiba, J}, title = {Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study.}, journal = {Frontiers in neural circuits}, volume = {18}, number = {}, pages = {1510324}, pmid = {39839676}, issn = {1662-5110}, mesh = {Humans ; Male ; Female ; Adult ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Electric Stimulation/methods ; Young Adult ; Motor Skills/physiology ; Somatosensory Cortex/physiology ; Sensorimotor Cortex/physiology ; }, abstract = {INTRODUCTION: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.<br><br>METHODS: Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation.<br><br>RESULTS: The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability.<br><br>DISCUSSION: This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.}, }
@article {pmid39837322, year = {2025}, author = {Coulter, ME and Gillespie, AK and Chu, J and Denovellis, EL and Nguyen, TTK and Liu, DF and Wadhwani, K and Sharma, B and Wang, K and Deng, X and Eden, UT and Kemere, C and Frank, LM}, title = {Closed-loop modulation of remote hippocampal representations with neurofeedback.}, journal = {Neuron}, volume = {113}, number = {6}, pages = {949-961.e3}, doi = {10.1016/j.neuron.2024.12.023}, pmid = {39837322}, issn = {1097-4199}, mesh = {Animals ; *Neurofeedback/methods/physiology ; Rats ; *Hippocampus/physiology ; Male ; *Rats, Long-Evans ; Place Cells/physiology ; Reward ; Cues ; Mental Recall/physiology ; }, abstract = {Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells," which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.}, }
@article {pmid39834791, year = {2025}, author = {Alotaibi, S and Alotaibi, MM and Alghamdi, FS and Alshehri, MA and Bamusa, KM and Almalki, ZF and Alamri, S and Alghamdi, AJ and Alhazmi, M and Osman, H and Khandaker, MU}, title = {The role of fMRI in the mind decoding process in adults: a systematic review.}, journal = {PeerJ}, volume = {13}, number = {}, pages = {e18795}, pmid = {39834791}, issn = {2167-8359}, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Brain/physiology/diagnostic imaging ; *Brain Mapping/methods ; Mentalization/physiology ; Theory of Mind/physiology ; Temporal Lobe/physiology/diagnostic imaging ; }, abstract = {BACKGROUND: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.<br><br>METHODOLOGY: A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).<br><br>RESULTS: This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).<br><br>CONCLUSIONS: This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.}, }
@article {pmid39834638, year = {2024}, author = {Liu, J and Yang, Z and Wang, Y and Wang, L and Li, Z}, title = {Editorial: Micro/nano devices and technologies for neural science and medical applications.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {12}, number = {}, pages = {1545853}, pmid = {39834638}, issn = {2296-4185}, }
@article {pmid39833722, year = {2025}, author = {Kocher, HM and ,  and ,  and ,  and Sasieni, P and Corrie, P and McNamara, MG and Sarker, D and Froeling, FEM and Christie, A and Gillmore, R and Khan, K and Propper, D}, title = {Study protocol: multi-centre, randomised controlled clinical trial exploring stromal targeting in locally advanced pancreatic cancer; STARPAC2.}, journal = {BMC cancer}, volume = {25}, number = {1}, pages = {106}, pmid = {39833722}, issn = {1471-2407}, support = {MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; PCRFTB//Pancreatic Cancer Research Fund/ ; PCRFTB//Pancreatic Cancer Research Fund/ ; }, mesh = {Humans ; *Pancreatic Neoplasms/drug therapy/pathology/mortality/surgery ; *Deoxycytidine/analogs &amp; derivatives/therapeutic use/administration &amp; dosage ; *Gemcitabine ; *Paclitaxel/therapeutic use/administration &amp; dosage ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Albumins/administration &amp; dosage/therapeutic use ; Carcinoma, Pancreatic Ductal/drug therapy/pathology/surgery/mortality ; Tretinoin/therapeutic use/administration &amp; dosage ; Male ; Female ; Progression-Free Survival ; Multicenter Studies as Topic ; Adult ; Clinical Trials, Phase II as Topic ; Randomized Controlled Trials as Topic ; Middle Aged ; Aged ; Stromal Cells/pathology ; }, abstract = {BACKGROUND: Pancreatic cancer (PDAC: pancreatic ductal adenocarcinoma, the commonest form), a lethal disease, is best treated with surgical excision but is feasible in less than a fifth of patients. Around a third of patients presentlocally advanced, inoperable, non-metastatic (laPDAC), whose stadrd of care is palliative chemotherapy; a small minority are down-sized sufficiently to enable surgical excision. We propose a phase II clinical trial to test whether a combination of standard chemotherapy (gemcitabine &amp; nab-Paclitaxel: GEM-NABP) and repurposing All Trans Retinoic Acid (ATRA) to target the stroma may extend progression-free survival and enable successful surgical resection for patients with laPDAC, since data from phase IB clinical trial demonstrate safety of GEM-NABP-ATRA combination to patients with advanced PDAC with potential therapeutic benefit.<br><br>METHODS: Patients with laPDAC will receive at least six cycles of GEM-NABP with 1:1 randomisation to receive this with or without ATRA to assess response, until progression or intolerance. Those with stable/responding disease may undergo surgical resection. Primary endpoint is progression free survival (PFS) defined as the time from the date of randomisation to the date of first documented tumour progression (response evaluation criteria in solid tumours [RECIST] v1.1) or death from any cause, whichever occurs first. Secondary endpoints include objective response rate (ORR), overall survival (OS), safety and tolerability, surgical resection rate, R0 surgical resection rate and patient reported outcome measures (PROMS) as measured by questionnaire EQ-5D-5L. Exploratory endpoints include a decrease or increase in CA19-9 and serum Vitamin A over time correlated with ORR, PFS, and OS.<br><br>DISCUSSION: STARPAC2 aims to assess the role of stromal targeting in laPDAC.<br><br>TRIAL REGISTRATION: EudraCT: 2019-004231-23;&#xa0;NCT04241276; ISRCTN11503604.}, }
@article {pmid39833405, year = {2025}, author = {Willsey, MS and Shah, NP and Avansino, DT and Hahn, NV and Jamiolkowski, RM and Kamdar, FB and Hochberg, LR and Willett, FR and Henderson, JM}, title = {A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.}, journal = {Nature medicine}, volume = {31}, number = {1}, pages = {96-104}, pmid = {39833405}, issn = {1546-170X}, support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01-DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01-DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Video Games ; *Fingers/physiopathology ; *Paralysis/rehabilitation/physiopathology ; Quadriplegia/physiopathology/rehabilitation ; Spinal Cord Injuries/physiopathology ; Male ; Adult ; }, abstract = {People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain-computer-interface system allowing continuous control of three independent finger groups, of which the thumb can be controlled in two dimensions, yielding a total of four degrees of freedom. The system was tested in a human research participant with tetraplegia due to spinal cord injury over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets per minute and completion time of 1.58 ± 0.06 seconds-comparing favorably to prior animal studies despite a twofold increase in the decoded degrees of freedom. More importantly, finger positions were then used to control a virtual quadcopter-the number-one restorative priority for the participant-using a brain-to-finger-to-computer interface to allow dexterous navigation around fixed- and random-ringed obstacle courses. The participant expressed or demonstrated a sense of enablement, recreation and social connectedness that addresses many of the unmet needs of people with paralysis.}, }
@article {pmid39833404, year = {2025}, author = {Ramsey, NF and Vansteensel, MJ}, title = {The expanding repertoire of brain-computer interfaces.}, journal = {Nature medicine}, volume = {31}, number = {1}, pages = {31-32}, pmid = {39833404}, issn = {1546-170X}, }
@article {pmid39833218, year = {2025}, author = {Sun, Z and Huang, J and Ma, X and Liang, J and Sun, C and Hu, L and He, H and Yu, G}, title = {A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain.}, journal = {Scientific data}, volume = {12}, number = {1}, pages = {109}, pmid = {39833218}, issn = {2052-4463}, support = {62076218//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {*Magnetic Resonance Imaging ; Humans ; *Brain/diagnostic imaging/growth &amp; development ; Infant ; Spatio-Temporal Analysis ; Infant, Newborn ; }, abstract = {Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness. The presented low-field infant structural MR data aims to manifest the feasibility of using low-field MR image to exam brain structural changes during early life in infants. The dataset comprises 100 T2 weighed MR images from infants with in-plane resolution of ~0.85 mm and ~6 mm slice thickness. To demonstrate the potential utility, we conducted atlas-based whole brain segmentations and volumetric quantifications to analyze brain development features in first 10 week in postnatal life. This dataset addresses the scarcity of a large, extended-span infant brain dataset that restricts the further tracking of infant brain development trajectories and the development of routine low-field MR imaging pipelines.}, }
@article {pmid39833018, year = {2025}, author = {Pickles, J and Griffiths, L and McCloskey, AP and Vasey, N and Lim, E and Rathbone, AP}, title = {Developing a behaviour change intervention using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.}, journal = {Research in social &amp; administrative pharmacy : RSAP}, volume = {21}, number = {4}, pages = {277-286}, doi = {10.1016/j.sapharm.2025.01.006}, pmid = {39833018}, issn = {1934-8150}, mesh = {Humans ; *Anti-Bacterial Agents/administration &amp; dosage ; *Greenhouse Gases ; Male ; Female ; Practice Patterns, Physicians' ; Primary Health Care ; England ; Climate Change ; Adult ; Middle Aged ; Motivation ; }, abstract = {INTRODUCTION: The determinants of antimicrobial prescribing often involve social influence, which can be harnessed through behaviour change techniques (BCTs). While previous studies have used BCTs to address antimicrobial resistance, there is a lack of evidence regarding their application to address climate change-related issues in antibiotic prescribing. This study aimed to develop a behaviour change intervention (BCI) using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.<br><br>METHODS: A convenience sample of participants from a primary care practice in North East England participated in semi-structured interviews. The intervention design was guided by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation - Behaviour (COM-B) model. Data were analysed thematically, mapped to the TDF, and used to refine the BCI.<br><br>FINDINGS: Participants identified motivating factors related to high rates of liquid prescribing, climate change, and solid oral dosage form (pill) aversion. The broader context of practice, such as initiatives reduce cost and improve sustainability, provided opportunities for intervention. Participants demonstrated the capability to change prescribing behaviours and expressed willingness to share resources within their teams.<br><br>CONCLUSION: This study underscores the potential of BCIs using greenhouse gas emissions data to reduce liquid antibiotic prescribing. Further research should focus on implementing and evaluating these interventions in practice settings.}, }
@article {pmid39832646, year = {2025}, author = {Chen, Q and Pan, C and Shen, Y and Pan, Q and Zhang, Q and Wang, J and Hu, Y and Xu, H and Gong, M and Jia, K}, title = {Atypical subcortical involvement in emotional face processing in major depressive disorder with and without comorbid social anxiety.}, journal = {Journal of affective disorders}, volume = {374}, number = {}, pages = {531-539}, doi = {10.1016/j.jad.2025.01.081}, pmid = {39832646}, issn = {1573-2517}, mesh = {Humans ; *Depressive Disorder, Major/physiopathology/psychology ; Male ; Female ; Adult ; *Facial Expression ; *Facial Recognition/physiology ; *Emotions/physiology ; *Phobia, Social/physiopathology/epidemiology/psychology ; Comorbidity ; Young Adult ; Anger/physiology ; Middle Aged ; Sadness ; }, abstract = {Previous research on major depressive disorder (MDD) has largely focused on cognitive biases and abnormalities in cortico-limbic circuitry during emotional face processing. However, it remains unclear whether these abnormalities start at early perceptual stages via subcortical pathways and how comorbid social anxiety influences this process. Here, we investigated subcortical mechanisms in emotional face processing using a psychophysical method that measures monocular advantage (i.e., superior discrimination performance when two stimuli are presented to the same eye than to different eyes). Participants included clinical patients diagnosed with MDD (n = 32), patients with MDD comorbid with social anxiety (comorbid MDD-SAD, n = 32), and a control group of healthy participants (HC, n = 32). We assessed monocular advantage across different emotions (neutral, sad, angry) and among groups. Results indicated that individuals with MDD showed a stronger monocular advantage for sad expressions compared to neutral and angry expressions. In contrast, HC and comorbid MDD-SAD groups showed a greater monocular advantage for neural over negative expressions. Cross-group comparisons revealed that MDD group had a stronger monocular advantage for sad expressions than both HC and comorbid MDD-SAD groups. Additionally, self-reported depressive symptoms were positively correlated with monocular advantage for sad expressions, while social anxiety symptoms were negatively correlated with monocular advantage for negative expressions. These findings suggest atypical early perceptual processing of sadness in individuals with MDD via subcortical mechanisms, with comorbid social anxiety potentially counteracting this effect. This study may inform novel interventions targeting sensory processing and expand beyond cognitive bias modification.}, }
@article {pmid39832388, year = {2025}, author = {Haddix, C and Bates, M and Garcia-Pava, S and Salmon Powell, E and Sawaki, L and Sunderam, S}, title = {Electroencephalogram features reflect effort corresponding to graded finger extension: implications for hemiparetic stroke.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {2}, pages = {}, doi = {10.1088/2057-1976/adabeb}, pmid = {39832388}, issn = {2057-1976}, mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology/complications ; Male ; *Paresis/physiopathology/etiology ; *Brain-Computer Interfaces ; *Fingers ; Female ; *Electromyography/methods ; Middle Aged ; Aged ; Movement ; Adult ; Case-Control Studies ; Hand/physiopathology ; Brain/physiopathology ; }, abstract = {Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or 'no-go' (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on EEG features, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n = 11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n = 3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.}, }
@article {pmid39832179, year = {2025}, author = {Jiang, M and Tan, Y and Wang, X and Hao, Y}, title = {The Phonograms' Genuine-Character Status: What the Embedded Semantic Radicals' Semantic Activation Live by.}, journal = {Brain and behavior}, volume = {15}, number = {1}, pages = {e70277}, pmid = {39832179}, issn = {2162-3279}, mesh = {Humans ; *Semantics ; Male ; Young Adult ; Female ; Adult ; Reading ; }, abstract = {BACKGROUND: In Chinese phonogram processing studies, it is widely accepted that both character and non-character semantic radicals could be semantically activated. However, little attention was paid to the underlying workings that enabled the semantic radicals' semantic activation.<br><br>PURPOSE: The present study aimed to address the above issue by conducting two experiments.<br><br>METHODS: Experiment 1 was committed to confirming whether both character and non-character semantic radicals could be semantically activated when embedded in genuine Chinese phonograms. Experiment 2 was devoted to exploring whether the same semantic radicals could also be semantically activated when incorporated in Chinese pseudo-characters.<br><br>RESULTS: Results demonstrated that both character and non-character semantic radicals embedded in the genuine phonograms were semantically activated, but those placed in the pseudo-characters underwent no semantic activation, suggesting that the semantic activation of semantic radicals was genuine-character status-dependent, irrespective of the semantic radicals' characterhood.<br><br>CONCLUSION: It seems that the genuine-character status and the meaning of the host phonogram have strong sway on the semantic activation of semantic radicals.}, }
@article {pmid39830198, year = {2024}, author = {Liu, D and Wang, N and Song, M and Chai, X and He, Q and Cao, T and Kong, D and Song, Z and Zhang, G and Liu, L and Wang, X and Chen, G and Yin, S and Yang, Y and Zhao, J}, title = {Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1425271}, pmid = {39830198}, issn = {1664-2295}, abstract = {OBJECTIVE: This study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.<br><br>METHODS: A cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS). Additionally, resting-state FDG-PET data were acquired from both the patient group and a control group consisting of 10 healthy individuals. The FDG-PET data underwent reorientation, spatial normalization to a stereotaxic space, and smoothing. The normalization procedure utilized a customized template following the methodology outlined by Phillips et al. Mean cortical metabolism of the overall sample was utilized for distinguishing between UWS and MCS, as well as for predicting the outcome at a 1-year follow-up through the application of receiver operating characteristic (ROC) analysis.<br><br>RESULTS: We used Global Glucose Metabolism as the Diagnostic Marker. A one-way ANOVA revealed that there was a statistically significant difference in cortical metabolic index between two groups (F(2, 53)&#x202f;=&#x202f;7.26, p&#x202f;<&#x202f;0.001). Multiple comparisons found that the mean of cortical metabolic index was significantly different between MCS (M&#x202f;=&#x202f;4.19, SD&#x202f;=&#x202f;0.64) and UWS group (M&#x202f;=&#x202f;2.74, SD&#x202f;=&#x202f;0.94,p&#x202f;<&#x202f;0.001). Also, the mean of cortical metabolic index was significantly different between MCS and healthy group (M&#x202f;=&#x202f;7.88, SD&#x202f;=&#x202f;0.80,p&#x202f;<&#x202f;0.001). Using the above diagnostic criterion, the diagnostic accuracy yielded an area under the curve (AUC) of 0.89 across the pooled cohort (95%CI 0.79-0.99). There was an 85% correct classification between MCS and UWS, with 88% sensitivity and 81% specificity for MCS. The best classification rate in the derivation cohort was achieved at a metabolic index of 3.32 (41% of the mean cortical metabolic index in healthy controls).<br><br>CONCLUSION: Our findings demonstrate that conscious awareness requires a minimum of 41% of normal cortical activity, as indicated by metabolic rates.}, }
@article {pmid39825141, year = {2025}, author = {Oby, ER and Degenhart, AD and Grigsby, EM and Motiwala, A and McClain, NT and Marino, PJ and Yu, BM and Batista, AP}, title = {Dynamical constraints on neural population activity.}, journal = {Nature neuroscience}, volume = {28}, number = {2}, pages = {383-393}, pmid = {39825141}, issn = {1546-1726}, support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; NCS BCS1533672//National Science Foundation (NSF)/ ; R01 MH118929/MH/NIMH NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; NS105318//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS129584/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Motor Cortex/physiology ; *Neurons/physiology ; *Macaca mulatta ; *Brain-Computer Interfaces ; Models, Neurological ; Male ; Nerve Net/physiology ; }, abstract = {The manner in which neural activity unfolds over time is thought to be central to sensory, motor and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface to challenge monkeys to violate the naturally occurring time courses of neural population activity that we observed in the motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.}, }
@article {pmid39824976, year = {2025}, author = {Zhao, J and Shen, Q and Yong, X and Li, X and Tian, X and Sun, S and Xu, Z and Zhang, X and Zhang, L and Yang, H and Shao, Z and Xu, H and Jiang, Y and Zhang, Y and Yan, W}, title = {Cryo-EM reveals cholesterol binding in the lysosomal GPCR-like protein LYCHOS.}, journal = {Nature structural &amp; molecular biology}, volume = {}, number = {}, pages = {}, pmid = {39824976}, issn = {1545-9985}, abstract = {Cholesterol plays a pivotal role in modulating the activity of mechanistic target of rapamycin complex 1 (mTOR1), thereby regulating cell growth and metabolic homeostasis. LYCHOS, a lysosome-localized G-protein-coupled receptor-like protein, emerges as a cholesterol sensor and is capable of transducing the cholesterol signal to affect the mTORC1 function. However, the precise mechanism by which LYCHOS recognizes cholesterol remains unknown. Here, using cryo-electron microscopy, we determined the three-dimensional structural architecture of LYCHOS in complex with cholesterol molecules, revealing a unique arrangement of two sequential structural domains. Through a comprehensive analysis of this structure, we elucidated the specific structural features of these two domains and their collaborative role in the process of cholesterol recognition by LYCHOS.}, }
@article {pmid39824751, year = {2025}, author = {Cong, L and Wang, X and Wang, J and Liu, W and Xu, W and Zhang, S and Xu, S}, title = {Three-Dimensional SERS-Active Hydrogel Microbeads Enable Highly Sensitive Homogeneous Phase Detection of Alkaline Phosphatase in Biosystems.}, journal = {ACS applied materials &amp; interfaces}, volume = {17}, number = {4}, pages = {5933-5941}, doi = {10.1021/acsami.4c18139}, pmid = {39824751}, issn = {1944-8252}, mesh = {*Alkaline Phosphatase/metabolism ; Humans ; *Spectrum Analysis, Raman/methods ; *Silver/chemistry ; *Metal Nanoparticles/chemistry ; *Hydrogels/chemistry ; *Microspheres ; Hep G2 Cells ; }, abstract = {Alkaline phosphatase (ALP) is a biomarker for many diseases, and monitoring its activity level is important for disease diagnosis and treatment. In this study, we used the microdroplet technology combined with an in situ laser-induced polymerization method to prepare the Ag nanoparticle (AgNP) doped hydrogel microbeads (HMBs) with adjustable pore sizes that allow small molecules to enter while blocking large molecules. The AgNPs embedded in the hydrogel microspheres can provide SERS activity, improving the SERS signal of small molecules that diffuse to the AgNPs. A specific hydrolysis reaction of ALP on 5-bromo-4-chloro-3-indolylphosphate (BCIP) was introduced and itsproduct 5,5'-dibromo-4,4'-dichloro-1H,1H-[2,2']bisindolyl-3,3'-dione (BCI) was employed to assess ALP activity due to its highly resonance Raman activity. The sensing platform was applied to model ALP activity in serum and evaluate ALP inhibitors. The SERS assay showed higher sensitivity than UV-vis absorption spectroscopy, with the lowest detectable ALP concentration of 1.0 × 10[-20] M. In addition, the ALP activity in HepG2 cells was evaluated using this sensing platform, showing lower ALP-expressing activity than that of controls in response to hypoxia and iron metastasis. This SERS-activated HMB shows great potential in detecting ALP and is expected to help analyze complex clinical samples.}, }
@article {pmid39823647, year = {2025}, author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE}, title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, pmid = {39823647}, issn = {1741-2552}, support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Animals ; *Muscle, Skeletal/physiology/innervation ; *Motor Cortex/physiology ; *Macaca mulatta ; *Electromyography/methods ; Male ; Linear Models ; Unsupervised Machine Learning ; }, abstract = {Objective.Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.Approach.We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.Main results.We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network decoder with 10-12 clusters.Significance.This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.}, }
@article {pmid39821163, year = {2025}, author = {Zou, Y and Xu, J and Chen, Y}, title = {Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0315167}, pmid = {39821163}, issn = {1932-6203}, mesh = {Humans ; *Commerce ; Models, Economic ; Ships ; Risk ; }, abstract = {The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.}, }
@article {pmid39820611, year = {2025}, author = {D, J and K C, S}, title = {Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0311942}, pmid = {39820611}, issn = {1932-6203}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Signal-To-Noise Ratio ; }, abstract = {In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.}, }
@article {pmid39819752, year = {2025}, author = {Chen, J and Chen, X and Wang, R and Le, C and Khalilian-Gourtani, A and Jensen, E and Dugan, P and Doyle, W and Devinsky, O and Friedman, D and Flinker, A and Wang, Y}, title = {Transformer-based neural speech decoding from surface and depth electrode signals.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, pmid = {39819752}, issn = {1741-2552}, support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; R01 NS109367/NS/NINDS NIH HHS/United States ; R01 NS115929/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; Male ; Electroencephalography/methods ; Speech/physiology ; Female ; Brain-Computer Interfaces ; Adult ; Deep Learning ; }, abstract = {Objective.This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e. Electrocorticographic (ECoG) or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface ECoG and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements. The model should not have subject-specific layers and the trained model should perform well on participants unseen during training.Approach.We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-subject models exploiting data from multiple participants.Main results.The subject-specific models using only low-density 8 × 8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC = 0.817), overN= 43 participants, significantly outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N= 39) led to further improvement (PCC = 0.838). For participants with only sEEG electrodes (N= 9), subject-specific models still enjoy comparable performance with an average PCC = 0.798. A single multi-subject model trained on ECoG data from 15 participants yielded comparable results (PCC = 0.837) as 15 models trained individually for these participants (PCC = 0.831). Furthermore, the multi-subject models achieved high performance on unseen participants, with an average PCC = 0.765 in leave-one-out cross-validation.Significance.The proposed SwinTW decoder enables future speech decoding approaches to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. The success of the single multi-subject model when tested on participants within the training cohort demonstrates that the model architecture is capable of exploiting data from multiple participants with diverse electrode placements. The architecture's flexibility in training with both single-subject and multi-subject data, as well as grid and non-grid electrodes, ensures its broad applicability. Importantly, the generalizability of the multi-subject models in our study population suggests that a model trained using paired acoustic and neural data from multiple patients can potentially be applied to new patients with speech disability where acoustic-neural training data is not feasible.}, }
@article {pmid39819697, year = {2025}, author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ}, title = {Correction: CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.}, journal = {Translational neurodegeneration}, volume = {14}, number = {1}, pages = {3}, pmid = {39819697}, issn = {2047-9158}, }
@article {pmid39819671, year = {2025}, author = {H Liu, D and Kumar, S and Alawieh, H and Samuel Racz, F and Del R Millán, J}, title = {Personalizedµ-transcranial alternating current stimulation improves online brain-computer interface control.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ada980}, pmid = {39819671}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Transcranial Direct Current Stimulation/methods ; Male ; Adult ; Female ; Young Adult ; *Imagination/physiology ; *Electroencephalography/methods ; Single-Blind Method ; Online Systems ; Psychomotor Performance/physiology ; Movement/physiology ; }, abstract = {Objective.A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).Approach.Previous studies have identified that the peak power spectral density value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 min of tACS at the participant-specific, peakµfrequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-stateµSMRs.Main results.After tACS, we observed significant improvements in event-related desynchronizations (ERDs) ofµSMRs, and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N= 10)-but not in an active control-stimulation control group (N= 10). Lastly, we showed a significant correlation between the resting-stateµSMRs andµERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.Significance.Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.}, }
@article {pmid39819299, year = {2025}, author = {Tsai, PC and Akpan, A and Tang, KT and Lakany, H}, title = {Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review.}, journal = {BMC geriatrics}, volume = {25}, number = {1}, pages = {36}, pmid = {39819299}, issn = {1471-2318}, mesh = {Humans ; *Brain-Computer Interfaces ; Aged ; *Cognitive Dysfunction/psychology/therapy ; *Neurofeedback/methods/physiology ; *Electroencephalography/methods ; Cognition/physiology ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).<br><br>METHODS: Articles were searched via MEDLINE, PubMed, SCOPUS, SpringerLink, and Web of Science. 16 studies between 1st January 2010 to 1st November 2024 are included after screening using PRISMA. The risk of bias, system design, and neurofeedback protocols are reviewed.<br><br>RESULTS: The successful BCI applications in NF trials in older people were biased by the randomisation process and outcome measurement. Although the studies demonstrate promising results in effectiveness of research-grade BCI for cognitive enhancement in older people, it is premature to make definitive claims about widespread BCI usability and applicability.<br><br>SIGNIFICANCE: This review highlights the common issues in the field of EEG-based BCI for older people. Future BCI research could focus on trial design and BCI performance gaps between the old and the young to develop a robust BCI system that compensates for age-related declines in cognitive and motor functions.}, }
@article {pmid39818889, year = {2025}, author = {Marasco, PD}, title = {Navigating the complexity of touch.}, journal = {Science (New York, N.Y.)}, volume = {387}, number = {6731}, pages = {248-249}, doi = {10.1126/science.adu7929}, pmid = {39818889}, issn = {1095-9203}, mesh = {*Brain-Computer Interfaces ; *Touch/physiology ; Humans ; Somatosensory Cortex/physiology ; Animals ; Touch Perception/physiology ; Electric Stimulation ; }, abstract = {Precise cortical microstimulation improves tactile experience in brain-machine interfaces.}, }
@article {pmid39818280, year = {2025}, author = {Li, C and Ma, K and Li, S and Meng, X and Wang, R and Zhang, D and Zhu, Q}, title = {Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis.}, journal = {NeuroImage}, volume = {307}, number = {}, pages = {121013}, doi = {10.1016/j.neuroimage.2025.121013}, pmid = {39818280}, issn = {1095-9572}, mesh = {Humans ; *Brain Diseases/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging/methods ; *Diffusion Tensor Imaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; *Brain/diagnostic imaging/physiopathology ; Female ; Male ; Aged ; Adult ; Connectome/methods ; }, abstract = {Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases.}, }
@article {pmid39815608, year = {2025}, author = {Dai, C and Fu, Y and Li, X and Lin, M and Li, Y and Li, X and Huang, K and Zhou, C and Xie, J and Zhao, Q and Hu, S}, title = {Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression.}, journal = {Journal of Zhejiang University. Science. B}, volume = {26}, number = {1}, pages = {26-38}, pmid = {39815608}, issn = {1862-1783}, support = {OO2020491//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; JNL-2023001B//the Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation&#x2012;"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; 2022KTZ004//the Chinese Medical Education Association/ ; }, mesh = {Humans ; *Vortioxetine/therapeutic use ; *Bipolar Disorder/drug therapy ; Male ; Female ; Adult ; *Valproic Acid/therapeutic use/adverse effects ; Middle Aged ; Treatment Outcome ; Prospective Studies ; Pilot Projects ; Lurasidone Hydrochloride/therapeutic use/adverse effects ; Drug Therapy, Combination ; }, abstract = {OBJECTIVES: Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains controversial. This study aimed to validate the efficacy and safety of vortioxetine in bipolar depression.<br><br>METHODS: Patients with bipolar &#x2161; depression were enrolled in this prospective, two-center, randomized, 12-week pilot trial. The main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale (MADRS) of &#x2265;50%. All eligible patients initially received four weeks of lurasidone monotherapy. Patients who responded well continued to receive this kind of monotherapy. However, no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight weeks. By comprehensively comparing the results of MADRS over a period of 4&#x200d;&#x2012;&#x200d;12 weeks, a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar depression.<br><br>RESULTS: Thirty-seven patients responded to lurasidone monotherapy, and 60 patients were randomly assigned to the valproate or vortioxetine group for eight weeks. After two weeks of combined valproate or vortioxetine treatment, the MADRS score in the vortioxetine group was significantly lower than that in the valproate group. There was no difference in the MADRS scores between the two groups at 8 and 12 weeks. The incidence of side effects did not significantly differ between the valproate and vortioxetine groups. Importantly, three patients in the vortioxetine group appeared to switch to mania or hypomania.<br><br>CONCLUSIONS: This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage, while disease progression should be monitored closely for the risk of switching to mania.}, }
@article {pmid39814210, year = {2025}, author = {Kyoda, Y and Shibamori, K and Tachikawa, K and Nofuji, S and Saito, Y and Tabata, H and Shindo, T and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N}, title = {The Change of Detrusor Contractility at 5 Years After Transurethral Resection of the Prostate: A Single Center Prospective Observational Study.}, journal = {Urology}, volume = {197}, number = {}, pages = {166-173}, doi = {10.1016/j.urology.2024.12.038}, pmid = {39814210}, issn = {1527-9995}, mesh = {Humans ; Male ; Aged ; *Transurethral Resection of Prostate ; Prospective Studies ; *Urinary Bladder/physiopathology/surgery ; *Urinary Bladder Neck Obstruction/surgery/physiopathology/etiology ; *Prostatic Hyperplasia/surgery/physiopathology ; Middle Aged ; Muscle Contraction/physiology ; Time Factors ; Urodynamics ; Aged, 80 and over ; Follow-Up Studies ; }, abstract = {OBJECTIVE: To prospectively assess the impact of transurethral resection of the prostate (TURP) on detrusor function using pressure flow study (PFS) at 5 years after surgery in a single center prospective non-randomized observational study.<br><br>METHODS: Sixty consecutive male patients were prospectively enrolled and underwent TURP from November 2014 to November 2018. A questionnaire survey, free uroflowmetry, and PFS were performed at baseline, and 6, 24, and 60 months after surgery. We divided the age groups at 70 years and defined the younger group as those younger than 70 years, and the elderly group as those aged 70 years or older. The primary endpoint was the change in the bladder contractility index (BCI).<br><br>RESULTS: Of the 60 patients, 39 completed the protocol. Regardless of age, the bladder outlet obstruction indices at 6, 24, and 60 months after surgery were significantly lower than before surgery (all, P<.01). Although the BCI did not significantly change during 60 months for the entire group of 39 patients, it was significantly decreased at 60 months (85.6) after surgery compared to before surgery (102) in the elderly group (P=.02).<br><br>CONCLUSION: We prospectively evaluated detrusor contractility up to 5 years after TURP. It was significantly reduced in the elderly, in spite of which the relief of bladder outlet obstruction was maintained for 5 years after surgery.}, }
@article {pmid39814172, year = {2025}, author = {Wei, Q and Fan, W and Li, HF and Wang, PS and Xu, M and Dong, HL and Yu, H and Lyu, J and Luo, WJ and Chen, DF and Ge, W and Wu, ZY}, title = {Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia.}, journal = {Journal of genetics and genomics = Yi chuan xue bao}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jgg.2025.01.004}, pmid = {39814172}, issn = {1673-8527}, abstract = {Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP.}, }
@article {pmid39813939, year = {2025}, author = {Chen, L and Yin, Z and Gu, X and Zhang, X and Cao, X and Zhang, C and Li, X}, title = {Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.}, journal = {Computer methods and programs in biomedicine}, volume = {261}, number = {}, pages = {108594}, doi = {10.1016/j.cmpb.2025.108594}, pmid = {39813939}, issn = {1872-7565}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; *Models, Statistical ; Signal-To-Noise Ratio ; Algorithms ; Brain/diagnostic imaging/physiology ; Normal Distribution ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; Databases, Factual ; }, abstract = {BACKGROUND AND OBJECTIVE: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.<br><br>METHODS: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy.<br><br>RESULTS: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participant-dependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database.<br><br>CONCLUSIONS: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.}, }
@article {pmid39811472, year = {2024}, author = {Maibam, PC and Pei, D and Olikkal, P and Vinjamuri, RK and Kakoty, NM}, title = {Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.}, journal = {Wearable technologies}, volume = {5}, number = {}, pages = {e18}, pmid = {39811472}, issn = {2631-7176}, abstract = {Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.}, }
@article {pmid39809040, year = {2025}, author = {Xu, R and Allison, BZ and Zhao, X and Liang, W and Wang, X and Cichocki, A and Jin, J}, title = {Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {107124}, doi = {10.1016/j.neunet.2025.107124}, pmid = {39809040}, issn = {1879-2782}, mesh = {Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Evoked Potentials/physiology ; *Electroencephalography/methods ; *Attention/physiology ; Brain/physiology ; Algorithms ; Deep Learning ; }, abstract = {Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. The model integrates a multi-scale architecture, self-attention mechanism, and deep metric learning to achieve a more comprehensive, refined, and discriminative feature representation. The MS module aggregates fine-grained local features and global features with a larger receptive field within a multi-scale architecture, effectively capturing the dynamic characteristics of complex oscillatory activities in the brain at different levels of abstraction. This preserves complementary spatiotemporal representation information. The PSA module continues the multi-scale contextual modeling from the previous module and achieves adaptive recalibration of multi-scale features. By employing effective aggregation and selection mechanisms, it highlights key features while suppressing redundant information. The SOC module jointly optimizes similarity metric loss and classification loss, maintaining the feature space distribution while focusing on sample class labels. This optimization of similarity relationships between samples improves the model's generalization ability and robustness. Results from public and self-collected datasets demonstrate that the command recognition accuracy of the MS-PSA-SOC model is at least 3.1% and 2.8% higher than other advanced algorithms, achieving superior performance. Additionally, the method demonstrates a lower standard deviation across both datasets. This study also validated the network parameters based on Shannon's sampling theorem and EEG "microstates" through relevant experiments.}, }
@article {pmid39808940, year = {2025}, author = {Gu, M and Pei, W and Gao, X and Wang, Y}, title = {Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaa1e}, pmid = {39808940}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography/methods ; *Photic Stimulation/methods ; Female ; Adult ; Young Adult ; }, abstract = {Objective.Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.Approach.In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.Main results.Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.Significance.These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.}, }
@article {pmid39808939, year = {2025}, author = {Prakash, PR and Lei, T and Flint, RD and Hsieh, JK and Fitzgerald, Z and Mugler, E and Templer, J and Goldrick, MA and Tate, MC and Rosenow, J and Glaser, J and Slutzky, MW}, title = {Decoding speech intent from non-frontal cortical areas.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, pmid = {39808939}, issn = {1741-2552}, support = {UL1 TR000150/TR/NCATS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; R21 NS084069/NS/NINDS NIH HHS/United States ; T32 NS047987/NS/NINDS NIH HHS/United States ; F32 DC015708/DC/NIDCD NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Speech/physiology ; Male ; Female ; Adult ; *Parietal Lobe/physiology/physiopathology ; *Brain-Computer Interfaces ; Temporal Lobe/physiology/physiopathology ; Intention ; Young Adult ; Frontal Lobe/physiology/physiopathology ; Electrocorticography/methods ; Middle Aged ; }, abstract = {Objective. Brain machine interfaces (BMIs) that can restore speech have predominantly focused on decoding speech signals from the speech motor cortices. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobe could be useful not only for people with locked-in syndrome, but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intent could be found in the temporal and parietal corticesApproach. Using intracranial recordings, we examined neural activity across temporal and parietal cortices to identify signals associated with speech intent. We employed causal information to distinguish speech intent from resting states and other language-related processes, such as comprehension and working memory. Neural signals were analyzed for their spatial distribution and temporal dynamics to determine their relevance to speech production.Main results. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri.Significance. Loss of communication due to neurological diseases can be devastating. While speech BMIs have made strides in decoding speech from frontal lobe signals, our study reveals that the temporal and parietal cortices contain information about speech production intent that can be causally decoded prior to the onset of voice. This information is distributed across a large network. This information can be used to improve current speech BMIs and potentially expand the patient population for speech BMIs to include people with frontal lobe damage from stroke or traumatic brain injury.}, }
@article {pmid39808931, year = {2025}, author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB}, title = {Decoding imagined movement in people with multiple sclerosis for brain-computer interface translation.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaa1d}, pmid = {39808931}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/rehabilitation ; *Imagination/physiology ; Male ; Female ; Adult ; *Movement/physiology ; Middle Aged ; *Electroencephalography/methods ; }, abstract = {Objective.Multiple sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A brain-computer interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement.Approach.We collected electroencephalography data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared.Main Results.In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency.Significance.This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.}, }
@article {pmid39808922, year = {2025}, author = {Dekleva, BM and Collinger, JL}, title = {Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/adaa1f}, pmid = {39808922}, issn = {1741-2552}, support = {R01 NS121079/NS/NINDS NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; U01 NS123125/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Male ; Adult ; Female ; Hand Strength/physiology ; Electroencephalography/methods ; Young Adult ; Hand/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Algorithms ; Motor Cortex/physiology ; }, abstract = {Objective.Real-world implementation of brain-computer interfaces (BCIs) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control. However, the relation between cortical activity and behavior is not stationary: neural responses that appear related to a certain aspect of behavior (e.g. grasp force) in one context will exhibit a relationship to something else in another context (e.g. reach speed). This presents a challenge for generalizable decoding, since the applicability of a decoder for a given parameter changes over time.Approach.We developed a method to simplify the problem of continuous decoding that uses transient, end effector-specific neural responses to identify periods of relevant effector engagement. Specifically, we use transient responses in the population response observed at the onset and offset of all hand-related actions to signal the applicability of hand-related feature decoders (e.g. digit movement or force). By using this transient-based gating approach, specific feature decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions.Main results.The transient-based decoding approach enabled high-quality online decoding of grasp force and individual finger control in multiple behavioral paradigms. The benefits of the gated approach are most evident in tasks that require both hand and arm control, for which standard continuous decoding approaches exhibit high output variability.Significance.The approach proposed here addresses the challenge of decoder generalization across contexts. By limiting decoding to identified periods of effector engagement, this approach can support reliable BCI control in real-world applications.Clinical Trial ID: NCT01894802.}, }
@article {pmid39805390, year = {2025}, author = {Xu, A and Huang, Y and Wu, B and Zhang, J and Deng, B and Cai, M and Cao, J and Wang, J and Yang, B and Shao, X and He, Q and Ying, M}, title = {Phase separation-based screening identifies arsenic trioxide as the N-Myc-DNA interaction inhibitor for neuroblastoma therapy.}, journal = {Cancer letters}, volume = {612}, number = {}, pages = {217449}, doi = {10.1016/j.canlet.2025.217449}, pmid = {39805390}, issn = {1872-7980}, }
@article {pmid39805258, year = {2025}, author = {Liu, DH and Kumar, S and Alawieh, H and Racz, FS and Millan, JDR}, title = {Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada9c0}, pmid = {39805258}, issn = {1741-2552}, abstract = {OBJECTIVE: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).<br><br>APPROACH: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak &#xb5; frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state &#xb5; SMRs.<br><br>MAIN RESULTS: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of &#xb5; sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state &#xb5; SMRs and &#xb5; ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.<br><br>SIGNIFICANCE: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.}, }
@article {pmid39803338, year = {2024}, author = {Kim, KH and Jeong, JH and Ko, MJ and Lee, S and Kwon, WK and Lee, BJ}, title = {Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.}, journal = {Korean journal of neurotrauma}, volume = {20}, number = {4}, pages = {215-224}, pmid = {39803338}, issn = {2234-8999}, abstract = {Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.}, }
@article {pmid39801915, year = {2025}, author = {Gu, C and Jin, X and Zhu, L and Yi, H and Liu, H and Yang, X and Babiloni, F and Kong, W}, title = {Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {15}, pmid = {39801915}, issn = {1871-4080}, abstract = {Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.}, }
@article {pmid39801913, year = {2025}, author = {Zhou, Y and Wang, P and Gong, P and Wan, P and Wen, X and Zhang, D}, title = {Cross-subject mental workload recognition using bi-classifier domain adversarial learning.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {16}, pmid = {39801913}, issn = {1871-4080}, abstract = {To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.}, }
@article {pmid39801910, year = {2025}, author = {Wang, J and Zhang, L and Chen, S and Xue, H and Du, M and Xu, Y and Liu, S and Ming, D}, title = {Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {9}, pmid = {39801910}, issn = {1871-4080}, abstract = {Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.}, }
@article {pmid39798828, year = {2025}, author = {Wu, L and Jiang, M and Zhao, M and Hu, X and Wang, J and Zhang, K and Jia, K and Ren, F and Gao, F}, title = {Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model.}, journal = {NeuroImage}, volume = {306}, number = {}, pages = {121004}, doi = {10.1016/j.neuroimage.2025.121004}, pmid = {39798828}, issn = {1095-9572}, mesh = {Humans ; Male ; *Inhibition, Psychological ; *Magnetic Resonance Imaging ; Female ; Young Adult ; Adult ; Brain Mapping/methods ; Frontal Lobe/physiology/diagnostic imaging ; Psychomotor Performance/physiology ; Motor Cortex/physiology/diagnostic imaging ; Models, Neurological ; Executive Function/physiology ; }, abstract = {Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.}, }
@article {pmid39796947, year = {2024}, author = {Chio, N and Quiles-Cucarella, E}, title = {A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {1}, pages = {}, pmid = {39796947}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/methods ; *Evoked Potentials, Visual/physiology ; *Bibliometrics ; Imagination/physiology ; Rehabilitation/methods ; Electroencephalography/methods ; }, abstract = {In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain-computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.}, }
@article {pmid39796911, year = {2024}, author = {Özkahraman, A and Ölmez, T and Dokur, Z}, title = {Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {1}, pages = {}, pmid = {39796911}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Electrooculography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.}, }
@article {pmid39793200, year = {2025}, author = {Peters, B and Celik, B and Gaines, D and Galvin-McLaughlin, D and Imbiriba, T and Kinsella, M and Klee, D and Lawhead, M and Memmott, T and Smedemark-Margulies, N and Wiedrick, J and Erdogmus, D and Oken, B and Vertanen, K and Fried-Oken, M}, title = {RSVP keyboard with inquiry preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, pmid = {39793200}, issn = {1741-2552}, support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; Middle Aged ; *Communication Devices for People with Disabilities ; Young Adult ; Computer Peripherals ; Psychomotor Performance/physiology ; }, abstract = {Objective.The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce inquiry preview (IP), a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.Approach.Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of IP and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of IP and two modes of switch input on typing accuracy and speed during a copy-spelling task.Main results.For participants without disabilities, IP and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience (UX) with IP and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with IP and switch input and gave favorable UX ratings to those conditions, but preferred standard RSVP Keyboard.Significance.IP is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and UX and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.}, }
@article {pmid39793091, year = {2025}, author = {Zhang, F and Pu, Y and Kong, XZ}, title = {Parallel vector memories or single memory updating?.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {1}, pages = {e2422788121}, pmid = {39793091}, issn = {1091-6490}, support = {32171031//MOST | National Natural Science Foundation of China (NSFC)/ ; 32400882//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200409//STI 2030-Major Projects/ ; }, }
@article {pmid39793054, year = {2025}, author = {Chivukula, S and Aflalo, T and Zhang, C and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA}, title = {Population encoding of observed and actual somatosensations in the human posterior parietal cortex.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {1}, pages = {e2316012121}, pmid = {39793054}, issn = {1091-6490}, support = {P50 MH094258/MH/NIMH NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; P50MH094258//CIT | Caltech Conte Center for Social Decision Making (Caltech Conte Center for Neuroscience)/ ; R01EY015545//HHS | NIH | National Eye Institute (NEI)/ ; R01 EY015545/EY/NEI NIH HHS/United States ; }, mesh = {*Sensation/physiology ; *Parietal Lobe/physiology ; Neurons/physiology ; Touch/physiology ; Female ; Middle Aged ; Task Performance and Analysis ; }, abstract = {Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations. Here, we hypothesize that mirror-like responses represent one facet of a broader framework in which our brains engage internal models for cognition. We recorded populations of single neurons in the human posterior parietal cortex (PPC) of a brain-machine interface clinical trial participant implanted with a microelectrode array while she either experienced actual touch, or observed diverse tactile stimuli applied to other individuals. Two body locations were tested, on each of the participant and other individuals. Some neurons exhibited mirror-like properties, consistent with earlier literature. However, they were fragile, breaking with increased task complexity. Population responses were better characterized by generalizable and compositional basic-level features encoded within neural subspaces. These features enable the population to respond to diverse actual and observed touch stimuli and are recruited similarly for similar forms of touch. Mirror-like neurons belong within these subspaces, contributing more globally to compositionality and generalizability. We speculate that at a population-level, human PPC manifests an internal model for touch, and that cognition unfolds in the high-level human cortex by versatility in its representational building blocks. In a broad sense, we speculate that the population features we demonstrate support a broad mechanism by which the high-level human cortex enables understanding.}, }
@article {pmid39789264, year = {2025}, author = {Lin, N and Wang, S and Li, Y and Wang, B and Shi, S and He, Y and Zhang, W and Yu, Y and Zhang, Y and Zhang, X and Wong, K and Wang, S and Chen, X and Jiang, H and Zhang, X and Lin, P and Xu, X and Qi, X and Wang, Z and Shang, D and Liu, Q and Liu, M}, title = {Resistive memory-based zero-shot liquid state machine for multimodal event data learning.}, journal = {Nature computational science}, volume = {5}, number = {1}, pages = {37-47}, pmid = {39789264}, issn = {2662-8457}, support = {62422004//National Natural Science Foundation of China (National Science Foundation of China)/ ; Z210006//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, abstract = {The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware-software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.}, }
@article {pmid39788124, year = {2025}, author = {An, D and You, Y and Ma, Q and Xu, Z and Liu, Z and Liao, R and Chen, H and Wang, Y and Wang, Y and Dai, H and Li, H and Jiang, L and Chen, Z and Hu, W}, title = {Deficiency of histamine H2 receptors in parvalbumin-positive neurons leads to hyperactivity, impulsivity, and impaired attention.}, journal = {Neuron}, volume = {113}, number = {4}, pages = {572-589.e6}, doi = {10.1016/j.neuron.2024.12.002}, pmid = {39788124}, issn = {1097-4199}, mesh = {Animals ; *Impulsive Behavior/physiology ; *Receptors, Histamine H2/metabolism/genetics ; *Attention Deficit Disorder with Hyperactivity/metabolism/genetics ; Mice ; *Parvalbumins/metabolism ; Male ; *Neurons/metabolism ; Humans ; Substantia Nigra/metabolism ; Female ; Mice, Knockout ; Dopaminergic Neurons/metabolism ; Mice, Inbred C57BL ; Attention/physiology ; Dopamine Plasma Membrane Transport Proteins/metabolism/deficiency/genetics ; }, abstract = {Attention deficit hyperactivity disorder (ADHD), affecting 4% of the population, is characterized by inattention, hyperactivity, and impulsivity; however, its neurophysiological mechanisms remain unclear. Here, we discovered that deficiency of histamine H2 receptor (H2R) in parvalbumin-positive neurons in substantia nigra pars recticulata (PV[SNr]) attenuates PV[+] neuronal activity and induces hyperactivity, impulsivity, and inattention in mice. Moreover, decreased H2R expression was observed in PV[SNr] in patients with ADHD symptoms and dopamine-transporter-deficient mice, whose behavioral phenotypes were alleviated by H2R agonist treatment. Dysfunction of PV[SNr] efferents to the substantia nigra pars compacta dopaminergic neurons and superior colliculus differently contributes to H2R-deficiency-induced behavioral disorders. Collectively, our results demonstrate that H2R deficiency in PV[+] neurons contributes to hyperactivity, impulsivity, and inattention by dampening PV[SNr] activity and involving different efferents in mice. It may enhance understanding of the molecular and circuit-level basis of ADHD and afford new potential therapeutic targets for ADHD-like psychiatric diseases.}, }
@article {pmid39787896, year = {2025}, author = {Li, L and Menendez-Lustri, DM and Hartzler, A and Pogharian, A and Zaorski, B and Chen, A and Palen, J and Traylor, B and Quill, E and Pawlowski, CL and Bruckman, MA and Gupta, AS and Capadona, JR and Shoffstall, AJ}, title = {Systemically administered platelet-inspired nanoparticles to reduce inflammation surrounding intracortical microelectrodes.}, journal = {Biomaterials}, volume = {317}, number = {}, pages = {123082}, doi = {10.1016/j.biomaterials.2025.123082}, pmid = {39787896}, issn = {1878-5905}, support = {I01 RX003420/RX/RRD VA/United States ; }, mesh = {Animals ; Male ; *Microelectrodes ; *Rats, Sprague-Dawley ; *Nanoparticles/chemistry ; *Blood Platelets/metabolism ; *Inflammation ; Rats ; Blood-Brain Barrier/metabolism/drug effects ; Electrodes, Implanted ; }, abstract = {Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP). Systemically administered PINs can potentially enhance hemostasis and promote the resealing of IME insertion-induced leaky blood-brain barrier (BBB), thereby attenuating the influx of blood-derived proteins into the brain parenchyma that trigger neuroinflammation. This study explores the potential of PINs to mitigate neuroinflammation at implant sites. Male Sprague Dawley rats underwent craniotomies and IME implantations, followed by a single dose of Cy5 labeled PINs (2 mg/kg). Rats were sacrificed at intervals from 0 to 4 days post-implantation (DPI) for biodistribution analysis using an in vivo live imaging system (IVIS) and immunohistochemistry (IHC) to assess neuroinflammation, BBB permeability, and active platelet distribution. Another cohort of rats received weekly PINs, trehalose buffer (TH, diluent control), or control nanoparticles (CP, PEG-coated liposomes) for 4 weeks, with similar endpoint analyses. Results indicated that PIN concentrations were significantly elevated near IME interfaces acutely (0-4 DPI) and after 4 weeks of repeated dosing. At 3 DPI, peak intensities of active platelets (CD62P), activated microglia/macrophages (CD68), and PINs were observed. Immunoglobulin G (IgG) was upregulated during the first 24 h near implant sites but declined thereafter. At 4 weeks, the PINs group exhibited higher intensities of active platelets and PINs, and reduced CD68 and IgG levels compared to controls. PINs effectively targeted the IME-tissue interface, alongside endogenous activated platelets, resulting in reduced neuroinflammatory and BBB-leakage markers compared to the diluent-only-infused control group. Repeated dosing of PINs presents a promising approach for enhancing the quality of neural recordings in future studies.}, }
@article {pmid39787745, year = {2025}, author = {Elsohaby, I and Kostoulas, P and Fayez, M and Elmoslemany, A and Alkafafy, ME and Bahhary, AM and Alzahrani, R and Morsi, AEKM and Arango-Sabogal, JC}, title = {Bayesian estimation of diagnostic accuracy of fecal smears, fecal PCR and serum ELISA for detecting Mycobacterium avium subsp. paratuberculosis infections in four domestic ruminant species in Saudi Arabia.}, journal = {Veterinary microbiology}, volume = {301}, number = {}, pages = {110377}, doi = {10.1016/j.vetmic.2025.110377}, pmid = {39787745}, issn = {1873-2542}, mesh = {Animals ; *Mycobacterium avium subsp. paratuberculosis/isolation &amp; purification/immunology/genetics ; *Paratuberculosis/diagnosis/microbiology/epidemiology ; Saudi Arabia/epidemiology ; *Bayes Theorem ; *Enzyme-Linked Immunosorbent Assay/veterinary ; *Feces/microbiology ; Cattle ; *Goats ; Sheep ; *Sensitivity and Specificity ; Cross-Sectional Studies ; *Camelus/microbiology ; *Polymerase Chain Reaction/veterinary ; *Sheep Diseases/diagnosis/microbiology/epidemiology ; Cattle Diseases/diagnosis/microbiology/epidemiology ; Goat Diseases/microbiology/diagnosis/epidemiology ; Ruminants/microbiology ; }, abstract = {Paratuberculosis, a chronic wasting disease affecting domestic and wild ruminants worldwide, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Various diagnostic tests exist for detecting MAP infection; however, none of them possess perfect accuracy to be qualified as a reference standard test, particularly due to their notably low sensitivity. Therefore, we used Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of fecal smears (FS), fecal PCR and serum ELISA for detecting MAP infections in sheep, goats, cattle, and camels older than 2 years in Saudi Arabia. Data from a cross-sectional study conducted in the Eastern Province of Saudi Arabia on 31 different farms with a history of MAP infection were analyzed. Fecal and blood samples from all animals older than 2 years in each farm were collected, resulting in a total of 220 sheep, 123 goats, 66 cattle, and 240 camels sampled. FS and IS900-PCR were performed on fecal samples to detect acid-fast bacilli and MAP DNA, respectively. The IDEXX ELISA kit was used to detect MAP antibodies in serum samples. For each ruminant species population, a BLCM was fitted to obtain posterior estimates [medians and 95 % Bayesian credible intervals (95 % BCI)] for sensitivity (Se) and specificity (Sp) of the three tests. We assumed FS and PCR to be conditionally dependent on the true animal MAP status. Prior distributions for test accuracy were used if available. FS had the highest Se among all tests and across all species with median values around 80 % in sheep, goats and camels, and near 50 % in cattle. Median Sp estimates of ELISA and PCR were higher than 90 % for all species. FS yielded the lowest Sp of the study when applied in camels, sheep, and goats. Using the prevalence observed in this study, median positive predictive value (PPV) was higher for PCR and ELISA than FS for camels, sheep, and goats. In cattle, PPV of all tests was similar with median estimates > 95 %. In camels, sheep, and goats, median negative predicative value (NPV) of all tests were > 60 %. The lowest median NPV for all tests were observed in cattle (< 30 %). Our results suggest that ELISA is a suitable option to identify MAP infected animals in farms with previous history of MAP in the Eastern region of Saudi Arabia.}, }
@article {pmid39787440, year = {2024}, author = {Alkawadri, CI and Yan, Q and Kocuglu Kinal, AG and Spencer, DD and Alkawadri, R}, title = {Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes.}, journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society}, volume = {}, number = {}, pages = {}, doi = {10.1097/WNP.0000000000001139}, pmid = {39787440}, issn = {1537-1603}, abstract = {OBJECTIVES: Our study aimed to compare signal characteristics of subdural electrodes (SDE) and depth stereo EEG placed within a 5-mm vicinity in patients with drug-resistant epilepsy. We report how electrode design and placement collectively affect signal content from a shared source between these electrode types.<br><br>METHODS: In subjects undergoing invasive intracranial EEG evaluation at a surgical epilepsy center from 2012 to 2018, stereo EEG and SDE electrode contacts placed within a 5-mm vicinity were identified. Of these, 24 contacts (12 pairs) met our criteria for signal-to-noise ratio and data availability for final analysis. We used Welch method to analyze the correlation of power spectral densities of EEG segments, root mean square of 1-second windows, and fast-Fourier transform to calculate coherence across conventional frequency bands.<br><br>RESULTS: We observed a median distance of 3.7 mm between the electrode contact pairs. Time-aware analysis highlighted the coherence's strength primarily in the high-gamma band, where the median (r) was 0.889. In addition, the median power ratios between the SDE and stereo EEG signal was 1.99. This ratio decreased from high-gamma to infra-low frequencies, with medians of 2.07 and 0.97, respectively. The power spectral densities for the stereo EEG and SDE electrodes demonstrated a strong correlation, with a median correlation coefficient (r) of 0.99 and an interquartile range from 0.915 to 0.996.<br><br>CONCLUSIONS: Signals captured by standard subdural and depth (intracranial EEG) electrodes within a 5-mm radius exhibit band-specific coherence and are not identical. The association was most pronounced in the high-gamma band, with coherence decreasing with lower frequencies. Our findings underscore the combined effects of electrode size, design, placement, preferred bandwidth, and the nature of the activity source on signal recording. Particularly, SDE employed herein may offer advantages for high-frequency signals, but the impact of electrode size on recordings necessitates careful consideration in context-specific situations.<br><br>SIGNIFICANCE: The findings relate to surgical epilepsy care and may inform the design of brain-computer interface.}, }
@article {pmid39781056, year = {2025}, author = {Cai, Z and Li, P and Cheng, L and Yuan, D and Li, M and Li, H}, title = {A high performance heterogeneous hardware architecture for brain computer interface.}, journal = {Biomedical engineering letters}, volume = {15}, number = {1}, pages = {217-227}, pmid = {39781056}, issn = {2093-985X}, abstract = {Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms. This paper proposes a heterogeneous BCI architecture based on ARM + FPGA, enabling real-time processing of electroencephalogram (EEG) signals. Adopting data quantization, layer fusion and data augmentation to optimize the compact neural network model EEGNet, and design dedicated hardware engines to accelerate the network. Experimental results show that the system achieves 93.3% classification accuracy for steady-state visual evoked potential signals, with a time delay of 0.2 ms per trail, and a power consumption of approximately (1.91 W). That is 31.5 times faster acceleration is realized at the cost of only 0.7% lower accuracy compared with the conventional processor. The results show that the BCI architecture proposed in this study has strong practicability and high research significance.}, }
@article {pmid39779796, year = {2025}, author = {Shelishiyah, R and Thiyam, DB and Margaret, MJ and Banu, NMM}, title = {A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {1360}, pmid = {39779796}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; Deep Learning ; Adult ; Male ; Movement/physiology ; }, abstract = {The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals. The current work focuses on the performance of deep learning methods like - Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. The model performance was evaluated using metrics such as Accuracy, F1 - score, Precision, Recall, AUC and ROC curve. The CNN and Hybrid CNN models have resulted in 98.3% and 99% accuracy respectively.}, }
@article {pmid39776784, year = {2024}, author = {Ahmadi, S and Desain, P and Thielen, J}, title = {A Bayesian dynamic stopping method for evoked response brain-computer interfacing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1437965}, pmid = {39776784}, issn = {1662-5161}, abstract = {INTRODUCTION: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data.<br><br>METHODS: We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimization approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications.<br><br>RESULTS AND DISCUSSION: We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.}, }
@article {pmid39775950, year = {2025}, author = {Zeng, X and Kang, T and Huang, W and Jin, T}, title = {How Should the BCI and BOOI Index be Correctly Applied in Patients With Low-Compliance Bladder?.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25663}, pmid = {39775950}, issn = {1520-6777}, }
@article {pmid39771903, year = {2024}, author = {Alzahrani, S and Banjar, H and Mirza, R}, title = {Systematic Review of EEG-Based Imagined Speech Classification Methods.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771903}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; Machine Learning ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.}, }
@article {pmid39771862, year = {2024}, author = {Khabti, J and AlAhmadi, S and Soudani, A}, title = {Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771862}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; Internet of Things ; Signal Processing, Computer-Assisted ; }, abstract = {One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs. The MI tasks are recognized through EEG signal processing and classification, which can drain sensor energy due to the complexity of the data and the presence of redundant information, often influenced by subject-dependent factors. To address these challenges, we propose in this paper a multi-subject transfer-learning approach for an efficient MI training framework in remote rehabilitation within an IoT environment. For efficient implementation, we propose an IoT architecture that includes cloud/edge computing as a solution to enhance the system's efficiency and reduce the use of network resources. Furthermore, deep-learning classification with and without channel selection is applied in the cloud, while multi-subject transfer-learning classification is utilized at the edge node. Various transfer-learning strategies, including different epochs, freezing layers, and data divisions, were employed to improve accuracy and efficiency. To validate this framework, we used the BCI IV 2a dataset, focusing on subjects 7, 8, and 9 as targets. The results demonstrated that our approach significantly enhanced the average accuracy in both multi-subject and single-subject transfer-learning classification. In three-subject transfer-learning classification, the FCNNA model achieved up to 79.77% accuracy without channel selection and 76.90% with channel selection. For two-subject and single-subject transfer learning, the application of transfer learning improved the average accuracy by up to 6.55% and 12.19%, respectively, compared to classification without transfer learning. This framework offers a promising solution for remote MI rehabilitation, providing both accurate task recognition and efficient resource usage.}, }
@article {pmid39771843, year = {2024}, author = {Mikhaylov, D and Saeed, M and Husain Alhosani, M and F Al Wahedi, Y}, title = {Comparison of EEG Signal Spectral Characteristics Obtained with Consumer- and Research-Grade Devices.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771843}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Brain/physiology ; Wearable Electronic Devices ; Electrodes ; Young Adult ; }, abstract = {Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices.}, }
@article {pmid39771785, year = {2024}, author = {Novičić, M and Djordjević, O and Miler-Jerković, V and Konstantinović, L and Savić, AM}, title = {Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771785}, issn = {1424-8220}, support = {6066223//Science Fund of the Republic of Serbia/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Somatosensory/physiology ; Male ; *Machine Learning ; Adult ; Female ; Algorithms ; Touch/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Support Vector Machine ; }, abstract = {Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory-motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.}, }
@article {pmid39771721, year = {2024}, author = {Víg, L and Zátonyi, A and Csernyus, B and Horváth, &#xc1;C and Bojtár, M and Kele, P and Madarász, M and Rózsa, B and Fürjes, P and Hermann, P and Hakkel, O and Péter, L and Fekete, Z}, title = {Optically Controlled Drug Delivery Through Microscale Brain-Machine Interfaces Using Integrated Upconverting Nanoparticles.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771721}, issn = {1424-8220}, support = {TKP2021-EGA-42//National Research, Development and Innovation Office/ ; TKP2021-EGA-04//National Research, Development and Innovation Office/ ; VKE-2018-00032//National Research, Development and Innovation Office/ ; KFI-2018-00097//National Research, Development and Innovation Office/ ; 2020-2.1.1-ED-2022-00208//National Research, Development and Innovation Office/ ; (NAP2022I-8/2022//Hungarian Academy of Sciences/ ; Bolyai Janos Scholarship//Hungarian Academy of Sciences/ ; NKFIH FK 134403//National Research, Development and Innovation Office/ ; }, mesh = {*Nanoparticles/chemistry ; *Brain-Computer Interfaces ; *Drug Delivery Systems/methods/instrumentation ; Animals ; Brain/physiology ; Rats ; }, abstract = {The aim of this work is to incorporate lanthanide-cored upconversion nanoparticles (UCNP) into the surface of microengineered biomedical implants to create a spatially controlled and optically releasable model drug delivery device in an integrated fashion. Our approach enables silicone-based microelectrocorticography (ECoG) implants holding platinum/iridium recording sites to serve as a stable host of UCNPs. Nanoparticles excitable in the near-infrared (lower energy) regime and emitting visible (higher energy) light are utilized in a study. With the upconverted higher energy photons, we demonstrate the induction of photochemical (cleaving) reactions that enable the local release of specific dyes as a model system near the implant. The modified ECoG electrodes can be implanted in brain tissue to act as an uncaging system that releases small amounts of substance while simultaneously measuring the evoked neural response upon light activation. In this paper, several technological challenges like the surface modification of UCNPs, the immobilization of particles on the implantable platform, and measuring the stability of integrated UCNPs in in vitro and in vivo conditions are addressed in detail. Besides the chemical, mechanical, and optical characterization of the ready-to-use devices, the effect of nanoparticles on the original electrophysiological function is also evaluated. The results confirm that silicone-based brain-machine interfaces can be efficiently complemented with UCNPs to facilitate local model drug release.}, }
@article {pmid39771689, year = {2024}, author = {Fang, F and Gao, T and Wu, J}, title = {Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771689}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; }, abstract = {Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test". In this context, "human nature" refers to emotion and consciousness, while "test" involves a critical analysis of AI technology and an exploration of the differences between humanity and technicality. Additionally, through experimental research and literature analysis, we found that the integration of electroencephalogram (EEG) data with AI systems is becoming a significant trend. The experiment involved 20 participants, with two conditions: C1 (using EEG data) and C2 (without EEG data). The results indicated a significant increase in immersion under the C1 condition, along with a more positive emotional experience. We summarized three design directions: enhancing immersion, creating emotional experiences, and expressing philosophical concepts. Based on these findings, there is potential for further developing EEG data as a medium to enrich interactive experiences, offering new insights into the fusion of technology and human emotion.}, }
@article {pmid39771656, year = {2024}, author = {Khalil, AEK and Perez-Diaz, JA and Cantoral-Ceballos, JA and Antelis, JM}, title = {Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771656}, issn = {1424-8220}, support = {N/A//Tecnol&#xf3;gico de Monterrey/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Computer Security ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; }, abstract = {With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user's intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.}, }
@article {pmid39768099, year = {2024}, author = {Mahmoud, TSM and Munawar, A and Nawaz, MZ and Chen, Y}, title = {Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {12}, pages = {}, pmid = {39768099}, issn = {2306-5354}, abstract = {Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.}, }
@article {pmid39768034, year = {2024}, author = {Geravanchizadeh, M and Shaygan Asl, A and Danishvar, S}, title = {Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {12}, pages = {}, pmid = {39768034}, issn = {2306-5354}, abstract = {Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain-computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.}, }
@article {pmid39766488, year = {2024}, author = {Ma, Y and Huang, Z and Yang, Y and Zhang, S and Dong, Q and Wang, R and Hu, L}, title = {Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766488}, issn = {2076-3425}, abstract = {BACKGROUND: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.<br><br>METHODS: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network's performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model's accuracy.<br><br>RESULTS: The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models.<br><br>CONCLUSIONS: This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.}, }
@article {pmid39766471, year = {2024}, author = {Adolf, A and Köllőd, CM and Márton, G and Fadel, W and Ulbert, I}, title = {The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766471}, issn = {2076-3425}, support = {KDP-2021-12, 1022428/001//National Research, Development and Innovation Office/ ; FK146115//National Research, Development and Innovation Office/ ; NAP2022-I-2/2022//Hungarian Academy of Sciences/ ; RRF-2.3.1-21-2022-00015//National Research, Development and Innovation Office/ ; }, abstract = {Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.}, }
@article {pmid39766454, year = {2024}, author = {Beck, S and Liberman, Y and Dubljević, V}, title = {Media Representation of the Ethical Issues Pertaining to Brain-Computer Interface (BCI) Technology.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766454}, issn = {2076-3425}, abstract = {BACKGROUND/OBJECTIVES: Brain-computer interfaces (BCIs) are a rapidly developing technology that captures and transmits brain signals to external sources, allowing the user control of devices such as prosthetics. BCI technology offers the potential to restore physical capabilities in the body and change how we interact and communicate with computers and each other. While BCI technology has existed for decades, recent developments have caused the technology to generate a host of ethical issues and discussions in both academic and public circles. Given that media representation has the potential to shape public perception and policy, it is necessary to evaluate the space that these issues take in public discourse.<br><br>METHODS: We conducted a rapid review of media articles in English discussing ethical issues of BCI technology from 2013 to 2024 as indexed by LexisNexis. Our searches yielded 675 articles, with a final sample containing 182 articles. We assessed the themes of the articles and coded them based on the ethical issues discussed, ethical frameworks, recommendations, tone, and application of technology.<br><br>RESULTS: Our results showed a marked rise in interest in media articles over time, signaling an increased focus on this topic. The majority of articles adopted a balanced or neutral tone when discussing BCIs and focused on ethical issues regarding privacy, autonomy, and regulation.<br><br>CONCLUSIONS: Current discussion of ethical issues reflects growing news coverage of companies such as Neuralink, and reveals a mounting distrust of BCI technology. The growing recognition of ethical considerations in BCI highlights the importance of ethical discourse in shaping the future of the field.}, }
@article {pmid39760490, year = {2025}, author = {Zhai, H and Li, P and Wang, H and Wang, X}, title = {DMSO-promoted α-bromination of α-aryl ketones for the construction of 2-aryl-2-bromo-cycloketones.}, journal = {Organic &amp; biomolecular chemistry}, volume = {23}, number = {7}, pages = {1627-1632}, doi = {10.1039/d4ob01937g}, pmid = {39760490}, issn = {1477-0539}, abstract = {A DMSO-promoted practical one-step α-bromination reaction of α-aryl ketones with NBS has been developed for the construction of 2-aryl-2-bromo-cycloketones. The desired regioselective α-bromination products were isolated in moderate to good yields, with a maximum tested scale of 15 mmol. Notably, ketamine derivatives could be smoothly synthesized in two steps.}, }
@article {pmid39760422, year = {2025}, author = {Zhu, L and Wang, Y and Huang, A and Tan, X and Zhang, J}, title = {A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2024.2448576}, pmid = {39760422}, issn = {1476-8259}, abstract = {Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.}, }
@article {pmid39759760, year = {2024}, author = {Cisotto, G and Zancanaro, A and Zoppis, IF and Manzoni, SL}, title = {hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction.}, journal = {Frontiers in neuroinformatics}, volume = {18}, number = {}, pages = {1459970}, pmid = {39759760}, issn = {1662-5196}, abstract = {INTRODUCTION: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.<br><br>METHODS: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects).<br><br>RESULTS: We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before.<br><br>DISCUSSION: Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.}, }
@article {pmid39759080, year = {2024}, author = {Zhao, X and Xu, S and Geng, K and Zhou, T and Xu, T and Wang, Z and Feng, S and Hu, H}, title = {MP: A steady-state visual evoked potential dataset based on multiple paradigms.}, journal = {iScience}, volume = {27}, number = {11}, pages = {111030}, pmid = {39759080}, issn = {2589-0042}, abstract = {In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size" and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.}, }
@article {pmid39758181, year = {2025}, author = {Ammar, A and Salem, A and Simak, ML and Horst, F and Schöllhorn, WI}, title = {Acute effects of motor learning models on technical efficiency in strength-coordination exercises: a comparative analysis of Olympic snatch biomechanics in beginners.}, journal = {Biology of sport}, volume = {42}, number = {1}, pages = {151-161}, pmid = {39758181}, issn = {0860-021X}, abstract = {Despite the development of various motor learning models over many decades, the question of which model is most effective under which conditions to optimize the acquisition of skills remains a heated and recurring debate. This is particularly important in connection with learning sports movements with a high strength component. This study aims to examine the acute effects of various motor learning models on technical efficiency and force production during the Olympic snatch movement. In a within-subject design, sixteen highly active male participants (mean age: 23.13 ± 2.09 years), who were absolute beginners regarding the learning task, engaged in randomized snatch learning bouts, consisting of 36 trials across different learning models: differential learning (DL), contextual interference (serial, sCI; and blocked, bCI), and repetitive learning (RL). Kinematic and kinetic data were collected from three snatch trials executed following each learning bout. Discrete data from the most commonly monitored biomechanical parameters in Olympic weightlifting were analyzed using inferential statistics to identify differences between learning models. The statistical analysis revealed no significant differences between the learning models across all tested parameters, with p-values ranging from 0.236 to 0.99. However, it was observed that only the bouts with an exercise sequence following the DL model resulted in an average antero-posterior displacement of the barbell that matched the optimal displacement. This was characterized by a mean positive displacement towards the lifter during the pulling phases, a negative displacement away from the lifter in the turnover phase, and a return to positive displacement in the catch phase. These findings indicate the limited acute impact of the exercise sequences based on the three motor learning models on Olympic snatch technical efficiency in beginners, yet they hint at a possible slight advantage for the DL model. Coaches might therefore consider incorporating the DL model to potentially enhance technical efficiency, especially during the early stages of skill acquisition. Future research, involving even bigger amounts of exercise noise, longer learning periods, or a greater number of total learning trials and sessions, is essential to verify the potential advantages of the DL model for weightlifting technical efficiency.}, }
@article {pmid39758128, year = {2025}, author = {Wang, B and Zhang, Y and Li, H and Dou, H and Guo, Y and Deng, Y}, title = {Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.}, journal = {National science review}, volume = {12}, number = {1}, pages = {nwae301}, pmid = {39758128}, issn = {2053-714X}, abstract = {The pursuit of artificial neural networks that mirror the accuracy, efficiency and low latency of biological neural networks remains a cornerstone of artificial intelligence (AI) research. Here, we incorporated recent neuroscientific findings of self-inhibiting autapse and neuron heterogeneity for innovating a spiking neural network (SNN) with enhanced learning and memorizing capacities. A bi-level programming paradigm was formulated to respectively learn neuron-level biophysical variables and network-level synapse weights for nested heterogeneous learning. We successfully demonstrated that our biologically inspired neuron model could reproduce neural statistics at both individual and group levels, contributing to the effective decoding of brain-computer interface data. Furthermore, the heterogeneous SNN showed higher accuracy (1%-10% improvement), superior efficiency (maximal 17.83-fold reduction in energy) and lower latency (maximal 5-fold improvement) in performing several AI tasks. For the first time, we benchmarked SNN for conducting cell type identification from scRNA-seq data. The proposed model correctly identified very rare cell types associated with severe brain diseases where typical SNNs failed.}, }
@article {pmid39757218, year = {2025}, author = {Kim, MS and Park, H and Kwon, I and An, KO and Kim, H and Park, G and Hyung, W and Im, CH and Shin, JH}, title = {Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {1}, pmid = {39757218}, issn = {1743-0003}, support = {NRCTR-IN20001//Translational Research Program for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Republic of Korea/ ; }, mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Chronic Disease ; Double-Blind Method ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Imagination/physiology ; *Neuronal Plasticity/physiology ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; Treatment Outcome ; *Upper Extremity/physiopathology ; *Feedback ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.<br><br>METHODS: This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.<br><br>RESULTS: Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference&#x2009;=&#x2009;0.52, 95% CI&#x2009;=&#x2009;0.03-1.00, p&#x2009;=&#x2009;0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p&#x2009;=&#x2009;0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional connectivity.<br><br>CONCLUSIONS: BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.<br><br>TRIAL REGISTRATION: CRIS (KCT0009013).}, }
@article {pmid39755291, year = {2025}, author = {Vogeley, AO and Livinski, AA and Dabaghi Varnosfaderani, S and Javaheripour, N and Jamalabadi, H and Kotoula, V and Henter, ID and Hejazi, NS and Price, RB and Yavi, M and Walter, M and Zarate, CA and Kheirkhah, M}, title = {Temporal dynamics of affective scene processing in the healthy adult human brain.}, journal = {Neuroscience and biobehavioral reviews}, volume = {169}, number = {}, pages = {106003}, pmid = {39755291}, issn = {1873-7528}, support = {ZIA MH002857/ImNIH/Intramural NIH HHS/United States ; ZIA MH002927/ImNIH/Intramural NIH HHS/United States ; }, mesh = {Humans ; *Brain/physiology ; *Emotions/physiology ; Magnetoencephalography ; Electroencephalography ; Adult ; Affect/physiology ; Time Factors ; }, abstract = {Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds. The review includes studies that used electroencephalography (EEG) or magnetoencephalography (MEG) in healthy adults to examine brain responses to emotional versus neutral images within one second. Articles of interest were limited to the English language but not to any publication year. Excluded studies involved only patients (of any diagnosis), participants under age 18 (since emotional processing can differ between adults and younger individuals), non-passive tasks, low temporal resolution techniques, time intervals over one second, and animals. Of the 3045 screened articles, 19 met these criteria. Despite the variations between studies, the earliest onset for heightened brain responses to basic affective scenes compared to neutral ones was most commonly observed within the 250-300 ms time window. To the best of our knowledge, this review is the first to synthesize data on the timing of brain differentiation between emotional and neutral scenes in healthy adults.}, }
@article {pmid39755229, year = {2025}, author = {Ma, X and Xue, S and Ma, H and Saeed, S and Zhang, Y and Meng, Y and Chen, H and Yu, H and Wang, H and Hu, S and Cai, M}, title = {Esketamine alleviates LPS-induced depression-like behavior by activating Nrf2-mediated anti-inflammatory response in adolescent mice.}, journal = {Neuroscience}, volume = {567}, number = {}, pages = {294-307}, doi = {10.1016/j.neuroscience.2024.12.062}, pmid = {39755229}, issn = {1873-7544}, mesh = {Animals ; *Ketamine/pharmacology ; *NF-E2-Related Factor 2/metabolism ; *Lipopolysaccharides/pharmacology ; Male ; Mice ; *Depression/drug therapy/chemically induced/metabolism ; *Mice, Inbred C57BL ; Antidepressive Agents/pharmacology ; Hippocampus/drug effects/metabolism ; Anti-Inflammatory Agents/pharmacology ; Cytokines/metabolism ; Prefrontal Cortex/drug effects/metabolism ; Inflammation/metabolism/drug therapy/chemically induced ; }, abstract = {BACKGROUND: The mechanisms underlying esketamine's therapeutic effects remain elusive. The study aimed to explore the impact of single esketamine treatment on LPS-induced adolescent depressive-like behaviors and the role of Nrf2 regulated neuroinflammatory response in esketamine-produced rapid antidepressant efficacy.<br><br>METHODS: Adolescent male C57BL/6J mice were randomly assigned to three groups: control, LPS, and LPS&#xa0;+&#xa0;esketamine (15&#xa0;mg/kg, i.p.). Depressive-like behaviors were evaluated via the OFT, NFST, and TST. Protein expression of Nrf2 and inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in the hippocampus and mPFC, were measured by western blot. Moreover, the Nrf2 inhibitor, ML385, was also applied in the current study. The depressive-like behaviors and the protein expression of Nrf2, TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in mPFC and hippocampus were also measured. Additionally, the plasma's pro-inflammatory cytokines and anti-inflammatory cytokines were assessed using ELISA methods with or without ML385.<br><br>RESULTS: A single administration of esketamine treatment alleviated the LPS-induced depressive-like behaviors. Esketamine increased the expression of Nrf2 and reduced the expression of the inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS, in the mPFC and hippocampus. Notably, pharmacological inhibition of Nrf2 via ML385 administration abrogated the antidepressive-like behaviors and anti-inflammatory effects induced by esketamine. In the periphery, esketamine mitigated the LPS-induced elevation of pro-inflammatory cytokines, and the reduction of anti-inflammatory cytokines, and this effect was reversed by Nrf2 inhibition.<br><br>CONCLUSION: Esketamine treatment exerts rapid antidepressant effects and attenuates neuroinflammation in LPS-induced adolescent depressive-like behaviors, potentially through the activation of Nrf2-mediated anti-inflammatory signaling.}, }
@article {pmid39755222, year = {2025}, author = {Huang, J and Wei, S and Gao, Z and Jiang, S and Wang, M and Sun, L and Ding, W and Zhang, D}, title = {Local structural-functional coupling with counterfactual explanations for epilepsy prediction.}, journal = {NeuroImage}, volume = {306}, number = {}, pages = {120978}, doi = {10.1016/j.neuroimage.2024.120978}, pmid = {39755222}, issn = {1095-9572}, mesh = {Humans ; *Epilepsy/diagnostic imaging/physiopathology ; *Diffusion Tensor Imaging/methods ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiopathology ; White Matter/diagnostic imaging/physiopathology ; Nerve Net/diagnostic imaging/physiopathology ; Male ; Female ; Adult ; Connectome/methods ; Brain Mapping/methods ; }, abstract = {The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.}, }
@article {pmid39755127, year = {2025}, author = {Chu, T and Si, X and Song, X and Che, K and Dong, F and Guo, Y and Chen, D and Yao, W and Zhao, F and Xie, H and Shi, Y and Ma, H and Ming, D and Mao, N}, title = {Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.}, journal = {Journal of affective disorders}, volume = {373}, number = {}, pages = {219-226}, doi = {10.1016/j.jad.2024.12.082}, pmid = {39755127}, issn = {1573-2517}, mesh = {Humans ; *Depressive Disorder, Major/physiopathology/psychology ; Male ; Female ; *White Matter/physiopathology/pathology/diagnostic imaging ; Adult ; *Suicidal Ideation ; Magnetic Resonance Imaging ; Suicide, Attempted/psychology ; Middle Aged ; Diffusion Tensor Imaging ; Case-Control Studies ; Connectome ; Young Adult ; }, abstract = {PURPOSE: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).<br><br>METHODS: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS).<br><br>RESULTS: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (PFDR&#xa0;<&#xa0;0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r&#xa0;=&#xa0;0.269, PFDR&#xa0;=&#xa0;0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r&#xa0;=&#xa0;-0.159, PFDR&#xa0;=&#xa0;0.036). An increase in left anterior limb of internal capsule (PBonferroni&#xa0;<&#xa0;0.01) and left corticospinal tract (PBonferroni&#xa0;<&#xa0;0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (PBonferroni&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD.}, }
@article {pmid39754303, year = {2025}, author = {Qiu, C and Zhang, D and Wang, M and Mei, X and Chen, W and Yu, H and Yin, W and Peng, G and Hu, S}, title = {Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {1}, pages = {e70204}, pmid = {39754303}, issn = {1755-5949}, support = {2022YFC3602604//National Key Research and Development Program of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation Ten Thousand Talents Program of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; JNL 2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2022KTZ004//Chinese Medical Education Association/ ; 2022 F28//NINGBO Medical Health Leading Academic Discipline Project/ ; }, mesh = {Humans ; *Alzheimer Disease/immunology/diagnosis ; *Lewy Body Disease/immunology/diagnosis ; Aged ; Female ; Male ; *Leukocytes, Mononuclear/immunology/metabolism ; Aged, 80 and over ; Single-Cell Analysis/methods ; Middle Aged ; Biomarkers/blood ; T-Lymphocytes/immunology ; }, abstract = {OBJECTIVE: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are common neurodegenerative diseases with distinct but overlapping pathogenic mechanisms. The clinical similarities between these diseases often result in high misdiagnosis rates, leading to serious consequences. Peripheral blood mononuclear cells (PBMCs) are easy to collect and can accurately reflect the immune characteristics of both DLB and AD.<br><br>METHODS: We utilized time-of-flight mass cytometry (CyTOF) with single-cell resolution to quantitatively analyze peripheral PBMCs, identifying 1228 immune characteristics. Based on the top-selected immune features, we constructed immunological elastic net (iEN) models.<br><br>RESULTS: These models demonstrated high diagnostic efficacy in distinguishing diseased samples from healthy donors as well as distinguishing AD and DLB cases. The selected features reveal that the primary peripheral immune characteristic of AD is a decrease in total T cells, while DLB is characterized by low expression of I-kappa-B-alpha (IKB&#x3b1;) in the classical monocyte subset.<br><br>CONCLUSIONS: These findings suggest that peripheral immune characteristics could serve as potential biomarkers, facilitating the diagnosis of neurodegenerative diseases.}, }
@article {pmid39753090, year = {2025}, author = {Pang, Y and Zhao, S and Zhang, Z and Xu, J and Gao, L and Zhang, R and Li, Z and Lu, F and Chen, H and Wu, H and Chen, M and Chen, K and Wang, J}, title = {Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment.}, journal = {Journal of psychiatric research}, volume = {181}, number = {}, pages = {709-715}, doi = {10.1016/j.jpsychires.2024.12.032}, pmid = {39753090}, issn = {1879-1379}, mesh = {Humans ; *Connectome ; Male ; Adult ; Female ; Young Adult ; Adolescent ; *Magnetic Resonance Imaging ; *Brain/diagnostic imaging/growth &amp; development/pathology ; *Adult Survivors of Child Abuse ; Child Abuse ; Age Factors ; Child ; }, abstract = {BACKGROUND: The long-term impact of childhood maltreatment (CM) on an individual's physical and mental health is suggested to be mediated by altered neurodevelopment. However, the exact neurobiological consequences of CM remain unclear.<br><br>METHODS: The present study aimed to investigate the relationship between CM and brain age based on structural magnetic resonance imaging data from a sample of 214 adults. The participants were divided into CM and non_CM groups according to Childhood Trauma Questionnaire. For each participant, brain connectome age was estimated from a large-scale structural covariance network through relevance vector regression. Brain predicted age difference (brain_PAD) was then calculated for each participant.<br><br>RESULTS: The brain connectome age matched well with chronological age in young adults (age range: 18-25 years) and adults (age range: 26-44 years) without CM, but not in individuals with CM. Compared with non_CM group, CM group was characterized by higher brain_PAD scores in young adults, whereas lower brain_PAD scores in adults. The finding revealed that brain development in individuals with CM seems to be accelerated in younger adults but retardation with increasing age. Moreover, individuals who suffered child abuse (but not neglect) showed higher brain_PAD scores than non_CM group, suggesting the different influence of abuse and neglect on neurodevelopment. Finally, the brain_PAD was positively correlated with attentional impulsivity in CM group.<br><br>CONCLUSIONS: CM affects different stages of adult brain development differently, and abuse and neglect have different influenced patterns, which may provide new evidence for the impact of CM on structural brain development.}, }
@article {pmid39752412, year = {2025}, author = {Ding, C and Kim Geok, S and Sun, H and Roslan, S and Cao, S and Zhao, Y}, title = {Does music counteract mental fatigue? A systematic review.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0316252}, pmid = {39752412}, issn = {1932-6203}, mesh = {*Mental Fatigue/prevention &amp; control ; Humans ; *Music/psychology ; *Cognition/physiology ; Music Therapy/methods ; }, abstract = {INTRODUCTION: Mental fatigue, a psychobiological state induced by prolonged and sustained cognitive tasks, impairs both cognitive and physical performance. Several studies have investigated strategies to counteract mental fatigue. However, potential health risks and contextual restrictions often limit these strategies, which hinder their practical application. Due to its noninvasive and portable nature, music has been proposed as a promising strategy to counteract mental fatigue. However, the effects of music on performance decrements vary with different music styles. Synthesizing studies that systematically report music style and its impact on counteracting performance decrements is crucial for theoretical and practical applications.<br><br>OBJECTIVES: This review aims to provide a comprehensive systematic analysis of different music styles in counteracting mental fatigue and their effects on performance decrements induced by mental fatigue. Additionally, the mechanisms by which music counteracts mental fatigue will be discussed.<br><br>METHODS: A comprehensive search was conducted across five databases-Web of Science, PubMed, SCOPUS, SPORTDiscus via EBSCOhost, and the Psychological and Behavioral Sciences Collection via EBSCOhost-up to November 18, 2023. The selected studies focused solely on music interventions, with outcomes including subjective feelings of mental fatigue, physiological markers, and both cognitive and behavioral performance.<br><br>RESULTS: Nine studies met the predetermined criteria for inclusion in this review. The types of music interventions that counteract mental fatigue include relaxing, exciting, and personal preference music, all of which were associated with decreased subjective feelings of mental fatigue and changes in objective physiological markers. Cognitive performance, particularly in inhibition and working memory tasks impaired by mental fatigue, was countered by both relaxing and exciting music. Exciting music was found to decrease reaction time more effectively than relaxing music in working memory tasks. The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. Behavioral performance in tasks such as arm-pointing, the Yo-Yo intermittent test, and the 5 km time-trial, which were impaired by mental fatigue, were counteracted by personal preference music.<br><br>CONCLUSION: Relaxing music, exciting music, and personal preference music effectively counteract mental fatigue by reducing feelings of fatigue and mitigating performance decrements. Individuals engaged in mentally demanding tasks can effectively counteract concurrent or subsequent cognitive performance decrements by simultaneously listening to relaxing or exciting music without lyrics or by using music during recovery from mental fatigue. Exciting music is more effective than relaxing music in counteracting mental fatigue. Personal preference music is effective in counteracting behavioral performance decrements in motor control and endurance tasks. Mentally fatigued individuals could apply personal preference music to counteract subsequent motor control performance decrements or simultaneously listen to it to counteract endurance performance decrements. Future studies should specify and examine the effects of different music genres, tempos, and intensities in counteracting mental fatigue. Additionally, the role of music in counteracting mental fatigue in contexts such as work productivity, traffic accident risk, and sports requires further investigation, along with the underlying mechanisms.}, }
@article {pmid39752045, year = {2025}, author = {Xin, Q and Hu, H}, title = {To Attack or Not: A Neural Circuit Coding Sexually Dimorphic Aggression.}, journal = {Neuroscience bulletin}, volume = {41}, number = {4}, pages = {728-730}, pmid = {39752045}, issn = {1995-8218}, }
@article {pmid39748261, year = {2025}, author = {Wu, JY and Zhang, JY and Xia, WQ and Kang, YN and Liao, RY and Chen, YL and Li, XM and Wen, Y and Meng, FX and Xu, LL and Wen, SH and Liu, HF and Li, YQ and Gu, JR and Lv, Q and Ren, Y}, title = {Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.}, journal = {Arthritis research &amp; therapy}, volume = {27}, number = {1}, pages = {1}, pmid = {39748261}, issn = {1478-6362}, support = {No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; Retrospective Studies ; *Sjogren's Syndrome/immunology/diagnosis ; Female ; Male ; Middle Aged ; *Thyroiditis, Autoimmune/immunology/diagnosis ; Adult ; *Autoantibodies/immunology/blood ; *Machine Learning ; Algorithms ; Aged ; Random Forest ; }, abstract = {BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients.<br><br>METHODS: A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies.<br><br>RESULTS: The results indicated that the Random Forest Classifier algorithm (AUC&#x2009;=&#x2009;0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms.<br><br>CONCLUSIONS: Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC&#x2009;=&#x2009;0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients.<br><br>TRIAL REGISTRATION: This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02).}, }
@article {pmid39748063, year = {2025}, author = {Wei, Q and Li, C and Wang, Y and Gao, X}, title = {Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {365}, pmid = {39748063}, issn = {2045-2322}, support = {62066028//National Natural Science Foundation of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Algorithms ; *Electroencephalography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established. To address this issue, we propose a classification framework named eTRCA + sbCNN that combines an ensemble task-related component analysis (eTRCA) algorithm and a sub-band convolutional neural network (sbCNN) for recognizing the frequency of SSVEP signals. The two models are first trained separately, then their classification score vectors are added together, and finally the frequency corresponding to the maximal summed score is decided as the frequency of SSVEP signals. The proposed framework can effectively exploit the complementarity between the two kinds of feature signals and significantly improve the classification performance of SSVEP-based BCIs. The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. Experimental results indicate that the proposed method significantly outperform the compared algorithms, and thus helps to promote the practical application of SSVEP- BCI systems.}, }
@article {pmid39747930, year = {2025}, author = {Anderson, L and De Ridder, D and Glue, P and Mani, R and van Sleeuwen, C and Smith, M and Adhia, DB}, title = {A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {209}, pmid = {39747930}, issn = {2045-2322}, mesh = {Humans ; *Fibromyalgia/therapy/physiopathology ; *Neurofeedback/methods ; Female ; Middle Aged ; *Electroencephalography ; Adult ; *Feasibility Studies ; Male ; Treatment Outcome ; Gyrus Cinguli/physiopathology/diagnostic imaging ; Chronic Pain/therapy/physiopathology ; }, abstract = {Fibromyalgia is a chronic pain condition contributing to significant disability worldwide. Neuroimaging studies identify abnormal effective connectivity between cortical areas responsible for descending pain modulation (pregenual anterior cingulate cortex, pgACC) and sensory components of pain experience (primary somatosensory cortex, S1). Neurofeedback, a brain-computer interface technique, can normalise dysfunctional brain activity, thereby improving pain and function. This study evaluates the safety, feasibility, and acceptability of a novel electroencephalography-based neurofeedback training, targeting effective alpha-band connectivity from the pgACC to S1 and exploring its effect on pain and function. Participants with fibromyalgia (N = 30; 15 = active, 15 = placebo) received 12 sessions of neurofeedback. Feasibility and outcome measures of pain (Brief Pain Inventory) and function (Revised Fibromyalgia Impact Questionnaire) were collected at baseline and immediately, ten-days, and one-month post-intervention. Descriptive statistics demonstrate effective connectivity neurofeedback training is feasible (recruitment rate: 6 participants per-month, mean adherence: 80.5%, dropout rate: 20%), safe (no adverse events) and highly acceptable (average 8.0/10) treatment approach for fibromyalgia. Active and placebo groups were comparable in their decrease in pain and functional impact. Future fully powered clinical trial is warranted to test the efficacy of the effective connectivity neurofeedback training in people with fibromyalgia with versus without chronic fatigue.}, }
@article {pmid39745941, year = {2025}, author = {Stavisky, SD}, title = {Restoring Speech Using Brain-Computer Interfaces.}, journal = {Annual review of biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-bioeng-110122-012818}, pmid = {39745941}, issn = {1545-4274}, abstract = {People who have lost the ability to speak due to neurological injuries would greatly benefit from assistive technology that provides a fast, intuitive, and naturalistic means of communication. This need can be met with brain-computer interfaces (BCIs): medical devices that bypass injured parts of the nervous system and directly transform neural activity into outputs such as text or sound. BCIs for restoring movement and typing have progressed rapidly in recent clinical trials; speech BCIs are the next frontier. This review covers the clinical need for speech BCIs, surveys foundational studies that point to where and how speech can be decoded in the brain, describes recent progress in both discrete and continuous speech decoding and closed-loop speech BCIs, provides metrics for assessing these systems' performance, and highlights key remaining challenges on the road toward clinically useful speech neuroprostheses.}, }
@article {pmid39745924, year = {2025}, author = {Trevelyan, AJ and Marks, VS and Graham, RT and Denison, T and Jackson, A and Smith, EH}, title = {On brain stimulation in epilepsy.}, journal = {Brain : a journal of neurology}, volume = {148}, number = {3}, pages = {746-752}, pmid = {39745924}, issn = {1460-2156}, mesh = {Humans ; Animals ; *Deep Brain Stimulation/methods ; *Epilepsy/therapy/physiopathology ; *Optogenetics/methods ; Vagus Nerve Stimulation/methods ; Brain/physiopathology ; Drug Resistant Epilepsy/therapy/physiopathology ; }, abstract = {Brain stimulation has, for many decades, been considered as a potential solution for the unmet needs of the many people living with drug-resistant epilepsy. Clinically, there are several different approaches in use, including vagus nerve stimulation, deep brain stimulation of the thalamus, and responsive neurostimulation. Across populations of patients, all deliver reductions in seizure load and sudden unexpected death in epilepsy risk, yet do so variably, and the improvements seem incremental rather than transformative. In contrast, within the field of experimental neuroscience, the transformational impact of optogenetic stimulation is evident; by providing a means to control subsets of neurons in isolation, it has revolutionized our ability to dissect out the functional relations within neuronal microcircuits. It is worth asking, therefore, how preclinical optogenetics research could advance clinical practice in epilepsy? Here, we review the state of the clinical field, and the recent progress in preclinical animal research. We report various breakthrough results, including the development of new models of seizure initiation, its use for seizure prediction, and for fast, closed-loop control of pathological brain rhythms, and what these experiments tell us about epileptic pathophysiology. Finally, we consider how these preclinical research advances may be translated into clinical practice.}, }
@article {pmid39745545, year = {2025}, author = {Romano, V and Manto, M}, title = {How and where Effectively Apply Cerebellum Stimulation: The frequency-dependent Modulation of Cerebellar Output by Transcranial Alternating Current Stimulation.}, journal = {Cerebellum (London, England)}, volume = {24}, number = {1}, pages = {22}, pmid = {39745545}, issn = {1473-4230}, mesh = {Animals ; Humans ; Rats ; *Cerebellum/physiology ; *Transcranial Direct Current Stimulation/methods ; }, abstract = {As brain-machine interfaces (BMI) are growingly used in clinical settings, understanding how to apply brain stimulation is increasingly important. Despite the emergence of optogenetic techniques, ethical and medical concerns suggest that interventions that are safe and non-invasive, such as Transcranial Alternating Current Stimulation (tACS), are more likely to be employed in human in the near future. Consequently, the question of how and where to apply current stimulation is becoming increasingly important for the efficient neuromodulation of both neurological and psychiatric disorders. In this edition of The Cerebellum, Mourra et al. demonstrate how ctACS influences cerebellar output at both single-cell and population levels by stimulating Crus I in rats. As the neuron generating this output serves as a crucial convergence and divergence center in the nervous system, it can be leveraged as a strategic hub to target multiple brain structures and influence various behaviors. Accordingly, the discovery that neurons in this relatively deep brain region can be indirectly entrained through Purkinje neuron activation and optimal frequency around 80 Hz could be highly relevant for future medical interventions. In light of these findings, high-γ-tACS might be more effective in humans compared to the more commonly used low-γ (50 Hz) or θ-tACS (5 Hz). This could enhance the chance of cerebellar tACS being utilized in clinical settings and BMI.}, }
@article {pmid39744695, year = {2025}, author = {Li, C and Miao, C and Ge, Y and Wu, J and Gao, P and Yin, S and Zhang, P and Yang, H and Tian, B and Chen, W and Chen, XQ}, title = {A molecularly distinct cell type in the midbrain regulates intermale aggression behaviors in mice.}, journal = {Theranostics}, volume = {15}, number = {2}, pages = {707-725}, pmid = {39744695}, issn = {1838-7640}, mesh = {Animals ; *Aggression/physiology ; Mice ; *Periaqueductal Gray/metabolism/physiology ; Male ; *Neurons/metabolism/physiology ; Mice, Inbred C57BL ; Tachykinins/metabolism/genetics ; Behavior, Animal/physiology ; Mesencephalon/metabolism/physiology ; Serotonin/metabolism ; }, abstract = {Rationale: The periaqueductal gray (PAG) is a central hub for the regulation of aggression, whereas the circuitry and molecular mechanisms underlying this regulation remain uncharacterized. In this study, we investigate the role of a distinct cell type, Tachykinin 2-expressing (Tac2[+]) neurons, located in the dorsomedial PAG (dmPAG) and their modulation of aggressive behavior in mice. Methods: We combined activity mapping, in vivo Ca[2+] recording, chemogenetic and pharmacological manipulation, and a viral-based translating ribosome affinity purification (TRAP) profiling using a mouse resident-intruder model. Results: We revealed that dmPAG[Tac2] neurons are selectively activated by fighting behaviors. Chemogenetic activation of these neurons evoked fighting behaviors, while inhibition or genetic ablation of dmPAG[Tac2] neurons attenuated fighting behaviors. TRAP profiling of dmPAG[Tac2] neurons revealed an enrichment of serotonin-associated transcripts in response to fighting behaviors. Finally, we validated these effects by selectively administering pharmacological agents to the dmPAG, reversing the behavioral outcomes induced by chemogenetic manipulation. Conclusions: We identify dmPAG[Tac2] neurons as critical modulators of aggressive behavior in mouse and thus suggest a distinct molecular target for the treatment of exacerbated aggressive behaviors in populations that exhibit high-level of violence.}, }
@article {pmid39743292, year = {2024}, author = {Huang, T and Guo, X and Huang, X and Yi, C and Cui, Y and Dong, Y}, title = {Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.}, journal = {Journal of Zhejiang University. Science. B}, volume = {25}, number = {12}, pages = {1055-1065}, pmid = {39743292}, issn = {1862-1783}, support = {2022ZD0211700//the STI2030-Major Projects/ ; 32371057, 31922031, 32071017, 81971309, 32170980, 82201707 and 82200562//the National Natural Science Foundation of China/ ; LDQ24C090001//the Zhejiang Provincial Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; JCTD-2022-11//the CAS Youth Interdisciplinary Team/ ; BX20230319//the China Postdoctoral Science Foundation/ ; 2022B1515020012 and 2021A1515110121//the Guangdong Basic and Applied Basic Research Foundation/ ; 2023B1212060018//the Science and Technology Planning Project of Guangdong Province/ ; JCYJ20210324123212035, RCYX20200714114644167, ZDSYS20220606100801003, JCYJ20210324122809025 and JCYJ20230807110315031//the Shenzhen Fundamental Research Program/ ; }, mesh = {*Habenula/physiology ; Animals ; *Stress, Psychological/physiopathology ; Humans ; Neurons/physiology ; Neuronal Plasticity/physiology ; Depression/physiopathology ; Neural Pathways/physiology ; }, abstract = {Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.}, }
@article {pmid39743145, year = {2025}, author = {Hao, X and Ma, M and Meng, F and Liang, H and Liang, C and Liu, X and Zhang, B and Ju, Y and Liu, S and Ming, D}, title = {Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis.}, journal = {Journal of affective disorders}, volume = {373}, number = {}, pages = {227-236}, doi = {10.1016/j.jad.2024.12.095}, pmid = {39743145}, issn = {1573-2517}, mesh = {Humans ; *Anxiety Disorders/physiopathology ; Female ; Male ; *Electroencephalography ; Adult ; *Attention/physiology ; *Default Mode Network/physiopathology/diagnostic imaging ; Brain/physiopathology/diagnostic imaging ; Young Adult ; Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; Case-Control Studies ; Rest/physiology ; }, abstract = {Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.}, }
@article {pmid39742538, year = {2025}, author = {Cao, L and Zhao, W and Sun, B}, title = {Emotion recognition using multi-scale EEG features through graph convolutional attention network.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {107060}, doi = {10.1016/j.neunet.2024.107060}, pmid = {39742538}, issn = {1879-2782}, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet). DSSTNet includes three main parts, the first is spatial features extractor, which converts EEG signal into graph structure data, and uses graph convolutional network (GCN) to dynamically optimize the adjacency matrix during the training process to obtain the spatial features between the channels. Next, band attention module is composed of semi-global pooling, localized cross-band interaction and adaptive weighting, which further extracts frequency information. Finally, through the temporal features extractor, the deep temporal information is extracted by stacking several one-dimensional convolutional layers. In addition, in order to improve the performance of emotion recognition and filter valid channels, we add a ℓ2,1-norm regularization term to the loss function to make the adjacency matrix constraint sparse. This makes it easier to preserve emotionally relevant channels and eliminate noise in irrelevant channel. Combined with the channel selection method of graph theory, a small number of optimal channels are selected. We used a self-constructed dataset TJU-EmoEEG and a publicly available SEED dataset to evaluate DSSTNet. The experimental results demonstrate that DSSTNet outperforms current state-of-the-art (SOTA) methods in emotional recognition tasks.}, }
@article {pmid39741784, year = {2024}, author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y}, title = {EEG channel and feature investigation in binary and multiple motor imagery task predictions.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1525139}, pmid = {39741784}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.<br><br>METHODS: Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.<br><br>DISCUSSION: Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.}, }
@article {pmid39741250, year = {2024}, author = {Liang, S and Li, L and Zu, W and Feng, W and Hang, W}, title = {Adaptive deep feature representation learning for cross-subject EEG decoding.}, journal = {BMC bioinformatics}, volume = {25}, number = {1}, pages = {393}, pmid = {39741250}, issn = {1471-2105}, support = {61902197//National Natural Science Foundation of China/ ; KYCX23_1073//Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province/ ; 23KJB520012//Natural Science Research of Jiangsu Higher Education Institutions of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Deep Learning ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; }, abstract = {BACKGROUND: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification.<br><br>METHODS: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects.<br><br>RESULTS: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets.<br><br>CONCLUSIONS: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications.}, }
@article {pmid39738346, year = {2024}, author = {Le, TT and Luong, DAQ and Joo, H and Kim, D and Woo, J}, title = {Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31918}, pmid = {39738346}, issn = {2045-2322}, support = {NRF-2020R1A2C2003319//National Research Foundation of Korea/ ; }, mesh = {Humans ; *Electroencephalography ; *Semantics ; Male ; Female ; Adult ; *Brain Mapping ; Brain/physiology ; Young Adult ; Comprehension/physiology ; }, abstract = {Semantic processing is an essential mechanism in human language comprehension and has profound implications for speech brain-computer interface technologies. Despite recent advances in brain imaging techniques and data analysis algorithms, the mechanisms underlying human brain semantic representations remain a topic of debate, specifically whether this occurs through the activation of selectively separated cortical regions or via a network of distributed and overlapping regions. This study investigates spatiotemporal neural representation during the perception of semantic words related to faces, numbers, and animals using electroencephalography. Source-level analysis focuses on contrasting neural responses to different semantic categories. Critical intervals used in the source contrast analysis are defined using the peak duration of global field power. Effective connectivity, determined through a causality analysis of brain regions activated for semantic processing, is explored. The findings reveal the necessity of a distributed network of regions for processing specific semantic categories and provide evidence suggesting the existence of a neural substrate for semantic representations.}, }
@article {pmid39738221, year = {2024}, author = {Wolde, HF and Clements, ACA and Gilmour, B and Alene, KA}, title = {Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31561}, pmid = {39738221}, issn = {2045-2322}, support = {APP1196549//Australian National Health and Medical Research Council/ ; }, mesh = {Humans ; Ethiopia/epidemiology ; *BCG Vaccine/administration &amp; dosage ; Prevalence ; *Vaccination Coverage/statistics &amp; numerical data ; Female ; *Tuberculosis/epidemiology/prevention &amp; control ; Male ; *Bayes Theorem ; Infant ; Child, Preschool ; Adult ; Adolescent ; Vaccination/statistics &amp; numerical data ; Young Adult ; Child ; Spatial Analysis ; }, abstract = {While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = -0.04, 95% BCI -0.05, -0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.}, }
@article {pmid39738213, year = {2024}, author = {Liu, Y and Huang, S and Xu, W and Wang, Z and Ming, D}, title = {An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.}, journal = {NPJ science of learning}, volume = {9}, number = {1}, pages = {80}, pmid = {39738213}, issn = {2056-7936}, support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin City (Natural Science Foundation of Tianjin)/ ; MSV202418//State Key Laboratory of Mechanical System and Vibration/ ; }, abstract = {Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.}, }
@article {pmid39735964, year = {2024}, author = {Luo, TJ and Li, J and Li, R and Zhang, X and Wu, SR and Peng, H}, title = {Motion Cognitive Decoding of Cross-Subject Motor Imagery Guided on Different Visual Stimulus Materials.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {12}, pages = {218}, doi = {10.31083/j.jin2312218}, pmid = {39735964}, issn = {0219-6352}, support = {24JCXK01YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 24JCXK02YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 62106049//National Natural Science Foundation of China/ ; 61662025//National Natural Science Foundation of China/ ; 61871289//National Natural Science Foundation of China/ ; 62007016//National Natural Science Foundation of China/ ; 2022J01655//Natural Science Foundation of Fujian Province of China/ ; }, mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; Male ; *Brain-Computer Interfaces ; Female ; Motor Activity/physiology ; Psychomotor Performance/physiology ; Motion Perception/physiology ; }, abstract = {BACKGROUND: Motor imagery (MI) plays an important role in brain-computer interfaces, especially in evoking event-related desynchronization and synchronization (ERD/S) rhythms in electroencephalogram (EEG) signals. However, the procedure for performing a MI task for a single subject is subjective, making it difficult to determine the actual situation of an individual's MI task and resulting in significant individual EEG response variations during motion cognitive decoding.<br><br>METHODS: To explore this issue, we designed three visual stimuli (arrow, human, and robot), each of which was used to present three MI tasks (left arm, right arm, and feet), and evaluated differences in brain response in terms of ERD/S rhythms. To compare subject-specific variations of different visual stimuli, a novel cross-subject MI-EEG classification method was proposed for the three visual stimuli. The proposed method employed a covariance matrix centroid alignment for preprocessing of EEG samples, followed by a model agnostic meta-learning method for cross-subject MI-EEG classification.<br><br>RESULTS AND CONCLUSION: The experimental results showed that robot stimulus materials were better than arrow or human stimulus materials, with an optimal cross-subject motion cognitive decoding accuracy of 79.04%. Moreover, the proposed method produced robust classification of cross-subject MI-EEG signal decoding, showing superior results to conventional methods on collected EEG signals.}, }
@article {pmid39733553, year = {2025}, author = {Jain, A and Kumar, L}, title = {ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task.}, journal = {Computers in biology and medicine}, volume = {186}, number = {}, pages = {109608}, doi = {10.1016/j.compbiomed.2024.109608}, pmid = {39733553}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Hand Strength/physiology ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; Hand/physiology ; Deep Learning ; }, abstract = {BACKGROUND: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop Brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature.<br><br>METHOD: In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding.<br><br>RESULTS: The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100ms and 450ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively.<br><br>CONCLUSION: This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.}, }
@article {pmid39733023, year = {2024}, author = {Cheng, Z and Bu, X and Wang, Q and Yang, T and Tu, J}, title = {EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31319}, pmid = {39733023}, issn = {2045-2322}, support = {2020CB-34//the Science and Technology Plan Project of Jingzhou City/ ; 2022BCE009//Key Plan of Science and Technology Department of Hubei Province/ ; }, mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).}, }
@article {pmid39732819, year = {2024}, author = {Jin, L and Hu, J and Li, Y and Zhu, Y and He, X and Bai, R and Wang, L}, title = {Altered neurovascular coupling and structure-function coupling in Moyamoya disease affect postoperative collateral formation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31324}, pmid = {39732819}, issn = {2045-2322}, support = {81870910//National Natural Science Foundation of China/ ; 2022C03133//Key Research and Development Program of Zhejiang Province/ ; }, mesh = {Humans ; *Moyamoya Disease/physiopathology/surgery/diagnostic imaging ; Female ; Male ; Adult ; *Cerebrovascular Circulation/physiology ; *Neurovascular Coupling/physiology ; *Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Middle Aged ; Collateral Circulation/physiology ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging/pathology ; Young Adult ; }, abstract = {Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging. All patients had preoperative and postoperative digital subtraction angiography. Upon constructing the structural connectivity (SC) and functional connectivity (FC) networks, the SC-FC coupling was calculated. After obtaining the graph theoretical parameters, neurovascular coupling represented the spatial correlation between node degree centrality (DC) of functional networks and CBF. The CBF-DC coupling and SC-FC coupling were compared between MMD and HC groups. We further analyzed the correlation between coupling indexes and cognitive scores, as well as postoperative collateral formation. Compared with HC, CBF-DC coupling was decreased in MMD (p = 0.021), especially in the parietal lobe (p = 0.047). SC-FC coupling in MMD decreased in frontal, occipital, and subcortical regions. Cognitive scores were correlated with the CBF-DC coupling in frontal lobes (r = 0.394, p = 0.029) and SC-FC coupling (r = 0.397, p = 0.027). The CBF-DC coupling of patients with good postoperative collateral formation was higher (p = 0.041). Overall, neurovascular decoupling and structure-functional decoupling at the cortical level may be the underlying neuropathological mechanisms of MMD.}, }
@article {pmid39731856, year = {2025}, author = {Guo, X and Feng, Y and Ji, X and Jia, N and Maimaiti, A and Lai, J and Wang, Z and Yang, S and Hu, S}, title = {Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus.}, journal = {EBioMedicine}, volume = {111}, number = {}, pages = {105530}, pmid = {39731856}, issn = {2352-3964}, mesh = {Humans ; *Genome-Wide Association Study ; *Multifactorial Inheritance ; *Genetic Predisposition to Disease ; *Polymorphism, Single Nucleotide ; *Mental Disorders/genetics ; Phenotype ; Genetic Pleiotropy ; Female ; Risk Factors ; Metabolic Diseases/genetics/epidemiology ; }, abstract = {BACKGROUND: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.<br><br>METHODS: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.<br><br>FINDINGS: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.<br><br>INTERPRETATION: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).}, }
@article {pmid39730087, year = {2024}, author = {Kong, S and Zhang, J and Wang, L and Li, W and Guo, H and Weng, Q and He, Q and Lou, H and Ding, L and Yang, B}, title = {Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.}, journal = {Cancer letters}, volume = {611}, number = {}, pages = {217432}, doi = {10.1016/j.canlet.2024.217432}, pmid = {39730087}, issn = {1872-7980}, abstract = {Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8[+] T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8[+] T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8[+] T cells, such as immune checkpoint inhibitors (ICIs). Accumulated evidence suggested that restoring MHC I expression is an effective strategy for enhancing tumor immunotherapy. This review focuses on mechanisms underlying MHC I downregulation include gene deletion and mutation, transcriptional inhibition, reduced mRNA stability, increased protein degradation, and disruption of endocytic trafficking. We also provide a comprehensive review of small molecules that restore or upregulate MHC I expression, as well as clinical trials involving the combination of ICIs and these small molecule drugs.}, }
@article {pmid39729483, year = {2024}, author = {Gao, S and Sun, Y and Wu, F and Jiang, J and Peng, T and Zhang, R and Ling, C and Han, Y and Xu, Q and Zou, L and Liao, Y and Liang, C and Zhang, D and Qi, S and Tang, J and Xu, X}, title = {Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.}, journal = {Schizophrenia bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1093/schbul/sbae176}, pmid = {39729483}, issn = {1745-1701}, support = {82172061//National Natural Science Foundation of China/ ; BE2022677//Key Research and Development Plan in Jiangsu/ ; WSN-166//16th Batch of Six Talent Peak Projects in Jiangsu/ ; YKK23138//Nanjing Health Technology Development Project/ ; 23-25-289//Training and Management of Young Talents in Nanjing Brain Hospital/ ; 2016YFC1306900//National Key R&amp;D Program of China/ ; }, abstract = {BACKGROUND AND HYPOTHESIS: Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.<br><br>STUDY DESIGN: Sixty-four female drug-na&#xef;ve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.<br><br>STUDY RESULTS: At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.<br><br>CONCLUSIONS: SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.}, }
@article {pmid39727765, year = {2024}, author = {Yousefipour, B and Rajabpour, V and Abdoljabbari, H and Sheykhivand, S and Danishvar, S}, title = {An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, pmid = {39727765}, issn = {2313-7673}, abstract = {In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.}, }
@article {pmid39727293, year = {2024}, author = {Gefen, N and Mazer, B and Krasovsky, T and Weiss, PL}, title = {Novel rehabilitation technologies in pediatric rehabilitation: knowledge towards translation.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/17483107.2024.2445017}, pmid = {39727293}, issn = {1748-3115}, abstract = {Purpose: Knowledge translation (KT) refers to the process of applying the most promising research outcomes into practice to ensure that new discoveries and innovations improve healthcare accessibility, effectiveness, and accountability. The objective of this perspective paper is to discuss and illustrate via examples how the KT process can be implemented in an era of rapid advancement in rehabilitation technologies that have the potential to significantly impact pediatric healthcare. Methods: Using Graham et al.'s (2006) Knowledge-to-Action cycle, which includes the knowledge creation funnel and the action cycle, we illustrate its application in implementing novel technologies into clinical practice and informing healthcare policy changes. We explore three successful applications of technology research: powered mobility, head support systems, and telerehabilitation. Additionally, we examine less clinically mature technologies such as brain-computer interfaces and robotic assistive devices, which are hindered by cost, robustness, and ease-of-use issues. Conclusions: The paper concludes by discussing how technology acceptance and usage in clinical settings are influenced by various barriers and facilitators at different stakeholder levels, including clients, families, clinicians, management, researchers, developers, and society. Recommendations include focusing on early and ongoing design partnerships, transitioning from research to real-life implementation, and identifying optimal timing for clinical adoption of new technologies.}, }
@article {pmid39726882, year = {2024}, author = {Afrah, R and Amini, Z and Kafieh, R}, title = {An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems.}, journal = {Journal of biomedical physics &amp; engineering}, volume = {14}, number = {6}, pages = {579-592}, pmid = {39726882}, issn = {2251-7200}, abstract = {BACKGROUND: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.<br><br>OBJECTIVE: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).<br><br>MATERIAL AND METHODS: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data. However, CNN and LSTM networks are combined to modify the classification system by extracting both spatial and temporal features. Then, the CNN-LSTM network was trained in an unsupervised learning method based on an autoencoder to improve Signal-to-noise Ratio (SNR) by extracting main components from latent space. To deal with imbalanced data, an Adaptive Synthetic Sampling Approach (ADASYN) is used and augmented without any duplication.<br><br>RESULTS: The trained model, tested on the BCI competition III dataset, including two normal subjects, with an accuracy of 95% and 94% for subjects A and B in P300 detection, respectively.<br><br>CONCLUSION: CNN-LSTM, was embedded into an autoencoder and introduced to simultaneously extract spatial and temporal features and manage the computational complexity of the method. Further, ADASYN as an augmentation method was proposed to deal with the imbalanced nature of data, which not only maintained feature space as before but also preserved anatomical features of P300. High-quality results highlight the suitable efficiency of the proposed method.}, }
@article {pmid39726001, year = {2024}, author = {Sun, Q and Merino, EC and Yang, L and Van Hulle, MM}, title = {Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {228}, pmid = {39726001}, issn = {1743-0003}, support = {202206050022//China Scholarship Council/ ; 101118964//Horizon Europe's Marie Sklodowska-Curie Action/ ; 857375//Horizon 2020 research and innovation programme/ ; C24/18/098//special research fund of the KU Leuven/ ; G0A4118N, G0A4321N, G0C1522N//Belgian Fund for Scientific Research - Flanders/ ; AKUL 043//Hercules Foundation/ ; }, mesh = {Humans ; *Fingers/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; *Movement/physiology ; Young Adult ; Brain-Computer Interfaces ; }, abstract = {BACKGROUND: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension.<br><br>METHODS: Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3-3&#xa0;Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8-13&#xa0;Hz) and beta (13-30&#xa0;Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3-70&#xa0;Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30-70&#xa0;Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest.<br><br>RESULTS: A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers.<br><br>CONCLUSION: Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses.}, }
@article {pmid39725763, year = {2024}, author = {Fu, R and Niu, S and Feng, X and Shi, Y and Jia, C and Zhao, J and Wen, G}, title = {Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {39725763}, issn = {1741-0444}, support = {62073282//the National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; F2020203070//Natural Science Foundation of Hebei Province/ ; 206Z0301G//the Central Guidance on Local Science and Technology Development Fund of Hebei Province/ ; }, abstract = {This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.}, }
@article {pmid39723643, year = {2025}, author = {Tao, T and Liu, S and He, M and Zhao, M and Chen, C and Peng, J and Wang, Y and Cai, J and Xiong, J and Lai, C and Gu, W and Ying, M and Mao, J and Li, L and Jia, X and Wu, X and Peng, W and Zhang, X and Li, Y and Li, T and Wang, J and Shu, Q}, title = {Synchronous bilateral Wilms tumors are prone to develop independently and respond differently to preoperative chemotherapy.}, journal = {International journal of cancer}, volume = {156}, number = {9}, pages = {1746-1755}, doi = {10.1002/ijc.35297}, pmid = {39723643}, issn = {1097-0215}, support = {32270853//National Natural Science Foundation of China/ ; U20A20137//National Natural Science Foundation of China/ ; LHDMZ23H160005//Joint Funds of the Zhejiang Provincial Natural Science Foundation of China/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; }, mesh = {Humans ; *Wilms Tumor/drug therapy/genetics/pathology ; *Kidney Neoplasms/drug therapy/pathology/genetics ; Male ; Female ; Infant ; *Mutation ; Child, Preschool ; DNA Copy Number Variations ; Loss of Heterozygosity ; beta Catenin/genetics/metabolism ; WT1 Proteins/genetics ; Exome Sequencing ; Neoplasms, Multiple Primary/drug therapy/genetics/pathology ; Transcriptome ; Chromosomes, Human, Pair 11/genetics ; }, abstract = {Wilms tumor (WT) is the most common kidney cancer in infants and young children. The determination of the clonality of bilateral WTs is critical to the treatment, because lineage-independent and metastatic tumors may require different treatment strategies. Here we found synchronous bilateral WT (n = 24 tumors from 12 patients) responded differently to preoperative chemotherapy. Transcriptome, whole-exome and whole-genome analysis (n = 12 tumors from 6 patients) demonstrated that each side of bilateral WT was clonally independent in terms of somatic driver mutations, copy number variations and transcriptomic profile. Molecular timing analysis revealed distinct timing and patterns of chromosomal evolution and mutational processes between the two sides of WT. Mutations in WT1, CTNNB1 and copy-neutral loss of heterozygosity of 11p15.5 provide possible genetic predisposition for the early initiation of bilateral WT. Our results provide comprehensive evidence and new insights regarding the separate initiation and early embryonic development of bilateral WT, which may benefit clinical practices in treating metastatic or refractory bilateral WT.}, }
@article {pmid39720868, year = {2024}, author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL}, title = {A Roadmap for Implanting Electrode Arrays to Evoke Tactile Sensations Through Intracortical Stimulation.}, journal = {Human brain mapping}, volume = {45}, number = {18}, pages = {e70118}, pmid = {39720868}, issn = {1097-0193}, support = {N66001-10-C-4056//Defense Advanced Research Projects Agency/ ; UH3 NS107714/NH/NIH HHS/United States ; }, mesh = {Humans ; Male ; Adult ; *Spinal Cord Injuries/physiopathology ; *Electrodes, Implanted ; Female ; *Somatosensory Cortex/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Middle Aged ; Touch Perception/physiology ; Electric Stimulation/methods ; Brain Mapping/methods ; Hand/physiology ; Magnetic Resonance Imaging ; }, abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.}, }
@article {pmid39720668, year = {2024}, author = {Li, X and Chu, Y and Wu, X}, title = {3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1485640}, pmid = {39720668}, issn = {1662-5218}, abstract = {Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution. Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these challenges, this paper proposes a 3D Convolutional Neural Network (P-3DCNN) decoding method that jointly learns spatial-frequency feature maps from the frequency and spatial domains of the EEG signals. First, the Welch method is used to calculate the frequency band power spectrum of the EEG, and a 2D matrix representing the spatial topology distribution of the electrodes is constructed. These spatial-frequency representations are then generated through cubic interpolation of the temporal EEG data. Next, the paper designs a 3DCNN network with 1D and 2D convolutional layers in series to optimize the convolutional kernel parameters and effectively learn the spatial-frequency features of the EEG. Batch normalization and dropout are also applied to improve the training speed and classification performance of the network. Finally, through experiments, the proposed method is compared to various classic machine learning and deep learning techniques. The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. This demonstrates the effectiveness of our approach in decoding motor imagery EEG and offers valuable insights for the development of BCI.}, }
@article {pmid39720230, year = {2024}, author = {Hu, F and He, K and Qian, M and Liu, X and Qiao, Z and Zhang, L and Xiong, J}, title = {STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1519970}, pmid = {39720230}, issn = {1662-4548}, abstract = {INTRODUCTION: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.<br><br>METHODS: We propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification.<br><br>RESULTS: Extensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures.<br><br>DISCUSSION: In summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition.}, }
@article {pmid39719191, year = {2025}, author = {Rudroff, T}, title = {Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.}, journal = {Brain research}, volume = {1850}, number = {}, pages = {149423}, doi = {10.1016/j.brainres.2024.149423}, pmid = {39719191}, issn = {1872-6240}, mesh = {*Brain-Computer Interfaces/ethics ; Humans ; *Artificial Intelligence ; Electroencephalography/methods ; Brain/physiology ; }, abstract = {OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.<br><br>METHODS: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019-2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.<br><br>RESULTS: Recent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5&#xa0;mm, with stable recordings over 15&#xa0;months; 2) Deep learning decoders show 40&#xa0;% improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90&#xa0;% success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55&#xa0;% while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.<br><br>CONCLUSIONS: BCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology's growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.}, }
@article {pmid39719121, year = {2025}, author = {Li, K and Chen, P and Chen, Q and Li, X}, title = {A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ada30b}, pmid = {39719121}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Algorithms ; Linear Models ; Signal Processing, Computer-Assisted ; }, abstract = {Objective. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a transformer with modified locally linear embedding and sliding window convolution for EEG decoding.Approach. This network separately extracts channel and temporal features from EEG signals, subsequently fusing these features using a cross-attention mechanism. Simultaneously, manifold learning is employed to lower the computational burden of the model by mapping the high-dimensional EEG data to a low-dimensional space by its dimension reduction function.Main results. The proposed model achieves accuracy rates of 84.44%, 94.96%, and 82.79% on the BCI Competition IV dataset 2a, high gamma dataset, and a self-constructed motor imagery (MI) dataset from the left and right hand fist-clenching tests respectively. The results indicate our model outperforms the baseline models by EEG-channel transformer with dimension-reduced EEG data and window attention with sliding window convolution. Additionally, to enhance the interpretability of the model, features preceding the temporal feature extraction network were visualized. This visualization promotes the understanding of how the model prefers task-related channels.Significance. The transformer-based method makes the MI-EEG decoding more practical for further clinical applications.}, }
@article {pmid39718409, year = {2024}, author = {Ma, X and Chen, W and Pei, Z and Zhang, J}, title = {An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.}, journal = {The Review of scientific instruments}, volume = {95}, number = {12}, pages = {}, doi = {10.1063/5.0243337}, pmid = {39718409}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; Humans ; *Lower Extremity/physiology ; *Exoskeleton Device ; *Electroencephalography/instrumentation ; Attention/physiology ; Neural Networks, Computer ; Adult ; Male ; Imagination/physiology ; }, abstract = {Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons. To address this challenge, we propose an attention-based motor imagery BCI system for lower limb exoskeletons. The decoding module of the proposed BCI system combines the convolutional neural network (CNN) with a lightweight attention module. The CNN aims to extract meaningful features from EEG signals, while the lightweight attention module aims to capture global dependencies among these features. The experiments are divided into offline and online experiments. The offline experiment is conducted to evaluate the effectiveness of different decoding methods, while the online experiment is conducted on a customized lower limb exoskeleton to evaluate the proposed BCI system. Eight subjects are recruited for the experiments. The experimental results demonstrate the great classification performance of the decoding method and validate the feasibility of the proposed BCI system. Our approach establishes a promising BCI system for the lower limb exoskeleton and is expected to achieve a more effective and user-friendly rehabilitation process.}, }
@article {pmid39715900, year = {2024}, author = {Robinson, JT and Norman, SL and Angle, MR and Constandinou, TG and Denison, T and Donoghue, JP and Field, RM and Forsland, A and Kouider, S and Millán, JDR and Michaels, JA and Orsborn, AL and Pandarinath, C and Pruszynski, JA and Rozell, CJ and Shah, NP and Shanechi, MM and Shoaran, M and Sheth, SA and Stavisky, SD and Trautmann, E and Vachicouras, N and Xie, C}, title = {An application-based taxonomy for brain-computer interfaces.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {39715900}, issn = {2157-846X}, }
@article {pmid39712143, year = {2024}, author = {Bai, G and Jin, J and Xu, R and Wang, X and Cichocki, A}, title = {A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3549-3563}, pmid = {39712143}, issn = {1871-4080}, abstract = {In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application effect of TL in MI-BCI. Therefore, this paper combines data alignment, source domain selection, and feature alignment into the MI-TL. We propose a novel dual-step transfer framework based on source domain selection and feature alignment. First, the source and target domains are aligned using a pre-calibration strategy (PS), and then a sequential reverse selection method is proposed to match the optimal source domain for each target domain with the designed dual model selection strategy. We use filter bank regularization common space pattern (FBRCSP) to obtain more features and introduce manifold embedded distribution alignment (MEDA) to correct the prediction results of the support vector machine (SVM). The experimental results on two competition public datasets (BCI competition IV Dataset 1 and Dataset 2a) and our dataset show that the average classification accuracy of the proposed framework is higher than the baseline method (no domain selection and no feature alignment), which reaches 84.12%, 79.91%, and 78.45%, respectively. And the computational cost is reduced by half compared with the baseline method.}, }
@article {pmid39712134, year = {2024}, author = {Zhang, J and Shen, C and Chen, W and Ma, X and Liang, Z and Zhang, Y}, title = {Decoding of movement-related cortical potentials at different speeds.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3859-3872}, pmid = {39712134}, issn = {1871-4080}, abstract = {The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.}, }
@article {pmid39712133, year = {2024}, author = {Wu, C and Wang, Y and Qiu, S and He, H}, title = {A bimodal deep learning network based on CNN for fine motor imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3791-3804}, pmid = {39712133}, issn = {1871-4080}, abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.}, }
@article {pmid39712131, year = {2024}, author = {Meng, M and Xu, B and Ma, Y and Gao, Y and Luo, Z}, title = {STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3663-3678}, pmid = {39712131}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.}, }
@article {pmid39712122, year = {2024}, author = {Li, M and Li, J and Zheng, X and Ge, J and Xu, G}, title = {MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3463-3476}, pmid = {39712122}, issn = {1871-4080}, abstract = {EEG decoding plays a crucial role in the development of motor imagery brain-computer interface. Deep learning has great potential to automatically extract EEG features for end-to-end decoding. Currently, the deep learning is faced with the chanllenge of decoding from a large amount of time-variant EEG to retain a stable peroformance with different sessions. This study proposes a multi-scale residual network with hybrid attention (MSHANet) to decode four motor imagery classes. The MSHANet combines a multi-head attention and squeeze-and-excitation attention to hybridly focus on important information of the EEG features; and applies a multi-scale residual block to extracts rich EEG features, sharing part of the block parameters to extract common features. Compared with seven state-of-the-art methods, the MSHANet exhits the best accuracy on BCI Competition IV 2a with an accuracy of 83.18% for session- specific task and 80.09% for cross-session task. Thus, the proposed MSHANet decodes the time-varying EEG robustly and can save the time cost of MI-BCI, which is beneficial for long-term use.}, }
@article {pmid39712121, year = {2024}, author = {Rahman, N and Khan, DM and Masroor, K and Arshad, M and Rafiq, A and Fahim, SM}, title = {Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3565-3583}, pmid = {39712121}, issn = {1871-4080}, abstract = {Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.}, }
@article {pmid39712116, year = {2024}, author = {Tang, C and Gao, T and Wang, G and Chen, B}, title = {Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3535-3548}, pmid = {39712116}, issn = {1871-4080}, abstract = {Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy. To improve the accuracy of MEG decoding, this paper proposes a new coherence-based channel selection method that effectively identifies task-relevant channels, reducing the presence of noisy and redundant information. Riemannian geometry is then used to extract effective features from selected channels of MEG data. Finally, MEG decoding is achieved by training a support vector machine classifier with the Radial Basis Function kernel. Experiments were conducted on two public MEG datasets to validate the effectiveness of the proposed method. The results from Dataset 1 show that Riemannian geometry achieves higher classification accuracy (compared to common spatial patterns and power spectral density) in the single-subject visual decoding task. Moreover, coherence-based channel selection significantly (P = 0.0002) outperforms the use of all channels. Moving on to Dataset 2, the results reveal that coherence-based channel selection is also significantly (P <0.0001) superior to all channels and channels around C3 and C4 in cross-session mental imagery decoding tasks. Additionally, the proposed method outperforms state-of-the-art methods in motor imagery tasks.}, }
@article {pmid39712104, year = {2024}, author = {Yu, H and Hu, Z and Zhao, Q and Liu, J}, title = {Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3507-3520}, pmid = {39712104}, issn = {1871-4080}, abstract = {Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.}, }
@article {pmid39712096, year = {2024}, author = {Zhang, Z and Chen, Y and Zhao, X and Fan, W and Peng, D and Li, T and Zhao, L and Fu, Y}, title = {A review of ethical considerations for the medical applications of brain-computer interfaces.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3603-3614}, pmid = {39712096}, issn = {1871-4080}, abstract = {The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics. Subsequently, it introduces the ethical standards under consideration within the medical objective framework for BCI medical applications. Finally, the paper discusses and forecasts the ethical standards for BCI medical applications. The paper emphasizes the necessity to differentiate between the ethical issues of implantable and non-implantable BCIs, to approach the research on BCI-based "controlling the brain" with caution, and to establish standardized operational procedures and efficacy evaluation methods for BCI medical applications. This paper aims to provide ideas for the establishment of ethical standards in BCI medical applications.}, }
@article {pmid39712090, year = {2024}, author = {Huang, Y and Huan, Y and Zou, Z and Wang, Y and Gao, X and Zheng, L}, title = {Data-driven natural computational psychophysiology in class.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3477-3489}, pmid = {39712090}, issn = {1871-4080}, abstract = {Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants' psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.}, }
@article {pmid39711742, year = {2024}, author = {Li, J and Nan, Z and Qi, G and Cai, J and Zhao, X and Li, X and Liu, S and Wang, Y and Wu, Y and Miao, X and Yu, G}, title = {Assessing severity of pediatric pneumonia using multimodal transformers with multi-task learning.}, journal = {Digital health}, volume = {10}, number = {}, pages = {20552076241305168}, pmid = {39711742}, issn = {2055-2076}, abstract = {OBJECTIVE: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the robust multimodal transformer (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.<br><br>METHOD: The RMT model leverages multimodal data, integrating X-ray images and clinical text data through a sophisticated AI-driven framework. It employs a Transformer-based architecture, enhanced by multi-task learning and mask attention mechanism. This approach aims to optimize the model's performance across different modalities, particularly under conditions of modality absence.<br><br>RESULTS: The RMT model demonstrates superior performance over traditional diagnostic methods and baseline models in accuracy, precision, sensitivity, and specificity. In tests involving various scenarios, including single-modal and multimodal tasks, the model shows remarkable robustness in handling incomplete data. Its effectiveness is further validated through extensive comparative analysis and ablation studies.<br><br>CONCLUSION: The RMT model represents a substantial advancement in pediatric pneumonia severity assessment. It successfully harnesses multimodal data and advanced AI techniques to improve assessment precision. While the RMT model sets a new precedent in AI applications in medical diagnostics, the development of a comprehensive pediatric pneumonia dataset marks a pivotal contribution, providing a robust foundation for future research.}, }
@article {pmid39705724, year = {2025}, author = {de Melo, GC and Castellano, G and Forner-Cordero, A}, title = {Identification and analysis of reference-independent movement event-related desynchronization.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {2}, pages = {}, doi = {10.1088/2057-1976/ada1dc}, pmid = {39705724}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Movement ; Male ; Female ; Adult ; Electrodes ; Signal Processing, Computer-Assisted ; Algorithms ; Cortical Synchronization/physiology ; Young Adult ; Brain/physiology ; Motor Cortex/physiology ; }, abstract = {Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.}, }
@article {pmid39704693, year = {2025}, author = {Svejgaard, B and Modrau, B and Hernández-Gloria, JJ and Wested, CL and Dosen, S and Stevenson, AJT and Mrachacz-Kersting, N}, title = {Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke.}, journal = {Journal of neurophysiology}, volume = {133}, number = {1}, pages = {333-341}, doi = {10.1152/jn.00452.2024}, pmid = {39704693}, issn = {1522-1598}, support = {2021-0008//Sundhedsinnovationspuljen, Region Nordjylland/ ; 229643//Melsen Fonden/ ; 21.584//Grosserer L. F. Foghts Fond (Grosserer LF Foghts Fund)/ ; 7683//Jascha Fonden (Jascha Foundation)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; Aged ; *Stroke/physiopathology ; *Wrist/physiology/physiopathology ; *Evoked Potentials, Motor/physiology ; Pyramidal Tracts/physiopathology/physiology ; Transcranial Magnetic Stimulation ; Motor Cortex/physiopathology/physiology ; Adult ; Electroencephalography ; Muscle, Skeletal/physiopathology/physiology ; }, abstract = {In a recently developed associative rehabilitative brain-computer interface (BCI) system, electroencephalography (EEG) is used to identify the most active phase of the motor cortex during attempted movement and deliver precisely timed peripheral stimulation during training. This approach has been demonstrated to facilitate corticospinal excitability and functional recovery in patients with lower limb weakness following stroke. The current study expands those findings by investigating changes in corticospinal excitability following the associative BCI intervention in patients with post stroke with upper limb weakness. In a randomized controlled trial, 24 patients with subacute stroke, subdivided into an intervention group and a "sham" control group, performed 30 wrist extensions. The intervention comprised 30 pairings of single peripheral nerve stimulation at the motor threshold, timed so that the generated afferent volley arrived at the motor cortex during the peak negativity of the movement-related cortical potential (MRCP), which was identified with EEG. The sham group underwent the same intervention, though the intensity of the nerve stimulation was below the perception threshold. Immediately after training, patients in the associative group exhibited significantly larger amplitudes of muscular-evoked potentials, compared with pretraining measurements in response to transcranial magnetic stimulation. These changes persisted for at least 30 min and were not observed in the sham group. We demonstrate that motor-evoked potential amplitudes increased significantly following paired associative BCI training targeting upper limb muscles in patients with subacute stroke, which is in line with results from lower limb studies.NEW &amp; NOTEWORTHY We have demonstrated that a single training session with an associative brain-computer interface increased corticospinal excitability in patients suffering from upper limb weakness following stroke. This is the first time such an effect is described in the upper limb, which paves the way for effect augmentation of existing upper limb rehabilitation protocols.}, }
@article {pmid39703669, year = {2024}, author = {Cao, Y and Gao, S and Yu, H and Zhao, Z and Zang, D and Wang, C}, title = {A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.}, journal = {Frontiers in physiology}, volume = {15}, number = {}, pages = {1487809}, pmid = {39703669}, issn = {1664-042X}, abstract = {OBJECTIVE: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.<br><br>METHODS: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT.<br><br>RESULTS: The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions.<br><br>CONCLUSION: The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.}, }
@article {pmid39703101, year = {2024}, author = {Zhou, H and Wang, M and Xu, T and Zhang, X and Zhao, X and Tang, L and Zhao, P and Wang, D and Lai, J and Wang, F and Zhang, S and Hu, S}, title = {Cognitive Remediation in Patients With Bipolar Disorder: A Randomized Trial by Sequential tDCS and Navigated rTMS Targeting the Primary Visual Cortex.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {12}, pages = {e70179}, pmid = {39703101}, issn = {1755-5949}, support = {82201675//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021C03107//Key Research and Development Program of Zhejiang Province/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; }, mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Bipolar Disorder/therapy/psychology ; *Transcranial Magnetic Stimulation/methods ; Adult ; Middle Aged ; *Cognitive Remediation/methods ; *Visual Cortex ; Young Adult ; Treatment Outcome ; }, abstract = {BACKGROUND: Non-invasive brain stimulation (NIBS), such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), has emerged as a promising alternative in the precise treatment of clinical symptoms, such as the cognitive impairment of bipolar disorder (BD). Optimizing the neurocognitive effects by combining tDCS and rTMS to strengthen the clinical outcome is a challenging research issue.<br><br>OBJECTIVE: In this randomized, controlled trial, we first combined tDCS and neuronavigated rTMS targeting the V1 region to explore the efficacy on neurocognitive function in BD patients with depressive episodes.<br><br>METHODS: Eligible individuals (n&#x2009;=&#x2009;105) were assigned into three groups, Group A (active tDCS-active rTMS), Group B (sham tDCS-active rTMS), and Group C (active tDCS-sham rTMS). All participants received 3-week treatment in which every participant received 15 sessions of stimulation through the study, 5 sessions every week, with tDCS treatment followed by neuronavigated rTMS every session. We evaluated the cognitive, emotional, and safety outcomes at week-0 (w0, baseline), week-3 (w3, immediately post-treatment), and week-8 (w8, follow-up period). The THINC-integrated tool (THINC-it), 17-item Hamilton Depression Rating Scale, and Young Mania Rating Scale were applied for evaluating the cognitive function and emotional state, respectively. Data were analyzed by repeated measure ANOVA and paired t-test.<br><br>RESULTS: Eventually, 32 patients in Group A, 27 in Group B, and 23 in Group C completed the entire treatment. Compared to Groups B and C, Group A showed greater improvement in Symbol Check items (Time and Accuracy) at W3 and Symbol Check Accuracy at W8 (p&#x2009;<&#x2009;0.01). The W0-W3 analysis indicated a significant improvement in depressive symptoms in both Group A and Group B (p&#x2009;<&#x2009;0.01). Additionally, neuroimaging data revealed increased activity in the calcarine sulcus in Group A, suggesting potential neuroplastic changes in the visual cortex following the electromagnetic stimulation.<br><br>CONCLUSIONS: These findings provide preliminary evidence that the combination of navigated rTMS with tDCS targeting V1 region may serve as a potential treatment strategy for improving cognitive impairment and depressive symptoms in BD patients.<br><br>TRIAL REGISTRATION: Clinical Trial Registry number: NCT05596461.}, }
@article {pmid39702315, year = {2024}, author = {Capecci, M and Gandolfi, M and Straudi, S and Calabrò, RS and Baldini, N and Pepa, L and Andrenelli, E and Smania, N and Ceravolo, MG and Morone, G and Bonaiuti, D and , }, title = {Advancing public health through technological rehabilitation: insights from a national clinician survey.}, journal = {BMC health services research}, volume = {24}, number = {1}, pages = {1626}, pmid = {39702315}, issn = {1472-6963}, mesh = {Humans ; Cross-Sectional Studies ; Italy ; Surveys and Questionnaires ; Male ; Female ; *Public Health ; Physical and Rehabilitation Medicine ; Middle Aged ; Adult ; Rehabilitation ; }, abstract = {INTRODUCTION: In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy.<br><br>METHODS: We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings."<br><br>RESULTS: A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care.<br><br>DISCUSSION: In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.}, }
@article {pmid39700898, year = {2025}, author = {Zhang, Y and Gao, S and Liang, C and Bustillo, J and Kochunov, P and Turner, JA and Calhoun, VD and Wu, L and Fu, Z and Jiang, R and Zhang, D and Jiang, J and Wu, F and Peng, T and Xu, X and Qi, S}, title = {Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia.}, journal = {NeuroImage. Clinical}, volume = {45}, number = {}, pages = {103726}, pmid = {39700898}, issn = {2213-1582}, support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; }, mesh = {Humans ; Male ; Adult ; Female ; *Schizophrenia/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; *Frontal Lobe/physiopathology/diagnostic imaging ; *Occipital Lobe/diagnostic imaging/physiopathology ; Schizophrenia, Treatment-Resistant/diagnostic imaging ; Young Adult ; Middle Aged ; Connectome/methods ; Limbic System/diagnostic imaging/physiopathology ; Nerve Net/diagnostic imaging/physiopathology ; Limbic Lobe/physiopathology/diagnostic imaging ; }, abstract = {BACKGROUND AND HYPOTHESIS: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.<br><br>STUDY DESIGN: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Na&#xef;ve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.<br><br>STUDY RESULTS: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90&#xa0;%) comparing with AAL and YEO in the most diagnostic tasks.<br><br>CONCLUSIONS: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.}, }
@article {pmid39700269, year = {2024}, author = {Li, W and Li, J and Li, J and Wei, C and Laviv, T and Dong, M and Lin, J and Calubag, M and Colgan, LA and Jin, K and Zhou, B and Shen, Y and Li, H and Cui, Y and Gao, Z and Li, T and Hu, H and Yasuda, R and Ma, H}, title = {Boosting neuronal activity-driven mitochondrial DNA transcription improves cognition in aged mice.}, journal = {Science (New York, N.Y.)}, volume = {386}, number = {6728}, pages = {eadp6547}, doi = {10.1126/science.adp6547}, pmid = {39700269}, issn = {1095-9203}, support = {R01 MH080047/MH/NIMH NIH HHS/United States ; R35 NS116804/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Male ; Mice ; *Aging/genetics ; *Brain/metabolism/physiology ; Cell Nucleus/metabolism ; *Cognition ; Cognitive Dysfunction/genetics ; *DNA, Mitochondrial/genetics/metabolism ; Mice, Inbred C57BL ; Mitochondria/metabolism ; *Neurons/metabolism/physiology ; *Transcription, Genetic ; }, abstract = {Deciphering the complex interplay between neuronal activity and mitochondrial function is pivotal in understanding brain aging, a multifaceted process marked by declines in synaptic function and mitochondrial performance. Here, we identified an age-dependent coupling between neuronal and synaptic excitation and mitochondrial DNA transcription (E-TCmito), which operates differently compared to classic excitation-transcription coupling in the nucleus (E-TCnuc). We demonstrated that E-TCmito repurposes molecules traditionally associated with E-TCnuc to regulate mitochondrial DNA expression in areas closely linked to synaptic activation. The effectiveness of E-TCmito weakens with age, contributing to age-related neurological deficits in mice. Boosting brain E-TCmito in aged animals ameliorated these impairments, offering a potential target to counteract age-related cognitive decline.}, }
@article {pmid39698084, year = {2024}, author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A}, title = {Tactile imagery affects cortical responses to vibrotactile stimulation of the fingertip.}, journal = {Heliyon}, volume = {10}, number = {23}, pages = {e40807}, pmid = {39698084}, issn = {2405-8440}, abstract = {Mental imagery is a crucial cognitive process, yet its underlying neural mechanisms remain less understood compared to perception. Furthermore, within the realm of mental imagery, the somatosensory domain is particularly underexplored compared to other sensory modalities. This study aims to investigate the influence of tactile imagery (TI) on cortical somatosensory processing. We explored the cortical manifestations of TI by recording EEG activity in healthy human subjects. We investigated event-related somatosensory oscillatory dynamics during TI compared to actual tactile stimulation, as well as somatosensory evoked potentials (SEPs) in response to short vibrational stimuli, examining their amplitude-temporal characteristics and spatial distribution across the scalp. EEG activity exhibited significant changes during TI compared to the no-imagery baseline. TI caused event-related desynchronization (ERD) of the contralateral μ-rhythm, with a notable correlation between ERD during imagery and real stimulation across subjects. TI also modulated several SEP components in sensorimotor and frontal areas, showing increases in the contralateral P100 and P300, contra- and ipsilateral P300, frontal P200, and parietal P600 components. The results clearly indicate that TI affects cortical processing of somatosensory stimuli, impacting EEG responses in various cortical areas. The assessment of SEPs in EEG could serve as a versatile marker of tactile imagery in practical applications. We propose incorporating TI in imagery-based brain-computer interfaces (BCIs) to enhance sensorimotor restoration and sensory substitution. This approach underscores the importance of somatosensory mental imagery in cognitive neuroscience and its potential applications in neurorehabilitation and assistive technologies.}, }
@article {pmid39697780, year = {2024}, author = {Wan, X and Xing, S and Zhang, Y and Duan, D and Liu, T and Li, D and Yu, H and Wen, D}, title = {Combining motion performance with EEG for diagnosis of mild cognitive impairment: a new perspective.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1476730}, pmid = {39697780}, issn = {1662-4548}, }
@article {pmid39697779, year = {2024}, author = {Cui, S and Lee, D and Wen, D}, title = {Toward brain-inspired foundation model for EEG signal processing: our opinion.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1507654}, pmid = {39697779}, issn = {1662-4548}, }
@article {pmid39696695, year = {2024}, author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ}, title = {CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.}, journal = {Translational neurodegeneration}, volume = {13}, number = {1}, pages = {64}, pmid = {39696695}, issn = {2047-9158}, support = {81970998//National Natural Science Foundation of China/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; }, mesh = {Animals ; *Alzheimer Disease/genetics/pathology/metabolism ; *p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; Mice ; *Adaptor Proteins, Signal Transducing/genetics/deficiency ; Mice, Transgenic ; Phenotype ; Disease Models, Animal ; Humans ; Mice, Knockout ; Neurons/pathology/metabolism ; Cytoskeletal Proteins ; }, abstract = {BACKGROUND: Alzheimer's disease (AD) is the most common form of neurodegenerative disorder, which is characterized by a decline in cognitive abilities. Genome-wide association and clinicopathological studies have demonstrated that the CD2-associated protein (CD2AP) gene is one of the most important genetic risk factors for AD. However, the precise mechanisms by which CD2AP is linked to AD pathogenesis remain unclear.<br><br>METHODS: The spatiotemporal expression pattern of CD2AP was determined. Then, we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion, using immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay, electrophysiology and behavioral tests. Additionally, we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD pathogenesis. Finally, the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our findings.<br><br>RESULTS: CD2AP is widely expressed in various regions of the mouse brain, with predominant expression in neurons and vascular endothelial cells. In APP/PS1 mice, neuronal knockout of Cd2ap significantly aggravated tau pathology, synaptic impairments and cognitive deficits. Mechanistically, the knockout of Cd2ap activated p38 mitogen-activated protein kinase (MAPK) signaling, which contributed to increased tau phosphorylation, synaptic injury, neuronal apoptosis and cognitive impairment. Furthermore, the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK inhibitor.<br><br>CONCLUSION: Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway, identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.}, }
@article {pmid39695740, year = {2024}, author = {Lowers, V and Kirby, R and Young, B and Harris, RV}, title = {Scoping review of fidelity strategies used in behaviour change trials delivered in primary dental care settings.}, journal = {Trials}, volume = {25}, number = {1}, pages = {824}, pmid = {39695740}, issn = {1745-6215}, mesh = {Humans ; *Randomized Controlled Trials as Topic/standards/methods ; *Primary Health Care/standards ; *Dental Care/standards ; Health Behavior ; Research Design/standards ; Behavior Therapy/methods/standards ; Oral Health/standards ; Health Knowledge, Attitudes, Practice ; }, abstract = {BACKGROUND: Primary dental care settings are strategically important locations where randomised controlled trials (RCTs) of behaviour change interventions (BCIs) can be tested to tackle oral diseases. Findings have so far produced equivocal results. Improving treatment fidelity is posed as a mechanism to improve scientific rigour, consistency and implementation of BCIs. The National Institutes of Health Behaviour Change Consortium (NIH BCC) developed a tool to assess and evaluate treatment fidelity in health behaviour change interventions, which has yet to be applied to the primary dental care BCI literature.<br><br>METHOD: We conducted a scoping review of RCTs delivered in primary dental care by dental team members (in real-world settings) between 1980 and 2023. Eligible studies were coded using the NIH BCC checklist to determine the presence of reported fidelity strategies across domains: design, training, delivery, receipt and enactment.<br><br>RESULTS: We included 34 eligible articles, reporting 21 RCTs. Fidelity reporting variations were found both between and within NIH BCC domains: strategy reporting ranged from 9.5 to 85.7% in design, 9.5 to 57.1% in training, 0 to 66.7% in delivery, 14.3 to 36.8% in receipt and 13.3 to 33.3% in enactment. The most reported domain was design (M&#x2009;=&#x2009;0.45), and the least reported domain was delivery (M&#x2009;=&#x2009;0.21). Only one study reported over 50% of the recommended strategies in every domain.<br><br>CONCLUSIONS: This review revealed inconsistencies in fidelity reporting with no evidence that fidelity guidelines or frameworks were being used within primary dental care trials. This has highlighted issues with interpretability, reliability and reproducibility of research findings. Recommendations are proposed to assist primary dental care trialists with embedding fidelity strategies into future research.}, }
@article {pmid39694240, year = {2025}, author = {Fonseca, M and Kurban, D and Roy, JP and Santschi, DE and Molgat, E and Yang, DA and Dufour, S}, title = {Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis.}, journal = {Journal of dairy science}, volume = {108}, number = {4}, pages = {3917-3928}, doi = {10.3168/jds.2024-25403}, pmid = {39694240}, issn = {1525-3198}, mesh = {Animals ; Cattle ; Female ; *Mastitis, Bovine/diagnosis ; *Milk/cytology ; Cell Count/veterinary ; *Mammary Glands, Animal ; Dairying ; }, abstract = {Mastitis, an inflammation of the udder primarily caused by an IMI, is one of the most common diseases in dairy cattle. Somatic cell count has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential SCC (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, although it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level SCS or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (SD) of 1.4 (1.3), and, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian credible interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.}, }
@article {pmid39693762, year = {2025}, author = {Oxley, TJ and Deo, DR and Cernera, S and Sawyer, A and Putrino, D and Ramsey, NF and Fry, A}, title = {The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ada0e6}, pmid = {39693762}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; Personal Autonomy ; User-Computer Interface ; }, abstract = {Objective. Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required for successful clinical translation and patient adoption of iBCIs may be under recognized within traditional academic iBCI research and deserve further consideration.Approach. Here we consider potentially critical factors to achieve iBCI user autonomy, reflecting the authors' perspectives on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023.Main results. Four key considerations were identified: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.Significance. Addressing these considerations may enable successful clinical translation of iBCIs.}, }
@article {pmid39693735, year = {2024}, author = {Kim, J and Cho, YS and Kim, SP}, title = {Task-relevant stimulus design improves P300-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada0e3}, pmid = {39693735}, issn = {1741-2552}, abstract = {OBJECTIVE: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.<br><br>APPROACH: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.<br><br>MAIN RESULTS: With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.}, }
@article {pmid39693734, year = {2024}, author = {Srisrisawang, N and Müller-Putz, GR}, title = {Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ada0ea}, pmid = {39693734}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Hand/physiology ; *Movement/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Adult ; Biomechanical Phenomena/physiology ; }, abstract = {Objective.The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored.Approach.We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model.Main results.Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification.Significance.Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.}, }
@article {pmid39693678, year = {2025}, author = {Shen, J and Wang, K and Gao, W and Liu, JK and Xu, Q and Pan, G and Chen, X and Tang, H}, title = {Temporal spiking generative adversarial networks for heading direction decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {106975}, doi = {10.1016/j.neunet.2024.106975}, pmid = {39693678}, issn = {1879-2782}, mesh = {Animals ; *Neural Networks, Computer ; *Neurons/physiology ; *Action Potentials/physiology ; Parietal Lobe/physiology ; Models, Neurological ; Macaca mulatta ; }, abstract = {The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.}, }
@article {pmid39692757, year = {2025}, author = {van Oosterhout, K and Chilundo, A and Branco, MP and Aarnoutse, EJ and Timmermans, M and Fattori, M and Ramsey, NF and Cantatore, E}, title = {Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {6}, pages = {e2408576}, pmid = {39692757}, issn = {2198-3844}, support = {//Dutch Research Council (NWO)/ ; 17608//Stichting voor de Technische Wetenschappen/ ; 19072//Stichting voor de Technische Wetenschappen/ ; UEBIT//EWUU Alliance/ ; }, abstract = {Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm[-2]). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm[-2]. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.}, }
@article {pmid39692430, year = {2024}, author = {Nie, J and Huang, T and Sun, Y and Peng, Z and Dong, W and Chen, J and Zheng, D and Guo, F and Shi, W and Ling, Y and Zhao, W and Yang, H and Shui, T and Yan, X}, title = {Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study.}, journal = {JMIR public health and surveillance}, volume = {10}, number = {}, pages = {e63146}, pmid = {39692430}, issn = {2369-2960}, mesh = {*Hand, Foot and Mouth Disease/epidemiology/prevention &amp; control ; Humans ; China/epidemiology ; *COVID-19/epidemiology/prevention &amp; control ; Infant ; Child, Preschool ; Incidence ; *Enterovirus A, Human ; *Viral Vaccines/administration &amp; dosage ; Male ; Child ; Female ; Adolescent ; Pandemics/prevention &amp; control ; }, abstract = {BACKGROUND: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control.<br><br>OBJECTIVE: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD.<br><br>METHODS: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively.<br><br>RESULTS: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from -11% to -229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions-ARIMA(1,0,1)(0,1,0)[12]-from February to September 2020, with considerable negative APEs (ranging from -31% to -2248%).<br><br>CONCLUSIONS: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.}, }
@article {pmid39691819, year = {2024}, author = {Vieira, R and Moreno, P and Vourvopoulos, A}, title = {EEG-based action anticipation in human-robot interaction: a comparative pilot study.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1491721}, pmid = {39691819}, issn = {1662-5218}, abstract = {As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.}, }
@article {pmid39691581, year = {2024}, author = {Eken, A and Yüce, M and Yükselen, G and Erdoğan, SB}, title = {Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.}, journal = {Neurophotonics}, volume = {11}, number = {4}, pages = {045015}, pmid = {39691581}, issn = {2329-423X}, abstract = {SIGNIFICANCE: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.<br><br>AIM: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.<br><br>APPROACH: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90&#xa0;min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.<br><br>RESULTS: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.<br><br>CONCLUSIONS: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.}, }
@article {pmid39688131, year = {2025}, author = {Wang, S and Song, X and Xu, J and Wang, J and Yu, L}, title = {Flexible silicon for high-performance photovoltaics, photodetectors and bio-interfaced electronics.}, journal = {Materials horizons}, volume = {12}, number = {4}, pages = {1106-1132}, doi = {10.1039/d4mh01466a}, pmid = {39688131}, issn = {2051-6355}, abstract = {Silicon (Si) is currently the most mature and reliable semiconductor material in the industry, playing a pivotal role in the development of modern microelectronics, renewable energy, and bio-electronic technologies. In recent years, widespread research attention has been devoted to the development of advanced flexible electronics, photovoltaics, and bio-interfaced sensors/detectors, boosting their emerging applications in distributed energy sources, healthcare, environmental monitoring, and brain-computer interfaces (BCIs). Despite the rigid and brittle nature of Si, a series of new fabrication technologies and integration strategies have been developed to enable a wide range of c-Si-based high-performance flexible photovoltaics and electronics, which were previously only achievable with intrinsically soft organic and polymer semiconductors. More interestingly, programmable geometric engineering of crystalline silicon (c-Si) units and logic circuits has been explored to enable the fabrication of various highly flexible nanoprobes for intracellular sensing and the deployment of soft BCI matrices to record and understand brain neural activities for the development of advanced neuroprosthetics. This review will systematically examine the latest progress in the fabrication of Si-based flexible solar cells, photodetectors, and biological probing interfaces over the past decade, identifying key design principles, mechanisms, and technological milestones achieved through novel geometry, morphology, and composition control. These advancements, when combined, will not only promote the practical applications of sustainable energy and wearable electronics but also spur new breakthroughs in emerging human-machine interfaces (HMIs) and artificial intelligence applications, which hold significant implications for understanding neural activities, implementing more efficient artificial Intelligence (AI) algorithms, and developing new therapies or treatments. Finally, we will summarize and provide an outlook on the current challenges and future opportunities of Si-based electronics, flexible optoelectronics, and bio-sensing.}, }
@article {pmid39687714, year = {2024}, author = {Hofmann, MJ and Chang, YN and Brouwer, H and Zock, M}, title = {Editorial: Neurocomputational models of language processing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1524366}, doi = {10.3389/fnhum.2024.1524366}, pmid = {39687714}, issn = {1662-5161}, }
@article {pmid39687306, year = {2024}, author = {Kancaoğlu, M and Kuntalp, M}, title = {Low-cost, mobile EEG hardware for SSVEP applications.}, journal = {HardwareX}, volume = {19}, number = {}, pages = {e00567}, pmid = {39687306}, issn = {2468-0672}, abstract = {The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.}, }
@article {pmid39687114, year = {2024}, author = {Ma, J and Rui, Z and Zou, Y and Qin, Z and Zhao, Z and Zhang, Y and Mao, Z and Bai, H and Zhang, J}, title = {Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.}, journal = {Heliyon}, volume = {10}, number = {23}, pages = {e39072}, pmid = {39687114}, issn = {2405-8440}, abstract = {This study investigates the effects of occipital lobe tumors on visual processing and the role of brain-computer interface (BCI) technologies in post-surgical visual rehabilitation. Through a combination of pre-surgical functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI), intra-operative direct cortical stimulation (DCS) and Electrocorticography (ECoG), and post-surgical BCI interventions, we provide insight into the complex dynamics between occipital lobe tumors and visual function. Our results highlight a discrepancy between clinical assessments of visual field damage and the patient's reported visual experiences, suggesting a residual functional capacity within the damaged occipital regions. Additionally, the absence of expected visual phenomena during surgery and the promising outcomes from BCI-driven rehabilitation underscore the complexity of visual processing and the potential of technology-enhanced rehabilitation strategies. This work emphasizes the need for an interdisciplinary approach in developing effective treatments for visual impairments related to brain tumors, illustrating the significant implications for neurosurgical practices and the advancement of rehabilitation sciences.}, }
@article {pmid39686818, year = {2025}, author = {Lv, Q and Li, Q and Cao, P and Wei, C and Li, Y and Wang, Z and Wang, L}, title = {Designing Silk Biomaterials toward Better Future Healthcare: The Development and Application of Silk-Based Implantable Electronic Devices in Clinical Diagnosis and Therapy.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {37}, number = {8}, pages = {e2411946}, doi = {10.1002/adma.202411946}, pmid = {39686818}, issn = {1521-4095}, support = {2022YFC2408100//National Key Research and Development Program of China/ ; 82372511//National Natural Science Foundation of China/ ; 82302585//National Natural Science Foundation of China/ ; 82072167//National Natural Science Foundation of China/ ; 82272277//National Natural Science Foundation of China/ ; 82173315//National Natural Science Foundation of China/ ; 81974382//National Natural Science Foundation of China/ ; [2022] No.11//Hubei Province Science and Technology Innovation Team Project/ ; 2022BCA013//the Major Scientific and Technological Innovation Projects of Hubei Province/ ; 2021CFB416//Natural Science Foundation of Hubei Province/ ; 2024AFB652//Natural Science Foundation of Hubei Province/ ; 2022CFB736//Natural Science Foundation of Hubei Province/ ; }, mesh = {*Biocompatible Materials/chemistry ; *Silk/chemistry ; Humans ; Animals ; Prostheses and Implants ; Brain-Computer Interfaces ; Electronics, Medical/instrumentation ; }, abstract = {Implantable medical electronic devices (IMEDs) have attracted great attention and shown versatility for solving clinical problems ranging from real-time monitoring of physiological/ pathological states to electrical stimulation therapy and from monitoring brain cell activity to deep brain stimulation. The ongoing challenge is to select appropriate materials in target device configuration for biomedical applications. Currently, silk-based biomaterials have been developed for the design of diagnostic and therapeutic electronic devices due to their excellent properties and abundant active sites in the structure. Herein, the aim is to summarize the structural characteristics, physicochemical properties, and bioactivities of natural silk biomaterials as well as their derived materials, with a particular focus on the silk-based implantable biomedical electronic devices, such as implantable devices for invasive brain-computer interfaces, neural recording, and in vivo electrostimulation. In addition, future opportunities and challenges are also envisioned, hoping to spark the interests of researchers in interdisciplinary fields such as biomaterials, clinical medicine, and electronics.}, }
@article {pmid39686393, year = {2024}, author = {Zhuang, W and Zhang, Y and Wang, Y and He, K}, title = {3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686393}, issn = {1424-8220}, mesh = {Humans ; *Neural Networks, Computer ; Learning/physiology ; Students ; Emotions/physiology ; Deep Learning ; Machine Learning ; }, abstract = {Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.}, }
@article {pmid39686227, year = {2024}, author = {Megalingam, RK and Sankardas, KS and Manoharan, SK}, title = {An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686227}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Motion ; Signal Processing, Computer-Assisted ; Wheelchairs ; Imagination/physiology ; }, abstract = {Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain-computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model's effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.}, }
@article {pmid39686148, year = {2024}, author = {Li, LL and Cao, GZ and Zhang, YP and Li, WC and Cui, F}, title = {MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686148}, issn = {1424-8220}, support = {U1813212//the State Key Program of National Natural Science Foundation of China/ ; 52277061//National Natural Science Foundation of China/ ; JCYJ20220818095804009 and JSGG20200701095406010//Shenzhen Science and Technology Program/ ; 20220809200041001//Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Lower Extremity/physiology ; Imagination/physiology ; Attention/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified.}, }
@article {pmid39685825, year = {2024}, author = {Villiger, AS and Hoehn, D and Ruggeri, G and Vaineau, C and Nirgianakis, K and Imboden, S and Kuhn, A and Mueller, MD}, title = {Lower Urinary Tract Dysfunction Among Patients Undergoing Surgery for Deep Infiltrating Endometriosis: A Prospective Cohort Study.}, journal = {Journal of clinical medicine}, volume = {13}, number = {23}, pages = {}, pmid = {39685825}, issn = {2077-0383}, abstract = {Background/Objectives: Postsurgical lower urinary tract dysfunction (LUTD) is a common problem following deep infiltrating endometriosis (DIE) resection. The condition may be caused either by surgically induced damage to the bladder innervation or by pre-existing endometriosis-associated nerve damage. The aim of this study is to evaluate the efficacy of preoperative and postoperative multichannel urodynamic testing (UD) in identifying pre-existing or surgically induced LUTD among patients with DIE. Methods: Women with suspected DIE and planned surgical resection of DIE at the Department of Obstetrics and Gynecology at the University Hospital of Bern from September 2015 to October 2022 were invited to participate in this prospective cohort study. UD was performed before and 6 weeks after surgery. The primary outcome was the maximum flow rate (uroflow), an indicator of LUTD. Secondary outcomes were further urodynamic observations of cystometry and pressure flow studies, lower urinary tract symptoms (LUTS) as assessed by the International Prostate Symptom Score (IPSS), and pain as assessed by the visual analog scale (VAS). Results: A total of 51 patients requiring surgery for DIE were enrolled in this study. All patients underwent surgical excision of the DIE. The cohort demonstrated a uroflow of 22.1 mL/s prior to surgery, which decreased postoperatively to 21.5 mL/s (p = 0.56, 95%CI -1.5-2.71). The mean bladder contractility index (BCI) exhibited a notable decline from 130.4 preoperatively to 116.6 postoperatively (p = 0.046, 95%CI 0.23-27.27). Significant improvements were observed in the prevalence of dysmenorrhea, abdominal pain, dyspareunia, and dyschezia following surgical intervention (p = <0.001). The IPSS score was within the lower moderate range both pre- and postoperatively (mean 8.37 vs. 8.51, p = 0.893, 95%CI -2.35-2.05). Subgroup analysis identified previous endometriosis surgery as a significant preoperative risk factor for elevated post-void residual (43.6 mL, p = 0.026, 95%CI 13.89-73.37). The postoperative post-void residual increased among participants with DIE on the rectum to 54.39 mL (p = 0.078, 95%CI 24.06-84.71). Participants who underwent hysterectomy exhibited a significantly decreased uroflow (16.4 mL/s, p = 0.014, 95%CI 12-20) and BCI (75.1, p = 0.036, 95%CI 34.9-115.38). Conclusions: Nerve-respecting laparoscopy for DIE may alter bladder function. UD is not advisable before surgery, but the measurement may detect patients with LUTD.}, }
@article {pmid39681925, year = {2024}, author = {Garrott, K and Ogilvie, D and Panter, J and Petticrew, M and Sowden, A and Jones, CP and Foubister, C and Lawlor, ER and Ikeda, E and Patterson, R and van Tulleken, D and Armstrong-Moore, R and Vethanayakam, G and Bo, L and White, M and Adams, J}, title = {Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions.}, journal = {BMC global and public health}, volume = {2}, number = {1}, pages = {13}, pmid = {39681925}, issn = {2731-913X}, support = {Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; }, abstract = {BACKGROUND: The 'agentic demand' of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields' ability to systematically explore these associations.<br><br>METHODS: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots.<br><br>RESULTS: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (two levels), mechanism of action - how the intervention enables or discourages behaviour (five levels) and engagement - recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness.<br><br>CONCLUSIONS: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.}, }
@article {pmid39679398, year = {2024}, author = {Wang, S and Zhang, W and Fu, P and Zhong, Y and Piatkevich, KD and Zhang, D and Lee, HJ}, title = {Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy.}, journal = {Biomedical optics express}, volume = {15}, number = {12}, pages = {6768-6782}, pmid = {39679398}, issn = {2156-7085}, abstract = {The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.}, }
@article {pmid39678727, year = {2024}, author = {Pan, H and Song, W and Li, L and Qin, X}, title = {The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2299-2309}, pmid = {39678727}, issn = {1871-4080}, abstract = {In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.}, }
@article {pmid39678535, year = {2024}, author = {Wang, Y and Gong, L and Zhao, Y and Yu, Y and Liu, H and Yang, X}, title = {Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1493264}, pmid = {39678535}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. We validated the performance of MFF-DANet on the public PhysioNet dataset, achieving optimal decoding accuracies of 61.6% for within-subject case and 52.7% for cross-subject case. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the features demonstrates the effectiveness of each designed module, and visualization of the adjacency matrix indicates that the extracted spatial topological features have physiological interpretability.}, }
@article {pmid39678531, year = {2024}, author = {Wang, N and Tu, WJ}, title = {Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1526723}, pmid = {39678531}, issn = {1662-4548}, }
@article {pmid39677402, year = {2024}, author = {Ninenko, I and Medvedeva, A and Efimova, VL and Kleeva, DF and Morozova, M and Lebedev, MA}, title = {Olfactory neurofeedback: current state and possibilities for further development.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1419552}, pmid = {39677402}, issn = {1662-5161}, abstract = {This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.}, }
@article {pmid39674592, year = {2025}, author = {Barker, N and Parker, H}, title = {Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures.}, journal = {Medical humanities}, volume = {50}, number = {4}, pages = {657-669}, pmid = {39674592}, issn = {1473-4265}, mesh = {Humans ; *Disabled Persons ; *Sports ; *Self-Help Devices ; Brain-Computer Interfaces ; Robotics ; Anthropology, Cultural ; Politics ; }, abstract = {Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability more present and visible across many (para) sporting cultures, and yet sport continues to operate on a tiered system that assumes a normative able body. This paper responds to this moment by offering imaginaries of future hybrid performances that critically engage with the politics and possibilities of novel technologies in sporting arenas and their wider impact on disability futures. These were generated from a collaborative ethnography that centred on becoming spectators of the Cybathlon Games. The Cybathlon Games began in 2016 as a global event where people with disabilities compete with technologies such as Brain-Computer Interfaces or robotic Prosthesis. Our imaginings are presented as three speculative fragments in the form of pages ripped from a comic book series, The In/Visibles These fragments and critical reflections are grounded on themes generated through watching the Games together. The purpose of this paper is not to offer predictions or even visions of desirable futures. Rather we present future technologised sporting bodies and spectacles with a view to extend critical posthuman discussions to these arenas. Through this we highlight: (1) The arbitrariness of where to draw the between un/natural performances; (2) The absurdities of unrestricted and open use of performance technologies when hybrid forms and functions are judged through current sporting-humanist values; and (3) The need to stay alert to socioeconomic and political drivers of sporting and disability futures. We offer these three zones of friction to guide further research when navigating the complex and shifting relations between sport, technology and the (dis)abled body now and into the future.}, }
@article {pmid39656892, year = {2025}, author = {Guo, D and Yao, B and Shao, WW and Zuo, JC and Chang, ZH and Shi, JX and Hu, N and Bao, SQ and Chen, MM and Fan, X and Li, XH}, title = {The Critical Role of YAP/BMP/ID1 Axis on Simulated Microgravity-Induced Neural Tube Defects in Human Brain Organoids.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {12}, number = {5}, pages = {e2410188}, pmid = {39656892}, issn = {2198-3844}, support = {2021YFF1200800//National Key Research and Development Program of China/ ; 82171861//National Natural Science Foundation of China/ ; 81971782//National Natural Science Foundation of China/ ; 82101448//National Natural Science Foundation of China/ ; }, mesh = {*Organoids/metabolism ; Humans ; *Brain/metabolism/pathology ; Neural Tube/metabolism ; Weightlessness Simulation/adverse effects ; Bone Morphogenetic Proteins/metabolism/genetics ; Signal Transduction ; Weightlessness/adverse effects ; Transcription Factors/genetics/metabolism ; }, abstract = {Integrated biochemical and biophysical signals regulate embryonic development. Correct neural tube formation is critical for the development of central nervous system. However, the role of microgravity in neurodevelopment and its underlying molecular mechanisms remain unclear. In this study, the effects of stimulated microgravity (SMG) on the development of human brain organoids are investigated. SMG impairs N-cadherin-based adherens junction formation, leading to neural tube defects associated with dysregulated self-renewal capacity and neuroepithelial disorganization in human brain organoids. Bulk gene expression analyses reveal that SMG alters Hippo and BMP signaling in brain organoids. The neuropathological deficits in SMG-treated organoids can be rescued by regulating YAP/BMP/ID1 axis. Furthermore, sing-cell RNA sequencing data show that SMG results in perturbations in the number and function of neural stem and progenitor cell subpopulations. One of these subpopulations senses SMG cues and transmits BMP signals to the subpopulation responsible for tube morphogenesis, ultimately affecting the proliferating cell population. Finally, SMG intervention leads to persistent neurologic damage even after returning to normal gravity conditions. Collectively, this study reveals molecular and cellular abnormalities associated with SMG during human brain development, providing opportunities for countermeasures to maintain normal neurodevelopment in space.}, }
@article {pmid39672531, year = {2025}, author = {Li, K and Qian, L and Zhang, C and Zhang, J and Xue, C and Zhang, Y and Deng, W}, title = {The entorhinal cortex and cognitive impairment in schizophrenia: A comprehensive review.}, journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry}, volume = {136}, number = {}, pages = {111218}, doi = {10.1016/j.pnpbp.2024.111218}, pmid = {39672531}, issn = {1878-4216}, mesh = {Humans ; *Schizophrenia/physiopathology/complications ; *Entorhinal Cortex/pathology/physiopathology ; *Cognitive Dysfunction/physiopathology/etiology ; Animals ; Schizophrenic Psychology ; }, abstract = {Schizophrenia, a severe mental illness characterized by cognitive impairment and olfactory dysfunction, remains an enigma with its pathological mechanism yet to be fully elucidated. The entorhinal cortex, a pivotal structure involved in numerous neural loop circuits related to olfaction, cognition, and emotion, has garnered significant attention due to its structural and functional abnormalities, which have been implicated in the pathogenesis of schizophrenia. This review focuses on the abnormal structural and functional changes in the entorhinal cortex in schizophrenia patients, as evidenced by neuroimaging, cellular biology, and genetic studies. These changes are posited to play a crucial role in the pathogenesis of cognitive impairment in schizophrenia. Furthermore, this review explores the various intervention strategies targeting the entorhinal cortex in current treatment modalities and proposes potential directions for future research endeavors, thereby providing a novel perspective on unraveling the complexity of neural mechanisms underlying schizophrenia and developing innovative therapeutic approaches for schizophrenia.}, }
@article {pmid39672015, year = {2025}, author = {Hameed, I and Khan, DM and Ahmed, SM and Aftab, SS and Fazal, H}, title = {Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.}, journal = {Computers in biology and medicine}, volume = {185}, number = {}, pages = {109534}, doi = {10.1016/j.compbiomed.2024.109534}, pmid = {39672015}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Machine Learning ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Imagination/physiology ; Deep Learning ; }, abstract = {This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.}, }
@article {pmid39671976, year = {2025}, author = {Brooks, KA and Kolousek, A and Holman, EK and Evans, SS and Govil, N and Alfonso, KP}, title = {MED-EL Bonebridge implantation in pediatric patients age 11 Years and younger: Is it safe and effective?.}, journal = {International journal of pediatric otorhinolaryngology}, volume = {188}, number = {}, pages = {112198}, doi = {10.1016/j.ijporl.2024.112198}, pmid = {39671976}, issn = {1872-8464}, mesh = {Humans ; Child ; Retrospective Studies ; Female ; Male ; *Bone Conduction ; Adolescent ; Treatment Outcome ; Child, Preschool ; Age Factors ; Hearing Loss, Conductive/surgery ; Bone-Anchored Prosthesis ; Cohort Studies ; Hearing Aids ; Speech Perception ; Prosthesis Implantation/adverse effects/methods ; }, abstract = {OBJECTIVE: To present our experience with off-label MED-EL Bonebridge implantation in pediatric patients younger than 12 years of age and compare outcomes to pediatric patients 12 years and older.<br><br>METHODS: Pediatric patients who underwent Bonebridge implantation were included in a retrospective cohort study and were categorized by off-label use (<12 years) and &#x2265;12 years at time of bone conduction implantation (BCI). Hearing outcomes were collected after implant activation, which was typically 4-8 weeks post-implantation. Mann-Whitney U tests were performed to assess for differences between audiometric outcomes. Significance was set at p&#xa0;<&#xa0;0.05.<br><br>RESULTS: Twenty patients (25 implants)&#xa0;<&#xa0;12 years of age and 17 patients (23 implants) &#x2265;12 years of age underwent BCI. Pre-BCI speech recognition threshold (SRT) was better for the older patient group (median 50&#xa0;dB) than the younger patient group (median 60&#xa0;dB). Post-BCI SRT, however, was significantly lower in the younger patient group (median 22.5&#xa0;dB) as compared to the older patient group (median 35&#xa0;dB), (p&#xa0;<&#xa0;0.001, Z&#xa0;=&#xa0;3.1). The two groups performed similarly on age-appropriate wordlists presented at 50&#xa0;dB HL in aided conditions (p&#xa0;>&#xa0;0.05, -1<Z&#xa0;<&#xa0;1). Postoperative complication rate was low for both age groups (9&#xa0;% vs 8&#xa0;%) and operative time was similar for both groups of patients (p=0.96, Z=0.52).<br><br>CONCLUSION: Pediatric patients younger than 12 years showed similar or better audiometric benefit from off-label Bonebridge implantation when compared to older patients. Pediatric patients younger than 12 years can be considered Bonebridge implant candidates if clinically indicated; Bonebridge implantation in this age group appears safe and technically feasible.}, }
@article {pmid39671798, year = {2024}, author = {Daly, I and Williams, N and Nasuto, SJ}, title = {TMS-evoked potential propagation reflects effective brain connectivity.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9ee0}, pmid = {39671798}, issn = {1741-2552}, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Brain/physiology ; Young Adult ; Nerve Net/physiology ; Evoked Potentials/physiology ; Feedback, Sensory/physiology ; Connectome/methods ; Evoked Potentials, Motor/physiology ; Psychomotor Performance/physiology ; }, abstract = {Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.}, }
@article {pmid39671787, year = {2025}, author = {H Liu, D and Hsieh, JC and Alawieh, H and Kumar, S and Iwane, F and Pyatnitskiy, I and Ahmad, ZJ and Wang, H and Millán, JDR}, title = {Novel AIRTrode-based wearable electrode supports long-term, online brain-computer interface operations.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad9edf}, pmid = {39671787}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Electrodes ; *Wearable Electronic Devices ; Adult ; Female ; Young Adult ; Online Systems ; }, abstract = {Objective.Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, holding potential promise to benefit users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.Approach.In this study, four healthy participants utilized AIRTrodes in two BCI control tasks-continuous and discrete-across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).Main Results.Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.Significance.AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.}, }
@article {pmid39671281, year = {2024}, author = {Ghodrati, MT and Aghababaei, S and Mirfathollahi, A and Shalchyan, V and Zarrindast, MR and Daliri, MR}, title = {Protocol for state-based decoding of hand movement parameters using neural signals.}, journal = {STAR protocols}, volume = {5}, number = {4}, pages = {103503}, pmid = {39671281}, issn = {2666-1667}, mesh = {Humans ; *Hand/physiology ; *Movement/physiology ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Somatosensory Cortex/physiology ; }, abstract = {We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.[1].}, }
@article {pmid39670475, year = {2024}, author = {Xie, S and He, C}, title = {An empirical study on native Mandarin-speaking children's metonymy comprehension development.}, journal = {Journal of child language}, volume = {}, number = {}, pages = {1-28}, doi = {10.1017/S0305000924000539}, pmid = {39670475}, issn = {1469-7602}, abstract = {This study investigates Mandarin-speaking children's (age 3-7) comprehension development of novel and conventional metonymy, combining online and offline methods. Both online and offline data show significantly better performances from the oldest group (6-to-7-year-old) and a delayed acquisition of conventional metonymy compared with novel metonymy. However, part of offline data shows no significant difference between adjacent age groups, while the eye-tracking data show a chronological development from age 3-7. Furthermore, in offline tasks, the three-year-old group features a high choice randomness and the four-to-five-year-olds show the longest reaction time. Therefore, we argue that, not only age but also metonymy type can influence metonymy acquisition, and that a lack of socio-cultural experience can be a source of acquisition difficulty for children under six. Methodologically speaking, we believe that online methods should not be considered superior to offline ones as they investigate different aspects of implicit and explicit language comprehension.}, }
@article {pmid39669979, year = {2024}, author = {Nakamura, D and Kaji, S and Kanai, R and Hayashi, R}, title = {Unsupervised method for representation transfer from one brain to another.}, journal = {Frontiers in neuroinformatics}, volume = {18}, number = {}, pages = {1470845}, pmid = {39669979}, issn = {1662-5196}, abstract = {Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person's data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person's brain into that obtained from another person's brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals' data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals' sensations.}, }
@article {pmid39669288, year = {2024}, author = {Nejatbakhsh, A and Fumarola, F and Esteki, S and Toyoizumi, T and Kiani, R and Mazzucato, L}, title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.}, journal = {Physical review research}, volume = {5}, number = {4}, pages = {}, pmid = {39669288}, issn = {2643-1564}, support = {R01 DA055439/DA/NIDA NIH HHS/United States ; R01 MH109180/MH/NIMH NIH HHS/United States ; R01 MH127375/MH/NIMH NIH HHS/United States ; R01 NS118461/NS/NINDS NIH HHS/United States ; }, abstract = {A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream with high efficacy, a procedure currently achieved by labor-intensive and potentially harmful trial and error. Can one predict the effects of electrical stimulation on neural activity based on the circuit dynamics during spontaneous periods? Here we show that the effects of single-site micro-stimulation on ensemble activity in an alert monkey's prefrontal cortex can be predicted solely based on the ensemble's spontaneous activity. We first inferred the ensemble's causal flow based on the directed functional interactions inferred during spontaneous periods using convergent cross-mapping and showed that it uncovers a causal hierarchy between the recording electrodes. We find that causal flow inferred at rest successfully predicts the spatiotemporal effects of micro-stimulation. We validate the computational features underlying causal flow using ground truth data from recurrent neural network models, showing that it is robust to noise and common inputs. A detailed comparison between convergent-cross mapping and alternative methods based on information theory reveals the advantages of the former method in predicting perturbation effects. Our results elucidate the causal interactions within neural ensembles and will facilitate the design of intervention protocols and targeted circuit manipulations suitable for brain-machine interfaces.}, }
@article {pmid39667504, year = {2025}, author = {Cao, HL and Wei, W and Meng, YJ and Tao, YJ and Yang, X and Li, T and Guo, WJ}, title = {Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals.}, journal = {Brain research bulletin}, volume = {220}, number = {}, pages = {111166}, doi = {10.1016/j.brainresbull.2024.111166}, pmid = {39667504}, issn = {1873-2747}, mesh = {Humans ; Male ; *Memory, Short-Term/physiology ; *Alcoholism/pathology/diagnostic imaging/physiopathology ; Adult ; *Magnetic Resonance Imaging/methods ; *Cerebral Cortex/diagnostic imaging/pathology/physiopathology ; Middle Aged ; Neuropsychological Tests ; Alcohol Abstinence ; }, abstract = {BACKGROUND: Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.<br><br>METHODS: Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.<br><br>RESULTS: Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.<br><br>CONCLUSION: Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.}, }
@article {pmid39667216, year = {2025}, author = {Sun, P and De Winne, J and Zhang, M and Devos, P and Botteldooren, D}, title = {Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {183}, number = {}, pages = {107003}, doi = {10.1016/j.neunet.2024.107003}, pmid = {39667216}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; *Brain-Computer Interfaces ; Acoustic Stimulation/methods ; Photic Stimulation/methods ; Brain/physiology ; Auditory Perception/physiology ; Neurons/physiology ; Visual Perception/physiology ; Neural Networks, Computer ; }, abstract = {Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli.}, }
@article {pmid39667215, year = {2025}, author = {Lan, Y and Wang, Y and Zhang, Y and Zhu, H}, title = {Low-power and lightweight spiking transformer for EEG-based auditory attention detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {183}, number = {}, pages = {106977}, doi = {10.1016/j.neunet.2024.106977}, pmid = {39667215}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; *Attention/physiology ; Humans ; *Neural Networks, Computer ; Auditory Perception/physiology ; Acoustic Stimulation/methods ; Neurons/physiology ; Action Potentials/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; Models, Neurological ; }, abstract = {EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.}, }
@article {pmid39665789, year = {2024}, author = {Tang, C and Wang, P and Li, Z and Zhong, S and Yang, L and Li, G}, title = {Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges.}, journal = {Neural regeneration research}, volume = {}, number = {}, pages = {}, doi = {10.4103/NRR.NRR-D-24-00613}, pmid = {39665789}, issn = {1673-5374}, abstract = {Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, including regenerative peripheral nerve interfaces, targeted muscle and sensory reinnervation, agonist-antagonist myoneural interfaces, and brain-machine interfaces. Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and high-resolution electrodes, which enhance the performance and longevity of neural machine interface technology. However, significant challenges remain, such as signal interference, fibrous tissue encapsulation, and the need for precise anatomical localization and reconstruction. The integration of advanced signal processing algorithms, particularly those utilizing artificial intelligence and machine learning, has the potential to improve the accuracy and reliability of neural signal interpretation, which will make neural machine interface technologies more intuitive and effective. These technologies have broad, impactful clinical applications, ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation. This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering, clinical surgery, and neuroengineering to develop more sophisticated and reliable interfaces. By addressing existing limitations and exploring new technological frontiers, neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation, promising enhanced mobility, independence, and quality of life for individuals with neurological impairments. By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles, researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.}, }
@article {pmid39664295, year = {2024}, author = {Chen, LX and Zhang, MD and Xu, HF and Ye, HQ and Chen, DF and Wang, PS and Bao, ZW and Zou, SM and Lv, YT and Wu, ZY and Li, HF}, title = {Single-Nucleus RNA Sequencing Reveals the Spatiotemporal Dynamics of Disease-Associated Microglia in Amyotrophic Lateral Sclerosis.}, journal = {Research (Washington, D.C.)}, volume = {7}, number = {}, pages = {0548}, pmid = {39664295}, issn = {2639-5274}, abstract = {Disease-associated microglia (DAM) are observed in neurodegenerative diseases, demyelinating disorders, and aging. However, the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis (ALS) remain unclear. Using a mouse model of ALS that expresses a human SOD1 gene mutation, we found that the microglia subtype DAM begins to appear following motor neuron degeneration, primarily in the brain stem and spinal cord. Using reverse transcription quantitative polymerase chain reaction, RNAscope in situ hybridization, and flow cytometry, we found that DAM increased in number as the disease progressed, reaching their peak in the late disease stage. DAM responded to disease progression in both SOD1[G93A] mice and sporadic ALS and C9orf72-mutated patients. Motor neuron loss in SOD1[G93A] mice exhibited 2 accelerated phases: P90 to P110 (early stage) and P130 to P150 (late stage). Some markers were synchronized with the accelerated phase of motor neuron loss, suggesting that these proteins may be particularly responsive to disease progression. Through pseudotime trajectory analysis, we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia. Interestingly, we used the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 to deplete microglia in SOD1[G93A] mice and observed that DAM survival is independent of CSF1R. An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes. These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS. Inducing the DAM phenotype and enhancing its function during the early phase of disease progression, or the time window between P130 and P150 where motor neuron loss slows, could serve as a neuroprotective strategy for ALS.}, }
@article {pmid39663729, year = {2025}, author = {Qin, Y and Zhao, H and Chang, Q and Liu, Y and Jing, Z and Yu, D and Mugo, SM and Wang, H and Zhang, Q}, title = {Amylopectin-based Hydrogel Probes for Brain-machine Interfaces.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {37}, number = {6}, pages = {e2416926}, doi = {10.1002/adma.202416926}, pmid = {39663729}, issn = {1521-4095}, support = {22377122//National Natural Science Foundation of China/ ; U22A20183//National Natural Science Foundation of China/ ; SKL202402018//Jilin Province Science and Technology Development Plan/ ; 029GJHZ2024038FN//International Partnership Program of the Chinese Academy of Sciences/ ; }, mesh = {Animals ; *Hydrogels/chemistry ; *Brain-Computer Interfaces ; Rats ; *Amylopectin/chemistry ; Motor Cortex/physiology ; Stroke ; Polymers/chemistry ; Rats, Sprague-Dawley ; Biocompatible Materials/chemistry/pharmacology ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Brain ; }, abstract = {Implantable neural probes hold promise for acquiring brain data, modulating neural circuits, and treating various brain disorders. However, traditional implantable probes face significant challenges in practical applications, such as balancing sensitivity with biocompatibility and the difficulties of in situ neural information monitoring and neuromodulation. To address these challenges, this study developed an implantable hydrogel probe capable of recording neural signals, modulating neural circuits, and treating stroke. Amylopectin is integrated into the hydrogels, which can induce reorientation of the poly(3,4-ethylenedioxythiophene) (PEDOT) chain and create compliant interfaces with brain tissues, enhancing both sensitivity and biocompatibility. The hydrogel probe shows the capability of continuously recording deep brain signals for 8 weeks. The hydrogel probe is effectively utilized to study deep brain signals associated with various physiological activities. Neuromodulation and neural signal monitoring are performed directly in the primary motor cortex of rats, enabling control over their limb behaviors through evoked signals. When applied to the primary motor cortex of stroke-affected rats, neuromodulation significantly reduced the brain infarct area, promoted synaptic reorganization, and restored motor functions and balance. This research represents a significant scientific breakthrough in the design of neural probes for brain monitoring, neural circuit modulation, and the development of brain disease therapies.}, }
@article {pmid39662472, year = {2025}, author = {Xin, Q and Zheng, D and Zhou, T and Xu, J and Ni, Z and Hu, H}, title = {Deconstructing the neural circuit underlying social hierarchy in mice.}, journal = {Neuron}, volume = {113}, number = {3}, pages = {444-459.e7}, doi = {10.1016/j.neuron.2024.11.007}, pmid = {39662472}, issn = {1097-4199}, mesh = {Animals ; Mice ; *Hierarchy, Social ; *Prefrontal Cortex/physiology ; *Neural Pathways/physiology ; Male ; *Neurons/physiology ; *Periaqueductal Gray/physiology ; Optogenetics ; Mice, Inbred C57BL ; Basolateral Nuclear Complex/physiology ; Dorsal Raphe Nucleus/physiology ; Proto-Oncogene Proteins c-fos/metabolism ; }, abstract = {Social competition determines hierarchical social status, which profoundly influences animals' behavior and health. The dorsomedial prefrontal cortex (dmPFC) plays a fundamental role in regulating social competitions, but it was unclear how the dmPFC orchestrates win- and lose-related behaviors through its downstream neural circuits. Here, through whole-brain c-Fos mapping, fiber photometry, and optogenetics- or chemogenetics-based manipulations, we identified anatomically segregated win- and lose-related neural pathways downstream of the dmPFC in mice. Specifically, layer 5 neurons projecting to the dorsal raphe nucleus (DRN) and periaqueductal gray (PAG) promote social competition, whereas layer 2/3 neurons projecting to the anterior basolateral amygdala (aBLA) suppress competition. These two neuronal populations show opposite changes in activity during effortful pushes in competition. In vivo and in vitro electrophysiology recordings revealed inhibition from the lose-related pathway to the win-related pathway. Such antagonistic interplay may represent a central principle in how the mPFC orchestrates complex behaviors through top-down control.}, }
@article {pmid39661668, year = {2024}, author = {Fu, P and Zhang, Y and Wang, S and Ye, X and Wu, Y and Yu, M and Zhu, S and Lee, HJ and Zhang, D}, title = {INSPIRE: Single-beam probed complementary vibrational bioimaging.}, journal = {Science advances}, volume = {10}, number = {50}, pages = {eadm7687}, pmid = {39661668}, issn = {2375-2548}, mesh = {*Spectrum Analysis, Raman/methods ; *Vibration ; Humans ; *Molecular Imaging/methods ; Animals ; Mice ; Spectrophotometry, Infrared/methods ; }, abstract = {Molecular spectroscopy provides intrinsic contrast for in situ chemical imaging, linking the physiochemical properties of biomolecules to the functions of living systems. While stimulated Raman imaging has found successes in deciphering biological machinery, many vibrational modes are Raman inactive or weak, limiting the broader impact of the technique. It can potentially be mitigated by the spectral complementarity from infrared (IR) spectroscopy. However, the vastly different optical windows make it challenging to develop such a platform. Here, we introduce in situ pump-probe IR and Raman excitation (INSPIRE) microscopy, a nascent cross-modality spectroscopic imaging approach by encoding the ultrafast Raman and the IR photothermal relaxation into a single probe beam for simultaneous detection. INSPIRE inherits the merits of complementary modalities and demonstrates high-content molecular imaging of chemicals, cells, tissues, and organisms. Furthermore, INSPIRE applies to label-free and molecular tag imaging, offering possibilities for optical sensing and imaging in biomedicine and materials science.}, }
@article {pmid39661515, year = {2024}, author = {Chen, Z and Tang, S and Xiao, X and Hong, Y and Fu, B and Li, X and Shao, Y and Chen, L and Yuan, D and Long, Y and Wang, H and Hong, H}, title = {Adiponectin receptor 1-mediated basolateral amygdala-prelimbic cortex circuit regulates methamphetamine-associated memory.}, journal = {Cell reports}, volume = {43}, number = {12}, pages = {115074}, doi = {10.1016/j.celrep.2024.115074}, pmid = {39661515}, issn = {2211-1247}, mesh = {Animals ; *Methamphetamine/pharmacology ; *Memory/drug effects/physiology ; *Receptors, Adiponectin/metabolism ; *Basolateral Nuclear Complex/metabolism/drug effects ; Male ; Mice ; *Mice, Inbred C57BL ; Neurons/metabolism/drug effects ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Signal Transduction/drug effects ; Reward ; AMP-Activated Protein Kinases/metabolism ; Piperidines ; }, abstract = {The association between drug-induced rewards and environmental cues represents a promising strategy to address addiction. However, the neural networks and molecular mechanisms orchestrating methamphetamine (MA)-associated memories remain incompletely characterized. In this study, we demonstrated that AdipoRon (AR), a specific adiponectin receptor (AdipoR) agonist, inhibits the formation of MA-induced conditioned place preference (CPP) in MA-conditioned mice, accompanied by suppression of basolateral amygdala (BLA) CaMKIIα neuron activity. Furthermore, we identified an association between the excitatory circuit from the BLA to the prelimbic cortex (PrL) and the integration of MA-induced rewards with environmental cues. We also determined that the phosphorylated AMPK (p-AMPK)/Cav1.3 signaling pathway mediates the modulatory effects of AdipoR1 in PrL-projecting BLA CaMKIIα neurons on the formation of MA reward memories, a process influenced by physical exercise. These findings highlight the critical function of AdipoR1 in the BLA[CaMKIIα]→PrL[CaMKIIα] circuit in regulating MA-related memory formation, suggesting a potential target for managing MA use disorders.}, }
@article {pmid39660042, year = {2024}, author = {Kojima, S and Kanoh, S}, title = {Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1461960}, pmid = {39660042}, issn = {1662-5161}, abstract = {INTRODUCTION: The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.<br><br>METHODS: Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.<br><br>RESULTS: Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.<br><br>DISCUSSION: Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.}, }
@article {pmid39658529, year = {2024}, author = {Wang, WW and Ji, SY and Xu, P and Zhang, Y and Zhang, Y}, title = {The future of G protein-coupled receptor therapeutics: Apelin receptor acts as a prototype for the advancement of precision drug design.}, journal = {Clinical and translational medicine}, volume = {14}, number = {12}, pages = {e70120}, pmid = {39658529}, issn = {2001-1326}, support = {//National Natural Science Foundation of China/ ; 2024C03147//Yan Zhang [ZJU]); the ''Pioneer'' and ''Leading Goose'' R&amp;D Program of Zhejiang/ ; //Ministry of Science and Technology/ ; 2021C03039//the Key R&amp;D Projects of Zhejiang Province/ ; 2020R01006//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 82325004//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 92168114//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 32400575//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 2024T170783//the China Postdoctoral Science Foundation/ ; GZC20232326//Postdoctoral Fellowship Program of CPSF/ ; }, }
@article {pmid39657314, year = {2024}, author = {Li, S and Tian, M and Xu, R and Cichocki, A and Jin, J}, title = {Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9cc1}, pmid = {39657314}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Nonlinear Dynamics ; *Movement/physiology ; Male ; Female ; Adult ; Algorithms ; Motion ; }, abstract = {Objective.Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life.Approach.This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons.Main results.The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511 ± 0.019 (p< 0.05).Significance.This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.}, }
@article {pmid39657294, year = {2025}, author = {Du, L and Zeng, J and Yu, H and Chen, B and Deng, W and Li, T}, title = {Efficacy of bright light therapy improves outcomes of perinatal depression: A systematic review and meta-analysis of randomized controlled trials.}, journal = {Psychiatry research}, volume = {344}, number = {}, pages = {116303}, doi = {10.1016/j.psychres.2024.116303}, pmid = {39657294}, issn = {1872-7123}, mesh = {Humans ; Female ; *Phototherapy/methods ; Pregnancy ; *Randomized Controlled Trials as Topic ; Depression/therapy ; Pregnancy Complications/therapy ; Depression, Postpartum/therapy ; Outcome Assessment, Health Care ; }, abstract = {The efficacy of bright light therapy (BLT) in the context of perinatal depression remains underexplored. This meta-analysis aimed to systematically assess the effectiveness of BLT among perinatal depression. A comprehensive literature search was performed across several databases, including the Cochrane Central Register of Controlled Trials, PubMed, Embase, CNKI and the clinical trials registry platform, covering the period from the inception of each database up to January 2024. The Cochrane Collaboration's bias assessment tool was employed to evaluate the quality of the included studies. Review Manager 5.3 Software was utilized to conduct the meta-analysis. Six trials, encompassed a total of 167 participants diagnosed with perinatal depression were incorporated quantitative analysis, all of those have been published in English, with no restriction on publication year, and used BLT and dim light therapy (DLT) as intervention. The relative risk (RR) of BLT compared to DLT for perinatal depression is 1.46 (fixed effects model, p = 0.04, 95 % CI = [1.02, 2.10]), indicating a significant improvement in depression outcomes compared to DLT groups. The heterogeneity test yielded an I[2] value of 41 % (p = 0.13), indicated a low degree of heterogeneity. Considering the small sample size, we conducted a sensitivity analysis, found RR increased to 2.33 (fixed effects model, p = 0.001, CI = 1.39-3.92). Cochrane Risk of Bias Tool showed only a single study was deemed high quality. This study indicates a beneficial impact of BLT on perinatal depression, subgroup analysis finds no significant mediation effects of different parameters after sensitivity analyses. It is recommended that future studies with larger samples be conducted to explore the effects of BLT on perinatal depression.}, }
@article {pmid39652971, year = {2024}, author = {Hinss, MF and Jahanpour, ES and Brock, AM and Roy, RN}, title = {A passive brain-computer interface for operator mental fatigue estimation in monotonous surveillance operations: time-on-task and performance labeling issues.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9bed}, pmid = {39652971}, issn = {1741-2552}, mesh = {Humans ; *Mental Fatigue/psychology/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Female ; Young Adult ; *Electroencephalography/methods ; Machine Learning ; Psychomotor Performance/physiology ; Eye Movements/physiology ; Algorithms ; }, abstract = {Objective: A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue (MF). This makes detecting MF a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT)-the popular method-and performance metrics for labels. Yet, to our knowledge, they have never been directly compared.Approach: This study was designed to address both issues: the use of a realistic task to elicit MF during a monotonous visual search task and the labeling type used for intra-participant fatigue estimation. Over four blocks of 15 min, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines.Main results: The results show that the capability of a machine learning algorithm to detect MF depends less on the input data but rather on how MF is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if MF is estimated based on behavioral performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%).Significance: TOT-based MF estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of MF are neglected.}, }
@article {pmid39652893, year = {2024}, author = {Noorbasha, SK and Kumar, A}, title = {VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad9bb6}, pmid = {39652893}, issn = {2057-1976}, mesh = {Humans ; *Artifacts ; *Electrooculography/methods ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; *Fourier Analysis ; Blinking ; Eye Movements/physiology ; Computer Simulation ; Brain/physiology/diagnostic imaging ; }, abstract = {The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 versus 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in theαband (0.10 ± 0.01 and 0.17 ± 0.04 versus 0.89 ± 0.91 and 0.22 ± 0.19, 1.32 ± 0.23 and 1.10 ± 0.07, 2.86 ± 1.30 and 1.19 ± 0.07, 3.96 ± 0.56 and 2.42 ± 2.48), and higher correlation coefficient (CC: 0.9732 versus 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in theαband, making it highly suitable for brain-computer interface (BCI) applications.}, }
@article {pmid39651250, year = {2024}, author = {Liang, KF and Kao, JC}, title = {A reinforcement learning based software simulator for motor brain-computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.11.25.625180}, pmid = {39651250}, issn = {2692-8205}, abstract = {Intracortical motor brain-computer interfaces (BCIs) are expensive and time-consuming to design because accurate evaluation traditionally requires real-time experiments. In a BCI system, a user interacts with an imperfect decoder and continuously changes motor commands in response to unexpected decoded movements. This "closed-loop" nature of BCI leads to emergent interactions between the user and decoder that are challenging to model. The gold standard for BCI evaluation is therefore real-time experiments, which significantly limits the speed and community of BCI research. We present a new BCI simulator that enables researchers to accurately and quickly design BCIs for cursor control entirely in software. Our simulator replaces the BCI user with a deep reinforcement learning (RL) agent that interacts with a simulated BCI system and learns to optimally control it. We demonstrate that our simulator is accurate and versatile, reproducing the published results of three distinct types of BCI decoders: (1) a state-of-the-art linear decoder (FIT-KF), (2) a "two-stage" BCI decoder requiring closed-loop decoder adaptation (ReFIT-KF), and (3) a nonlinear recurrent neural network decoder (FORCE).}, }
@article {pmid39651231, year = {2024}, author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T}, title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.11.29.626034}, pmid = {39651231}, issn = {2692-8205}, abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.}, }
@article {pmid39648165, year = {2024}, author = {Zhang, J and Dong, E and Zhang, Y and Zhang, Y}, title = {Harnessing the power of structure-based design: A new lease on life for cardiovascular drug development with apelin receptor modulators.}, journal = {Clinical and translational medicine}, volume = {14}, number = {12}, pages = {e70116}, pmid = {39648165}, issn = {2001-1326}, support = {2021YFF0501401//from the National Key R&amp;D Program of China/ ; 2018YFA0800501//from the National Key R&amp;D Program of China/ ; 82325004//National Natural Science Foundation of China/ ; 92168114//National Natural Science Foundation of China/ ; 92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 82300286//National Natural Science Foundation of China/ ; 92168113//National Natural Science Foundation of China/ ; 32430051//National Natural Science Foundation of China/ ; 22HHXBSS00007//Haihe Laboratory of Cell Ecosystem Innovation Fund/ ; BX20220023//China Postdoctoral Science Foundation/ ; 2022M720288//China Postdoctoral Science Foundation/ ; }, }
@article {pmid39645364, year = {2024}, author = {Abuduaini, Y and Pu, Y and Chen, W and Kong, XZ}, title = {Imaging the Unseen: Charting Amygdalar Tau's Link to Affective Symptoms in Preclinical Alzheimer's Disease.}, journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging}, volume = {9}, number = {12}, pages = {1236-1238}, doi = {10.1016/j.bpsc.2024.10.003}, pmid = {39645364}, issn = {2451-9030}, }
@article {pmid39645086, year = {2025}, author = {Li, S and Zhou, Y and Kong, D and Miao, Y and Guan, N and Gao, G and Jin, J and Ye, H}, title = {A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.}, journal = {Journal of controlled release : official journal of the Controlled Release Society}, volume = {378}, number = {}, pages = {27-37}, doi = {10.1016/j.jconrel.2024.12.006}, pmid = {39645086}, issn = {1873-4995}, mesh = {Animals ; *Optogenetics/methods ; *Homeostasis ; *Insulin/metabolism ; Humans ; *Blood Glucose/analysis ; *Diabetes Mellitus, Experimental/therapy ; Mice ; Male ; Mice, Inbred C57BL ; Electroencephalography ; HEK293 Cells ; Machine Learning ; }, abstract = {With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices. In this study, we developed an autonomous diabetes therapeutic system that employs visually-induced electroencephalography devices to capture and decode event-related potentials using machine learning techniques. We present the visually-induced optogenetically-engineered system for therapeutic expression regulation (VISITER), which generates diverse output commands to control illumination durations. This system regulates insulin expression through optogenetically-engineered cells, achieving blood glucose homeostasis in mice. Our results demonstrate that VISITER effectively and precisely modulates therapeutic protein expression in mammalian cells, facilitating the rapid restoration of blood glucose homeostasis in diabetic mice. These findings underscore the potential for diabetic patients to manage insulin levels autonomously by focusing on target images, paving the way for a more self-directed approach to blood glucose control.}, }
@article {pmid39644999, year = {2025}, author = {Wang, Y and Wang, X and Wang, L and Zheng, L and An, X and Zheng, C}, title = {Attenuated task-responsive representations of hippocampal place cells induced by amyloid-beta accumulation.}, journal = {Behavioural brain research}, volume = {480}, number = {}, pages = {115384}, doi = {10.1016/j.bbr.2024.115384}, pmid = {39644999}, issn = {1872-7549}, mesh = {Animals ; *Amyloid beta-Peptides/metabolism ; Male ; *Place Cells/physiology ; *Spatial Memory/physiology ; *Hippocampus/metabolism ; Rats ; Action Potentials/physiology ; Association Learning/physiology ; }, abstract = {Alzheimer's disease (AD) is a typical neurodegenerative disease featuring deficits in spatial memory, which relies on spatial representations by hippocampal place cells. Place cells exhibit task-responsive representation to support memory encoding and retrieval processes. Yet, it remains unclear how this task-responsive spatial representation was interrupted under AD pathologies. Here, we employed a delayed match-to-place spatial memory task with associative and predictive memory processes, during which we electrophysiologically recorded hippocampal place cells with multi-tetrode hyperdrives in rats with i.c.v. amyloid/saline injection. We found that the directional selectivity of place cells coding was maintained in the Amyloid group. The firing stability was higher during predictive memory than during associative memory in both groups. However, the spatial specificity was decreased in the Amyloid group during both associative and predictive memory. Importantly, the place cells in the Amyloid group exhibited attenuated task-responsive representations, i.e. lack of spatial over-representations towards the goal zone and a higher representation of the rest zone, especially during the predictive memory stage. These results raise a hypothesis that the disrupted task-responsive representations of place cells could be an underlying mechanism of spatial memory deficits induced by amyloid proteins.}, }
@article {pmid39643773, year = {2025}, author = {Liu, X and Zhu, J and Zheng, J and Xu, H}, title = {Role of the Thalamic Reticular Nucleus in Social Memory.}, journal = {Neuroscience bulletin}, volume = {41}, number = {2}, pages = {355-358}, pmid = {39643773}, issn = {1995-8218}, }
@article {pmid39643728, year = {2024}, author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A}, title = {Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {39643728}, issn = {2157-846X}, abstract = {To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.}, }
@article {pmid39642366, year = {2024}, author = {Ikegaya, N and Mallela, AN and Warnke, PC and Kunigk, NG and Liu, F and Schone, HR and Verbaarschot, C and Hatsopoulos, NG and Downey, JE and Boninger, ML and Gaunt, R and Collinger, JL and Gonzalez-Martinez, JA}, title = {A novel robot-assisted method for implanting intracortical sensorimotor devices for brain-computer interface studies: principles, surgical techniques, and challenges.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-9}, doi = {10.3171/2024.7.JNS241296}, pmid = {39642366}, issn = {1933-0693}, support = {R01 NS121079/NS/NINDS NIH HHS/United States ; U01 NS123125/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, abstract = {Precise anatomical implantation of a microelectrode array is fundamental for successful brain-computer interface (BCI) surgery, ensuring high-quality, robust signal communication between the brain and the computer interface. Robotic neurosurgery can contribute to this goal, but its application in BCI surgery has been underexplored. Here, the authors present a novel robot-assisted surgical technique to implant rigid intracortical microelectrode arrays for the BCI. Using this technique, the authors performed surgery in a 31-year-old male with tetraplegia due to a traumatic C4 spinal cord injury that occurred a decade earlier. Each of the arrays was embedded into the parenchyma with a single insertion without complication. Postoperative imaging verified that the devices were placed as intended. With the motor cortex arrays, the participant successfully accomplished 2D control of a virtual arm and hand, with a success rate of 20 of 20 attempts, and recording quality was maintained at 100 and 200 days postimplantation. Intracortical microstimulation of the somatosensory cortex arrays elicited sensations in the fingers and palm. A robotic neurosurgery technique was successfully translated into BCI device implantation as part of an early feasibility trial with the long-term goal of restoring upper-limb function. The technique was demonstrated to be accurate and subsequently contributed to high-quality signal communication.}, }
@article {pmid39642216, year = {2024}, author = {Li, C and Zhang, S and Jiang, J and Wang, S and He, S and Song, J}, title = {Laser-induced adhesives with excellent adhesion enhancement and reduction capabilities for transfer printing of microchips.}, journal = {Science advances}, volume = {10}, number = {49}, pages = {eads9226}, pmid = {39642216}, issn = {2375-2548}, abstract = {Transfer printing based on tunable and reversible adhesive that enables the heterogeneous integration of materials is essential for developing envisioned electronic systems. An adhesive with both adhesion enhancement and reduction capabilities in a rapid and selective manner is challenging. Here, we report a laser-induced adhesive, featuring a geometrically simple shape memory polymer layer on a glass backing, with excellent adhesion modulation capability for programmable pickup and noncontact printing of microchips. Selective and rapid laser heating substantially enhances the adhesive's adhesion strength from kilopascal to megapascal within 10 ms due to the shape fixing effect, allowing for precise and programmable pickup. Conversely, the enhanced adhesion can be quickly reduced and eliminated within 3 ms through the shape recovery effect, enabling noncontact printing. Demonstrations of transfer printing microlight-emitting diodes (LEDs) and mini-LEDs onto various low-adhesive flat, rough, and curved surfaces highlight the unusual capabilities of this adhesive for deterministic assembly.}, }
@article {pmid39640342, year = {2024}, author = {Deruelle, F}, title = {Microwave radiofrequencies, 5G, 6G, graphene nanomaterials: Technologies used in neurological warfare.}, journal = {Surgical neurology international}, volume = {15}, number = {}, pages = {439}, pmid = {39640342}, issn = {2229-5097}, abstract = {BACKGROUND: Scientific literature, with no conflicts of interest, shows that even below the limits defined by the International Commission on Non-Ionizing Radiation Protection, microwaves from telecommunication technologies cause numerous health effects: neurological, oxidative stress, carcinogenicity, deoxyribonucleic acid and immune system damage, electro-hypersensitivity. The majority of these biological effects of non-thermal microwave radiation have been known since the 1970s.<br><br>METHODS: Detailed scientific, political, and military documents were analyzed. Most of the scientific literature comes from PubMed. The other articles (except for a few) come from impacted journals . The rare scientific documents that were not peer reviewed were produced by recognized scientists in their fields. The rest of the documentation comes from official sources: political (e.g., European Union and World Health Organization), military (e.g., US Air Force and NATO), patents, and national newspapers.<br><br>RESULTS: (1) Since their emergence, the authorities have deployed and encouraged the use of wireless technologies (2G, 3G, 4G, WiFi, WiMAX, DECT, Bluetooth, cell phone towers/masts/base stations, small cells, etc.) in full awareness of their harmful effects on health. (2) Consequences of microwave radiation from communication networks are comparable to the effects of low-power directed-energy microwave weapons, whose objectives include behavioral modification through neurological (brain) targeting. Above 20 gigahertz, 5G behaves like an unconventional chemical weapon. (3) Biomedical engineering (via graphene-based nanomaterials) will enable brain-computer connections, linked wirelessly to the Internet of Everything through 5G and 6G networks (2030) and artificial intelligence, gradually leading to human-machine fusion (cyborg) before the 2050s.<br><br>CONCLUSION: Despite reports and statements from the authorities presenting the constant deployment of new wireless communication technologies, as well as medical research into nanomaterials, as society's ideal future, in-depth research into these scientific fields shows, above all, an objective linked to the current cognitive war. It could be hypothesized that, in the future, this aim will correspond to the control of humanity by machines.}, }
@article {pmid39639181, year = {2025}, author = {Qian, M and Wang, J and Gao, Y and Chen, M and Liu, Y and Zhou, D and Lu, HD and Zhang, X and Hu, JM and Roe, AW}, title = {Multiple loci for foveolar vision in macaque monkey visual cortex.}, journal = {Nature neuroscience}, volume = {28}, number = {1}, pages = {137-149}, pmid = {39639181}, issn = {1546-1726}, support = {31627802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52277232//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81701774//National Natural Science Foundation of China (National Science Foundation of China)/ ; 61771423//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Visual Cortex/physiology ; *Magnetic Resonance Imaging ; Visual Pathways/physiology ; Macaca mulatta ; Fovea Centralis/physiology ; Photic Stimulation/methods ; Male ; Brain Mapping ; }, abstract = {In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere. This ring surrounds an area called the 'foveolar core', which is populated by millimeter-scale functional domains sensitive to fine stimuli and high spatial frequencies, consistent with foveolar visual acuity, color and achromatic information and motion. Thus, this elaborate rerepresentation of central vision coupled with a previously unknown foveolar core area signifies a cortical specialization for primate foveation behaviors.}, }
@article {pmid39638842, year = {2024}, author = {Zhang, X and Wei, W and Qian, L and Yao, L and Jin, X and Xing, L and Qian, Z}, title = {Real-time monitoring of bioelectrical impedance for minimizing tissue carbonization in microwave ablation of porcine liver.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {30404}, pmid = {39638842}, issn = {2045-2322}, support = {xcxjh20230333//Nanjing University of Aeronautics and Astronautics Research and Practice Innovation Program/ ; 81727804//National Major Scientifc Instruments and Equipment Development Project Funded by National Natural Science Foundation of China/ ; 82151311//National Natural Science Foundation of China/ ; 12372306//National Natural Science Foundation of China/ ; NP2024102//Fundamental Research Funds for the Central Universities/ ; NJ2024016//Fundamental Research Funds for the Central Universities/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Microwaves ; *Electric Impedance ; *Liver/surgery/metabolism ; Swine ; Ablation Techniques/methods ; Finite Element Analysis ; }, abstract = {The charring tissue generated by the high temperature during microwave ablation can affect the therapeutic effect, such as limiting the volume of the coagulation zone and causing rejection. This paper aimed to prevent tissue carbonization while delivering an appropriate thermal dose for effective ablations by employing a treatment protocol with real-time bioelectrical impedance monitoring. Firstly, the current field response under different microwave ablation statuses is analyzed based on finite element simulation. Next, the change of impedance measured by the electrodes is correlated with the physical state of the ablated tissue, and a microwave ablation carbonization control protocol based on real-time electrical impedance monitoring was established. The finite element simulation results show that the dielectric properties of biological tissues changed dynamically during the ablation process. Finally, the relative change rule of the electrical impedance magnitude of the ex vivo porcine liver throughout the entire MWA process and the reduction of the central zone carbonization were obtained by the MWA experiment. Charring tissue was eliminated without water cooling at 40 W and significantly reduced at 50 W and 60 W. The carbonization during MWA can be reduced according to the changes in tissue electrical impedance to optimize microwave thermal ablation efficacy.}, }
@article {pmid39638804, year = {2024}, author = {Ma, X and Chen, LN and Liao, M and Zhang, L and Xi, K and Guo, J and Shen, C and Shen, DD and Cai, P and Shen, Q and Qi, J and Zhang, H and Zang, SK and Dong, YJ and Miao, L and Qin, J and Ji, SY and Li, Y and Liu, J and Mao, C and Zhang, Y and Chai, R}, title = {Molecular insights into the activation mechanism of GPR156 in maintaining auditory function.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10601}, pmid = {39638804}, issn = {2041-1723}, mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/genetics/chemistry ; *Cryoelectron Microscopy ; Animals ; HEK293 Cells ; Protein Binding ; Protein Multimerization ; Mice ; GTP-Binding Protein alpha Subunits, Gi-Go/metabolism/chemistry/genetics ; Models, Molecular ; }, abstract = {The class C orphan G-protein-coupled receptor (GPCR) GPR156, which lacks the large extracellular region, plays a pivotal role in auditory function through Gi2/3. Here, we firstly demonstrate that GPR156 with high constitutive activity is essential for maintaining auditory function, and further reveal the structural basis of the sustained role of GPR156. We present the cryo-EM structures of human apo GPR156 and the GPR156-Gi3 complex, unveiling a small extracellular region formed by extracellular loop 2 (ECL2) and the N-terminus. The GPR156 dimer in both apo state and Gi3 protein-coupled state adopt a transmembrane (TM)5/6-TM5/6 interface, indicating the high constitutive activity of GPR156 in the apo state. Furthermore, C-terminus in G-bound subunit of GPR156 plays a dual role in promoting G protein binding within G-bound subunit while preventing the G-free subunit from binding to additional G protein. Together, these results explain how GPR156 constitutive activity is maintained through dimerization and provide a mechanistic insight into the sustained role of GPR156 in maintaining auditory function.}, }
@article {pmid39637463, year = {2025}, author = {Wimmer, M and Pepicelli, A and Volmer, B and ElSayed, N and Cunningham, A and Thomas, BH and Müller-Putz, GR and Veas, EE}, title = {Counting on AR: EEG responses to incongruent information with real-world context.}, journal = {Computers in biology and medicine}, volume = {185}, number = {}, pages = {109483}, doi = {10.1016/j.compbiomed.2024.109483}, pmid = {39637463}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; Signal Processing, Computer-Assisted ; Brain/physiology ; Young Adult ; Support Vector Machine ; Evoked Potentials/physiology ; }, abstract = {Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions.}, }
@article {pmid39634507, year = {2024}, author = {Wei, M and Lin, X and Xu, K and Wu, Y and Wang, C and Wang, Z and Lei, K and Bao, K and Li, J and Li, L and Li, E and Lin, H}, title = {Inverse design of compact nonvolatile reconfigurable silicon photonic devices with phase-change materials.}, journal = {Nanophotonics (Berlin, Germany)}, volume = {13}, number = {12}, pages = {2183-2192}, pmid = {39634507}, issn = {2192-8614}, abstract = {In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability. Low-loss phase change materials (PCMs) exemplified by Sb2Se3 are a promising candidate for solving this problem benefiting from their high refractive index contrast. In this work, we first developed a robust inverse design method to realize reconfigurable silicon and phase-change materials hybrid photonic devices including mode converter and optical switch. The mode converter exhibits a broadband operation of >100 nm. The optical switch shows an extinction ratio of >25 dB and a multilevel switching of 41 (>5 bits) by simply changing the crystallinity of Sb2Se3. Here, we experimentally demonstrated a Sb2Se3/Si hybrid integrated optical switch for the first time, wherein routing can be switched by the phase transition of the whole Sb2Se3. Our work provides an effective solution for the design of photonic devices that is insensitive to fabrication errors, thereby paving the way for high integration density in future photonic chips.}, }
@article {pmid39632923, year = {2024}, author = {Karthiga, M and Suganya, E and Sountharrajan, S and Balusamy, B and Selvarajan, S}, title = {Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {30251}, pmid = {39632923}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Emotions/physiology ; *Brain-Computer Interfaces ; Heuristics ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Neural Networks, Computer ; }, abstract = {In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.}, }
@article {pmid39632897, year = {2024}, author = {Chen, Z and Tang, Y and Liu, X and Li, W and Hu, Y and Hu, B and Xu, T and Zhang, R and Xia, L and Zhang, JX and Xiao, Z and Chen, J and Feng, Z and Zhou, Y and He, Q and Qiu, J and Lei, X and Chen, H and Qin, S and Feng, T}, title = {Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10560}, pmid = {39632897}, issn = {2041-1723}, support = {32300907//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Connectome ; Male ; Female ; *Comorbidity ; Adult ; *Depression/genetics/epidemiology ; *Anxiety/genetics/epidemiology ; Young Adult ; Genetic Markers ; Adolescent ; Child ; Anxiety Disorders/genetics/epidemiology ; Brain/diagnostic imaging/metabolism ; Magnetic Resonance Imaging ; }, abstract = {Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.}, }
@article {pmid39630811, year = {2024}, author = {Grogan, M and Blum, KP and Wu, Y and Harston, JA and Miller, LE and Faisal, AA}, title = {Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.}, journal = {PLoS computational biology}, volume = {20}, number = {12}, pages = {e1012614}, pmid = {39630811}, issn = {1553-7358}, support = {/WT_/Wellcome Trust/United Kingdom ; F32 MH120893/MH/NIMH NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; }, mesh = {*Proprioception/physiology ; Animals ; *Models, Neurological ; *Somatosensory Cortex/physiology/anatomy &amp; histology ; Neurons/physiology ; Movement/physiology ; Computational Biology ; Macaca mulatta ; Brain-Computer Interfaces ; Humans ; }, abstract = {Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.}, }
@article {pmid39630448, year = {2024}, author = {Kelly, BC and Cova, TJ and Debbink, MP and Onega, T and Brewer, SC}, title = {Racial and Ethnic Disparities in Regulatory Air Quality Monitor Locations in the US.}, journal = {JAMA network open}, volume = {7}, number = {12}, pages = {e2449005}, pmid = {39630448}, issn = {2574-3805}, mesh = {Humans ; United States ; *Air Pollution/analysis/statistics &amp; numerical data ; Cross-Sectional Studies ; *Environmental Monitoring/methods ; *Air Pollutants/analysis ; Particulate Matter/analysis ; Ozone/analysis ; Ethnicity/statistics &amp; numerical data ; United States Environmental Protection Agency ; Racial Groups/statistics &amp; numerical data ; Nitrogen Dioxide/analysis ; Sulfur Dioxide/analysis ; Environmental Exposure/analysis/statistics &amp; numerical data ; }, abstract = {IMPORTANCE: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.<br><br>OBJECTIVE: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.<br><br>This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size. Bayesian mixed-effects models of the count of criteria pollutant monitors measuring an area were used, adjusting for population size and accounting for spatial autocorrelation. Data were analyzed from March to June 2024.<br><br>EXPOSURE: Census block group-level racial and ethnic composition.<br><br>MAIN OUTCOME AND MEASURES: Number of regulatory monitors measuring a census block group by criteria pollutant (particulate matter [PM], ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], lead [Pb], and carbon monoxide [CO]).<br><br>RESULTS: This analysis included 329&#x202f;725&#x202f;481 individuals living in 237&#x202f;631 block groups in the US (1&#x202f;936&#x202f;842 [0.6%] American Indian and Alaska Native, 18&#x202f;554&#x202f;697 [5.6%] Asian, 40&#x202f;196&#x202f;302 [12.2%] Black, 60&#x202f;806&#x202f;969 [18.4%] Hispanic, 555&#x202f;712 [0.2%] Native Hawaiian and Other Pacific Islander, 196&#x202f;010&#x202f;370 [59.4%] White, 1&#x202f;208&#x202f;267 [0.3%] some other race, and 10&#x202f;456&#x202f;322 [0.4%] 2 or more races). Adjusting for population size, monitoring disparities were identified for each criteria pollutant. Relative to the White non-Latino population, all groups were associated with fewer NO2, O3, Pb, and PM monitors. Disparities were consistently largest for Native Hawaiian and Other Pacific Islander populations, followed by American Indian and Alaska Native populations and those of 2 or more races. An increase in percentage of Native Hawaiian and Other Pacific Islander race was associated with fewer monitors for SO2 (adjusted odds ratio [aOR], 0.91; 95% BCI, 0.90-0.91), CO (aOR, 0.95; 95% BCI, 0.94-0.95), O3 (aOR, 0.95; 95% BCI, 0.94-0.95), NO2 (aOR, 0.97; 95% BCI, 0.91-0.94), and PM (aOR, 0.96; 95% BCI, 0.95-0.96). An increase in the percentage of those of Asian race was associated with slightly more SO2 (aOR, 1.04; 95% BCI, 1.03-1.04) monitors.<br><br>CONCLUSIONS AND RELEVANCE: This cross-sectional study of racial and ethnic disparities in the location of EPA regulatory monitors determined that data may not be equitably representative of air quality, particularly for areas with predominantly Native Hawaiian and Other Pacific Islander or American Indian or Alaska Native populations. Integration of multiple data sources may aid in filling monitoring gaps across race and ethnicity. Where possible, researchers should quantify uncertainty in exposure estimates.}, }
@article {pmid39628656, year = {2024}, author = {Pryss, R and Vom Brocke, J and Reichert, M and Rukzio, E and Schlee, W and Weber, B}, title = {Editorial: Application of neuroscience in information systems and software engineering.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1402603}, pmid = {39628656}, issn = {1662-4548}, }
@article {pmid39628388, year = {2024}, author = {Wang, X and Xu, M and Yang, H and Jiang, W and Jiang, J and Zou, D and Zhu, Z and Tao, C and Ni, S and Zhou, Z and Sun, L and Li, M and Nie, Y and Zhao, Y and He, F and Tao, TH and Wei, X}, title = {Ultraflexible Neural Electrodes Enabled Synchronized Long-Term Dopamine Detection and Wideband Chronic Recording Deep in Brain.}, journal = {ACS nano}, volume = {18}, number = {50}, pages = {34272-34287}, doi = {10.1021/acsnano.4c12429}, pmid = {39628388}, issn = {1936-086X}, mesh = {*Dopamine/analysis ; Animals ; *Graphite/chemistry ; *Brain/metabolism/physiology ; Rats ; Rats, Sprague-Dawley ; Electrodes ; Nanocomposites/chemistry ; Male ; Polystyrenes/chemistry ; Electrodes, Implanted ; Thiophenes ; }, abstract = {Ultraflexible neural electrodes have shown superior stability compared with rigid electrodes in long-term in vivo recordings, owing to their low mechanical mismatch with brain tissue. It is desirable to detect neurotransmitters as well as electrophysiological signals for months in brain science. This work proposes a stable electronic interface that can simultaneously detect neural electrical activity and dopamine concentration deep in the brain. This ultraflexible electrode is modified by a nanocomposite of reduced graphene oxide (rGO) and poly(3,4-ethylenedioxythiophene):poly(sodium 4-styrenesulfonate) (rGO/PEDOT:PSS), enhancing the electrical stability of the coating and increasing its specific surface area, thereby improving the sensitivity to dopamine response with 15 pA/μM. This electrode can detect dopamine fluctuations and can conduct long-term, stable recordings of local field potentials (LFPs), spiking activities, and amplitudes with high spatial and temporal resolution across multiple regions, especially in deep brain areas. The electrodes were implanted into the brains of rodent models to monitor the changes in neural and electrochemical signals across different brain regions during the administration of nomifensine. Ten minutes after drug injection, enhanced neuronal firing activity and increased LFP power were detected in the motor cortex and deeper cortical layers, accompanied by a gradual rise in dopamine levels with 192 ± 29 nM. The in vivo recording consistently demonstrates chronic high-quality neural signal monitoring with electrochemical signal stability for up to 6 weeks. These findings highlight the high quality and stability of our electrophysiological/electrochemical codetection neural electrodes, underscoring their tremendous potential for applications in neuroscience research and brain-machine interfaces.}, }
@article {pmid39627937, year = {2024}, author = {Gielas, AM}, title = {Wounds and Vulnerabilities. The Participation of Special Operations Forces in Experimental Brain-Computer Interface Research.}, journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees}, volume = {}, number = {}, pages = {1-22}, doi = {10.1017/S096318012400063X}, pmid = {39627937}, issn = {1469-2147}, abstract = {Brain-computer interfaces (BCIs) exemplify a dual-use neurotechnology with significant potential in both civilian and military contexts. While BCIs hold promise for treating neurological conditions such as spinal cord injuries and amyotrophic lateral sclerosis in the future, military decisionmakers in countries such as the United States and China also see their potential to enhance combat capabilities. Some predict that U.S. Special Operations Forces (SOF) will be early adopters of BCI enhancements. This article argues for a shift in focus: the U.S. Special Operations Command (SOCOM) should pursue translational research of medical BCIs for treating severely injured or ill SOF personnel. After two decades of continuous military engagement and on-going high-risk operations, SOF personnel face unique injury patterns, both physical and psychological, which BCI technology could help address. The article identifies six key medical applications of BCIs that could benefit wounded SOF members and discusses the ethical implications of involving SOF personnel in translational research related to these applications. Ultimately, the article challenges the traditional civilian-military divide in neurotechnology, arguing that by collaborating more closely with military stakeholders, scientists can not only help individuals with medical needs, including servicemembers, but also play a role in shaping the future military applications of BCI technology.}, }
@article {pmid39627867, year = {2024}, author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J}, title = {On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {211}, pmid = {39627867}, issn = {1743-0003}, support = {GR2019-12369207//Ministero della Salute/ ; RM123188F229EC72//Sapienza Universit&#xe0; di Roma/ ; }, mesh = {Humans ; *Robotics/instrumentation/methods ; Male ; Female ; *Feedback, Sensory/physiology ; Adult ; *Electroencephalography/methods ; Middle Aged ; *Eye-Tracking Technology ; Spinal Cord Injuries/rehabilitation ; Physical Therapists ; Exercise Therapy/methods/instrumentation ; Gait/physiology ; Gait Disorders, Neurologic/rehabilitation ; }, abstract = {BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.<br><br>METHODS: Ten individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.<br><br>RESULTS: We found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.<br><br>CONCLUSIONS: The type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.}, }
@article {pmid39622415, year = {2025}, author = {Zhu, M and Yang, Y and Niu, X and Peng, Y and Liu, R and Zhang, M and Han, Y and Wang, Z}, title = {Different responses of MVL neurons when pigeons attend to local versus global information during object classification.}, journal = {Behavioural brain research}, volume = {480}, number = {}, pages = {115363}, doi = {10.1016/j.bbr.2024.115363}, pmid = {39622415}, issn = {1872-7549}, mesh = {Animals ; *Columbidae/physiology ; *Neurons/physiology ; Pattern Recognition, Visual/physiology ; Attention/physiology ; Male ; Visual Perception/physiology ; Behavior, Animal/physiology ; Photic Stimulation/methods ; }, abstract = {Most prior studies have indicated that pigeons have a tendency to rely on local information for target categorization, yet there is a lack of electrophysiological evidence to support this claim. The mesopallium ventrolaterale (MVL) is believed to play a role in processing both local and global information during visual cognition. The difference between responses of MVL neurons when pigeons are focusing on local versus global information during visual object categorization remain unknown. In this study, pigeons were trained to categorize hierarchical stimuli that maintained consistency in local and global information. Subsequently, stimuli with different local and global components were presented to examine the pigeons' behavioral preferences. Not surprisingly, the behavioral findings revealed that pigeons predominantly attended to the local elements when performing categorization tasks. Moreover, MVL neurons exhibited significantly distinct responses when pigeons prioritized local versus global information. Specifically, most recording sites showed heightened gamma band power and increased nonlinear entropy values, indicating strong neural responses and rich information when pigeons concentrated on the local components of an object. Furthermore, neural population functional connectivity was weaker when the pigeons focused on local elements, suggesting that individual neurons operated more independently and effectively when focusing on local features. These findings offer electrophysiological evidence supporting the notion of pigeons displaying a behavioral preference for local information. The study provides valuable insight into the understanding of cognitive processes of pigeons when presented with complex objects, and further sheds light on the neural mechanisms underlying pigeons' behavioral preference for attending to local information.}, }
@article {pmid39622169, year = {2025}, author = {Wei, X and Narayan, J and Faisal, AA}, title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad9957}, pmid = {39622169}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Privacy ; Brain Waves/physiology ; }, abstract = {Objective. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural activity analysis and aiding paralysed patients via brain-computer interfaces. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach.Approach. We introduce the 'Sandwich' as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The 'Sandwich' framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning techniques, and individual classifiers (final layers) for specific brain tasks of each data set. This structure enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy.Main results. We evaluated the 'Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models likeShallow ConvNetandEEGInception, our 'Sandwich' implementations showed superior performance. The best-performing model, the Inception SanDwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%.Significance. The 'Sandwich' framework demonstrates advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy. In addition, through its diverse implementations with various backbone architectures and transfer learning approaches, the 'Sandwich' framework shows the potential as a model-agnostic meta-framework for decoding time series data like EEG, suggesting a direction towards large-scale brainwave decoding by combining deep transfer learning with privacy-preserving federated learning.}, }
@article {pmid39622162, year = {2025}, author = {Chen, C and Fang, H and Yang, Y and Zhou, Y}, title = {Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad9956}, pmid = {39622162}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Machine Learning ; Algorithms ; Brain-Computer Interfaces ; }, abstract = {Objective. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy.Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable electroencephalogram-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition.Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures.Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces.}, }
@article {pmid39621862, year = {2024}, author = {Xia, Y and He, M and Basang, S and Sha, L and Huang, Z and Jin, L and Duan, Y and Tang, Y and Li, H and Lai, W and Chen, L}, title = {Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.}, journal = {JMIR medical informatics}, volume = {12}, number = {}, pages = {e57727}, pmid = {39621862}, issn = {2291-9694}, mesh = {Humans ; *Electronic Health Records ; *Epilepsy/diagnosis/classification ; *Machine Learning ; Retrospective Studies ; *Natural Language Processing ; Female ; Male ; Adult ; Middle Aged ; China ; Adolescent ; Child ; Young Adult ; }, abstract = {BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.<br><br>OBJECTIVE: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.<br><br>METHODS: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.<br><br>RESULTS: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).<br><br>CONCLUSIONS: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.}, }
@article {pmid39621615, year = {2024}, author = {Yuan, X and Li, H and Guo, F}, title = {Temperature cues are integrated in a flexible circadian neuropeptidergic feedback circuit to remodel sleep-wake patterns in flies.}, journal = {PLoS biology}, volume = {22}, number = {12}, pages = {e3002918}, pmid = {39621615}, issn = {1545-7885}, mesh = {Animals ; *Sleep/physiology ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Connectome ; *Drosophila melanogaster/physiology ; *Neuropeptides/metabolism/genetics ; Neurons/physiology/metabolism ; Temperature ; Wakefulness/physiology ; Feedback, Physiological ; Brain/physiology/metabolism ; Drosophila/physiology ; Cues ; Signal Transduction ; }, abstract = {Organisms detect temperature signals through peripheral neurons, which relay them to central circadian networks to drive adaptive behaviors. Despite recent advances in Drosophila research, how circadian circuits integrate temperature cues with circadian signals to regulate sleep/wake patterns remains unclear. In this study, we used the FlyWire brain electron microscopy connectome to map neuronal connections, identifying lateral posterior neurons LPNs as key nodes for integrating temperature information into the circadian network. LPNs receive input from both circadian and temperature-sensing neurons, promoting sleep behavior. Through connectome analysis, genetic manipulation, and behavioral assays, we demonstrated that LPNs, downstream of thermo-sensitive anterior cells (ACs), suppress activity-promoting lateral dorsal neurons LNds via the AstC pathway, inducing sleep Disrupting LPN-LNd communication through either AstCR1 RNAi in LNds or in an AstCR1 mutant significantly impairs the heat-induced reduction in the evening activity peak. Conversely, optogenetic calcium imaging and behavioral assays revealed that cold-activated LNds subsequently stimulate LPNs through NPF-NPFR signaling, establishing a negative feedback loop. This feedback mechanism limits LNd activation to appropriate levels, thereby fine-tuning the evening peak increase at lower temperatures. In conclusion, our study constructed a comprehensive connectome centered on LPNs and identified a novel peptidergic circadian feedback circuit that coordinates temperature and circadian signals, offering new insights into the regulation of sleep patterns in Drosophila.}, }
@article {pmid39619679, year = {2024}, author = {Zhang, W and Tang, X and Wang, M}, title = {Attention model of EEG signals based on reinforcement learning.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1442398}, pmid = {39619679}, issn = {1662-5161}, abstract = {BACKGROUND: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.<br><br>METHODS: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.<br><br>RESULTS: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.<br><br>CONCLUSION: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.}, }
@article {pmid39619531, year = {2024}, author = {Patel, M and Gosai, J and Patel, P and Roy, M and Solanki, A}, title = {Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.}, journal = {ACS omega}, volume = {9}, number = {47}, pages = {46841-46850}, pmid = {39619531}, issn = {2470-1343}, abstract = {Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼10[3] s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.}, }
@article {pmid39615858, year = {2025}, author = {Xue, Z and Zhong, W and Cao, Y and Liu, S and An, X}, title = {Impact of different auditory environments on task performance and EEG activity.}, journal = {Brain research bulletin}, volume = {220}, number = {}, pages = {111142}, doi = {10.1016/j.brainresbull.2024.111142}, pmid = {39615858}, issn = {1873-2747}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Young Adult ; Adult ; *Acoustic Stimulation/methods ; *Brain/physiology ; *Psychomotor Performance/physiology ; *Auditory Perception/physiology ; Noise ; Music ; Attention/physiology ; Workload/psychology ; Environment ; Task Performance and Analysis ; }, abstract = {Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.}, }
@article {pmid39615554, year = {2025}, author = {Cai, Z and Gao, Y and Fang, F and Zhang, Y and Du, S}, title = {Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.}, journal = {Journal of neuroscience methods}, volume = {415}, number = {}, pages = {110332}, doi = {10.1016/j.jneumeth.2024.110332}, pmid = {39615554}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Transfer, Psychology/physiology ; *Electroencephalography/methods ; *Imagination/physiology ; *Algorithms ; Brain/physiology/diagnostic imaging ; Adult ; Machine Learning ; Male ; }, abstract = {In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.}, }
@article {pmid39612132, year = {2025}, author = {Yang, Y and Li, M and Wang, L}, title = {An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {63}, number = {4}, pages = {1059-1079}, pmid = {39612132}, issn = {1741-0444}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Machine Learning ; }, abstract = {Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.}, }
@article {pmid39609786, year = {2024}, author = {Sun, J and Chen, S and Wang, S and Guo, H and Wang, X}, title = {The relationship between work-family conflict, stress and depression among Chinese correctional officers: a mediation and network analysis study.}, journal = {BMC public health}, volume = {24}, number = {1}, pages = {3317}, pmid = {39609786}, issn = {1471-2458}, support = {2021ZD0200700//the 2030 Plan Technology and Innovation of China/ ; }, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology ; Correctional Facilities Personnel/psychology ; *Cross-Sectional Studies ; *Depression/epidemiology/psychology ; East Asian People/psychology ; Family/psychology ; Mediation Analysis ; Occupational Stress/psychology/epidemiology ; Police/psychology ; Prisons ; Social Network Analysis ; Stress, Psychological/epidemiology/psychology ; }, abstract = {BACKGROUND: Numerous studies have found that depression is prevalent among correctional officers (COs), which may be related to the work-family conflict (WFC) faced by this cohort. Role conflict theory posits that WFC emerges from the incompatibility between the demands of work and family roles, which induces stress and, in turn, results in emotional problems. Thus, this study seeks to investigate the association between WFC and depression, along with examining the mediating role of stress. Further network analysis is applied to identify the core and bridge symptoms within the network of WFC, stress, and depression, providing a basis for targeted interventions.<br><br>OBJECTIVE: This study aims to investigate the relationship between work-family conflict (WFC) and depressive symptoms among a larger sample of Chinese correctional officers (COs), exploring the potential mechanisms of stress in this population through network analysis.<br><br>METHODS: A cross-sectional study of 472 Chinese COs was conducted from October 2021 to January 2022. WFC, stress, and depressive symptoms were evaluated using the Work-Family Conflict Scale (WFCS) and the Depression Anxiety Stress Scale (DASS). Subsequently, correlation and regression analyses were conducted using SPSS 26.0, while mediation analysis was performed using Model 4 in PROCESS. By using the EBICglasso model, network analyses were utilized to estimate the network structure of WFC, stress and depression. Visualization and centrality measures were performed using the R package.<br><br>RESULTS: The results showed that (1) there was a significant positive correlation between WFC and stress and depression, as well as between stress and depression, (2) WFC and stress had a significant positive predictive effect on depression, (3) stress mediated the relationship between WFC and depression, with a total mediating effect of 0.262 (BootSE&#x2009;=&#x2009;0.031, BCI 95% = 0.278, 0.325), which accounted for 81.62% of the total effect, and (4) in the WFC, stress, and depression network model, strain-based work interference with family (SWF, (betweenness&#x2009;=&#x2009;2.24, closeness = -0.19, strength&#x2009;=&#x2009;1.40), difficult to relax (DR, betweenness&#x2009;=&#x2009;1.20, closeness&#x2009;=&#x2009;1.85, strength&#x2009;=&#x2009;1.06), and had nothing (HN, betweenness = -0.43, closeness&#x2009;=&#x2009;0.62, strength&#x2009;=&#x2009;0.73) were the core symptoms, and SWF, IT, and DH were the bridge symptoms, and (5) first-line COs had significantly higher levels of WFC, stress, and depression than non-first-line correctional officers.<br><br>CONCLUSION: Our findings elucidate the interrelationships between WFC, stress, and depression among COs. The study also enhances the understanding of the factors influencing WFC in this population and provides valuable guidance for the development of future interventions, offering practical clinical significance.}, }
@article {pmid39609406, year = {2024}, author = {Huang, W and Jin, N and Guo, J and Shen, C and Xu, C and Xi, K and Bonhomme, L and Quast, RB and Shen, DD and Qin, J and Liu, YR and Song, Y and Gao, Y and Margeat, E and Rondard, P and Pin, JP and Zhang, Y and Liu, J}, title = {Structural basis of orientated asymmetry in a mGlu heterodimer.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10345}, pmid = {39609406}, issn = {2041-1723}, support = {ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-10-INBS-04//Agence Nationale de la Recherche (French National Research Agency)/ ; }, mesh = {*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure ; *Cryoelectron Microscopy ; Humans ; *Protein Multimerization ; HEK293 Cells ; Allosteric Regulation ; Glutamic Acid/metabolism/chemistry ; Models, Molecular ; Fluorescence Resonance Energy Transfer ; Animals ; Protein Binding ; Binding Sites ; }, abstract = {The structural basis for the allosteric interactions within G protein-coupled receptors (GPCRs) heterodimers remains largely unknown. The metabotropic glutamate (mGlu) receptors are complex dimeric GPCRs important for the fine tuning of many synapses. Heterodimeric mGlu receptors with specific allosteric properties have been identified in the brain. Here we report four cryo-electron microscopy structures of mGlu2-4 heterodimer in different states: an inactive state bound to antagonists, two intermediate states bound to either mGlu2 or mGlu4 agonist only and an active state bound to both glutamate and a mGlu4 positive allosteric modulator (PAM) in complex with Gi protein. In addition to revealing a unique PAM binding pocket among mGlu receptors, our data bring important information for the asymmetric activation of mGlu heterodimers. First, we show that agonist binding to a single subunit in the extracellular domain is not sufficient to stabilize an active dimer conformation. Single-molecule FRET data show that the monoliganded mGlu2-4 can be found in both intermediate states and an active one. Second, we provide a detailed view of the asymmetric interface in seven-transmembrane (7TM) domains and identified key residues within the mGlu2 7TM that limits its activation leaving mGlu4 as the only subunit activating G proteins.}, }
@article {pmid39606007, year = {2024}, author = {Chang, C and Piao, Y and Zhang, M and Liu, Y and Du, M and Yang, M and Mei, T and Wu, C and Wang, Y and Chen, X and Zeng, GQ and Zhang, X}, title = {Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.}, journal = {Frontiers in psychiatry}, volume = {15}, number = {}, pages = {1441533}, pmid = {39606007}, issn = {1664-0640}, abstract = {BACKGROUND: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.<br><br>OBJECTIVE: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.<br><br>METHODS: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.<br><br>RESULTS: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.<br><br>CONCLUSION: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.}, }
@article {pmid39605556, year = {2024}, author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM}, title = {Speech motor cortex enables BCI cursor control and click.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39605556}, issn = {2692-8205}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; }, abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.}, }
@article {pmid39605372, year = {2024}, author = {Kumaresan, V and Hung, CY and Hermann, BP and Seshu, J}, title = {Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39605372}, issn = {2692-8205}, support = {G12 MD007591/MD/NIMHD NIH HHS/United States ; R21 AI149263/AI/NIAID NIH HHS/United States ; }, abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (BbLP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts.}, }
@article {pmid39603445, year = {2025}, author = {Kerezoudis, P and Jensen, MA and Huang, H and Ojemann, JG and Klassen, BT and Ince, NF and Hermes, D and Miller, KJ}, title = {Spatial and spectral changes in cortical surface potentials during pinching versusthumb and index finger flexion.}, journal = {Neuroscience letters}, volume = {845}, number = {}, pages = {138062}, doi = {10.1016/j.neulet.2024.138062}, pmid = {39603445}, issn = {1872-7972}, mesh = {Humans ; *Fingers/physiology ; Adult ; Male ; *Electrocorticography/methods ; Female ; *Motor Cortex/physiology ; *Movement/physiology ; Brain Mapping/methods ; Young Adult ; Epilepsy/physiopathology ; }, abstract = {Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.}, }
@article {pmid39600948, year = {2024}, author = {Adams, M and Cottrell, J}, title = {Development and characterization of an in vitro fluorescently tagged 3D bone-cartilage interface model.}, journal = {Frontiers in endocrinology}, volume = {15}, number = {}, pages = {1484912}, pmid = {39600948}, issn = {1664-2392}, mesh = {Animals ; Mice ; *Cell Differentiation ; *Osteoblasts/metabolism/cytology ; *Chondrocytes/metabolism/cytology ; *Osteoclasts/metabolism/cytology ; Bone and Bones/metabolism/cytology ; Cartilage/metabolism/cytology ; RAW 264.7 Cells ; Osteocytes/metabolism/cytology ; Cell Culture Techniques, Three Dimensional/methods ; Osteogenesis/physiology ; Cell Line ; }, abstract = {Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.}, }
@article {pmid39600168, year = {2024}, author = {Amandusson, &#xc5; and Nilsson, J and Pequito, S}, title = {[The role of EEG in tomorrow's medicine].}, journal = {Lakartidningen}, volume = {121}, number = {}, pages = {}, pmid = {39600168}, issn = {1652-7518}, mesh = {Humans ; Artificial Intelligence ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Wearable Electronic Devices ; }, abstract = {There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.}, }
@article {pmid39598903, year = {2024}, author = {Angulo Medina, AS and Aguilar Bonilla, MI and Rodríguez Giraldo, ID and Montenegro Palacios, JF and Cáceres Gutiérrez, DA and Liscano, Y}, title = {Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {22}, pages = {}, pmid = {39598903}, issn = {1424-8220}, support = {Convocatoria Interna No. 01-2024//Universidad Santiago de Cali/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Bibliometrics ; Artificial Intelligence ; Rehabilitation/methods ; Brain/physiology ; }, abstract = {EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.}, }
@article {pmid39597167, year = {2024}, author = {Li, W and Zhou, J and Sheng, W and Jia, Y and Xu, W and Zhang, T}, title = {Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection.}, journal = {Micromachines}, volume = {15}, number = {11}, pages = {}, pmid = {39597167}, issn = {2072-666X}, support = {12072151 and 12472153//National Natural Science Foundation of China/ ; 2019YFA0705400//National Key Research and Development Program of China/ ; MCAS-E-0124Y03//Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures/ ; }, abstract = {A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa[-1] within the low-pressure range of 0-390 Pa and 0.012 kPa[-1] within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.}, }
@article {pmid39597097, year = {2024}, author = {Ji, W and Su, H and Jin, S and Tian, Y and Li, G and Yang, Y and Li, J and Zhou, Z and Wei, X and Tao, TH and Qin, L and Ye, Y and Sun, L}, title = {A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.}, journal = {Micromachines}, volume = {15}, number = {11}, pages = {}, pmid = {39597097}, issn = {2072-666X}, support = {2022YFF0706500//National Key R &amp; D Program of China/ ; 2022ZD0209300//National Key R &amp; D Program of China/ ; 2021ZD0201600//National Key R &amp; D Program of China/ ; 2019YFA0905200//National Key R &amp; D Program of China/ ; 2021YFC2501500//National Key R &amp; D Program of China/ ; 2021YFF1200700//National Key R &amp; D Program of China/ ; 2022ZD0212300//National Key R &amp; D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21PJ1415100//Shanghai Pujiang Program/ ; 19PJ1410900//Shanghai Pujiang Program/ ; 21JM0010200//Science and Technology Commission Foundation of Shanghai/ ; 21142200300//Science and Technology Commission Foundation of Shanghai/ ; 22QA1410900//Shanghai Rising-Star Program/ ; 22YF1454700//Shanghai Sailing Program/ ; 20212ABC03W07//Jiangxi Province 03 Special Project and 5G Project/ ; 20201ZDE04013//Fund for Central Government in Guidance of Local Science and Technology Development/ ; 2021B0909060002//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; 2021B0909050004//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; }, abstract = {Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.}, }
@article {pmid39595855, year = {2024}, author = {Tubbs, A and Vazquez, EA}, title = {Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review.}, journal = {Brain sciences}, volume = {14}, number = {11}, pages = {}, pmid = {39595855}, issn = {2076-3425}, abstract = {In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact.}, }
@article {pmid39593731, year = {2024}, author = {Nuñez Ponasso, G and Wartman, WA and McSweeney, RC and Lai, P and Haueisen, J and Maess, B and Knösche, TR and Weise, K and Noetscher, GM and Raij, T and Makaroff, SN}, title = {Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {11}, pages = {}, pmid = {39593731}, issn = {2306-5354}, support = {R01 EB035484/EB/NIBIB NIH HHS/United States ; R01EB035484/EB/NIBIB NIH HHS/United States ; 2018 IZN 004//Free State of Thuringia/ ; R01MH130490/MH/NIMH NIH HHS/United States ; R01 MH130490/MH/NIMH NIH HHS/United States ; 01GQ2201//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 1R01NS126337/NS/NINDS NIH HHS/United States ; R01 NS126337/NS/NINDS NIH HHS/United States ; 01GQ2304A//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 2018 IZN 004//European Regional Development Fund/ ; }, abstract = {Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10-20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.}, }
@article {pmid39592732, year = {2025}, author = {Zheng, MM and Li, JY and Guo, HJ and Zhang, J and Wang, LS and Jiang, KF and Wu, HH and He, QJ and Ding, L and Yang, B}, title = {IMPDH inhibitors upregulate PD-L1 in cancer cells without impairing immune checkpoint inhibitor efficacy.}, journal = {Acta pharmacologica Sinica}, volume = {46}, number = {4}, pages = {1058-1067}, pmid = {39592732}, issn = {1745-7254}, mesh = {Humans ; *B7-H1 Antigen/metabolism/antagonists &amp; inhibitors ; *IMP Dehydrogenase/antagonists &amp; inhibitors/metabolism ; *Immune Checkpoint Inhibitors/pharmacology ; Animals ; *Up-Regulation/drug effects ; Cell Line, Tumor ; Enzyme Inhibitors/pharmacology ; Mice ; Mycophenolic Acid/pharmacology ; Mice, Inbred BALB C ; }, abstract = {Tumor cells are characterized by rapid proliferation. In order to provide purines for DNA and RNA synthesis, inosine 5'-monophosphate dehydrogenase (IMPDH), a key enzyme in the de novo guanosine biosynthesis, is highly expressed in tumor cells. In this study we investigated whether IMPDH was involved in cancer immunoregulation. We revealed that the IMPDH inhibitors AVN944, MPA or ribavirin concentration-dependently upregulated PD-L1 expression in non-small cell lung cancer cell line NCI-H292. This effect was reproduced in other non-small cell lung cancer cell lines H460, H1299 and HCC827, colon cancer cell lines HT29, RKO and HCT116, as well as kidney cancer cell line Huh7. In NCI-H292 cells, we clarified that IMPDH inhibitors increased CD274 mRNA levels by enhancing CD274 mRNA stability. IMPDH inhibitors improved the affinity of the ARE-binding protein HuR for CD274 mRNA, thereby stabilizing CD274 mRNA. Guanosine supplementation abolished the IMPDH inhibitor-induced increase in PD-L1 expression. In CT26 and EMT6 tumor models used for ICIs based studies, we showed that despite its immunosuppressive properties, the IMPDH inhibitor mycophenolate mofetil did not reduce the clinical response of checkpoint inhibitors, representing an important clinical observation given that this class of drugs is approved for use in multiple diseases. We conclude that PD-L1 induction contributes to the immunosuppressive effect of IMPDH inhibitors. Furthermore, the IMPDH inhibitor mycophenolate mofetil does not antagonize immune checkpoint blockade.}, }
@article {pmid39592434, year = {2024}, author = {Paban, V and Feraud, L and Weills, A and Duplan, F}, title = {Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.}, journal = {The European journal of neuroscience}, volume = {60}, number = {12}, pages = {7164-7182}, pmid = {39592434}, issn = {1460-9568}, support = {214535, UMR7077//Janssen Horizon/ ; }, mesh = {Humans ; *Neurofeedback/methods ; *Cognitive Dysfunction/therapy/physiopathology/rehabilitation ; Female ; Male ; Aged ; Middle Aged ; *Electroencephalography/methods ; }, abstract = {IMPACT STATEMENT: This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals.<br><br>ABSTRACT: In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.}, }
@article {pmid39591752, year = {2024}, author = {Sun, Y and Zhang, F and Li, Z and Liu, X and Zheng, D and Zhang, S and Fan, S and Wu, X}, title = {Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9778}, pmid = {39591752}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Scalp/physiology ; *Evoked Potentials, Visual/physiology ; Male ; Ear/physiology ; Adult ; Female ; Young Adult ; Photic Stimulation/methods ; }, abstract = {BACKGROUND: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.<br><br>OBJECTIVE: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.<br><br>APPROACH: To address this challenge, we focus on enhancing ear-EEG feature representations by training the model to learn features similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed to optimize SSVEP target classification in ear-EEG data. This framework combines signals from the scalp to obtain multi-layer distilled knowledge through the cooperation of mid-layer feature distillation and output layer response distillation.Mainresults.We improve the classification of the initial 1 s data and achieved a maximum classification accuracy of 81.1%. We evaluate the proposed MESD framework through single-session, cross-session, and cross-subject transfer decoding, comparing it with baseline methods. The results demonstrate that the proposed framework achieves the best classification results in all experiments.<br><br>SIGNIFICANCE: Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks such as auxiliary control and rehabilitation training in future endeavors.}, }
@article {pmid39591745, year = {2024}, author = {Smedemark-Margulies, N and Wang, Y and Koike-Akino, T and Liu, J and Parsons, K and Bicer, Y and Erdoğmuş, D}, title = {Improving subject transfer in EEG classification with divergence estimation.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9777}, pmid = {39591745}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; Algorithms ; Neural Networks, Computer ; Brain-Computer Interfaces ; }, abstract = {Objective. Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training.Approach. We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models.Main results. We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We first show the performance of each method across a wide range of hyperparameters, demonstrating that each method can be easily tuned to yield significant benefits over an unregularized model. We show that, using ideal hyperparameters for all methods, our first technique gives significantly better performance than the baseline regularization technique. We also show that, across hyperparameters, our second technique gives significantly more stable performance than the baseline. The proposed methods require only a small computational cost at training time that is equivalent to the cost of the baseline.Significance. The high variability in signal distribution between subjects means that typical approaches to EEG signal modeling often require time-intensive calibration for each user, and even re-calibration before every use. By improving the performance of population models in the most stringent case of zero-shot subject transfer, we may help reduce or eliminate the need for model calibration.}, }
@article {pmid39590012, year = {2024}, author = {AlQahtani, NJ and Al-Naib, I and Ateeq, IS and Althobaiti, M}, title = {Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.}, journal = {Biosensors}, volume = {14}, number = {11}, pages = {}, pmid = {39590012}, issn = {2079-6374}, support = {KSRG-2023-195//King Salman Center for Disability Research/ ; }, mesh = {Humans ; *Spectroscopy, Near-Infrared ; *Electromyography ; Male ; Adult ; Knee Prosthesis ; Knee/physiology ; Brain-Computer Interfaces ; Artificial Limbs ; }, abstract = {The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain-computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.}, }
@article {pmid39589888, year = {2024}, author = {Liu, J and Li, Z and Sun, M and Zhou, L and Wu, X and Lu, Y and Shao, Y and Liu, C and Huang, N and Hu, B and Wu, Z and You, C and Li, L and Wang, M and Tao, L and Di, Z and Sheng, X and Mei, Y and Song, E}, title = {Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {49}, pages = {e2412423121}, pmid = {39589888}, issn = {1091-6490}, support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057 62304044 12022209//the National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; LG-QS-202202-02//Lingang Laboratory/ ; }, mesh = {Animals ; Humans ; *Inflammation/therapy ; *Wound Healing ; Hydrogels/chemistry ; Wearable Electronic Devices ; Temperature ; Monitoring, Physiologic/instrumentation/methods ; Rats ; Biosensing Techniques/instrumentation/methods ; }, abstract = {Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.}, }
@article {pmid39589717, year = {2024}, author = {Chan, RW and Edelman, BJ and Tsang, SY and Gao, K and Yu, AC}, title = {Opportunities for System Neuroscience.}, journal = {Advances in neurobiology}, volume = {41}, number = {}, pages = {247-253}, pmid = {39589717}, issn = {2190-5215}, mesh = {Humans ; *Neurosciences ; *Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuroimaging ; Precision Medicine ; Nerve Net/diagnostic imaging/physiology ; }, abstract = {Systems neuroscience explores the intricate organization and dynamic function of neural circuits and networks within the brain. By elucidating how these complex networks integrate to execute mental operations, this field aims to deepen our understanding of the biological basis of cognition, behavior, and consciousness. In this chapter, we outline the promising future of systems neuroscience, highlighting the emerging opportunities afforded by powerful technological innovations and their applications. Cutting-edge tools such as awake functional MRI, ultrahigh field strength neuroimaging, functional ultrasound imaging, and optoacoustic techniques have revolutionized the field, enabling unprecedented observation and analysis of brain activity. The insights gleaned from these advanced methodologies have empowered the development of a suite of exciting applications across diverse domains. These include brain-machine interfaces (BMIs) for neural prosthetics, cognitive enhancement therapies, personalized mental health interventions, and precision medicine approaches. As our comprehension of neural systems continues to grow, it is envisioned that these and related applications will become increasingly refined and impactful in improving human health and well-being.}, }
@article {pmid39588722, year = {2025}, author = {Xiang, Z and Yang, L and Yu, B and Zeng, Q and Huang, T and Shi, S and Yu, H and Zhang, Y and Wu, J and Zhu, M}, title = {Recent advances in polymer-based thin-film electrodes for ECoG applications.}, journal = {Journal of materials chemistry. B}, volume = {13}, number = {2}, pages = {454-471}, doi = {10.1039/d4tb02090a}, pmid = {39588722}, issn = {2050-7518}, mesh = {*Polymers/chemistry ; *Electrodes ; Humans ; *Electrocorticography/instrumentation ; Brain-Computer Interfaces ; Animals ; }, abstract = {Electrocorticography (ECoG) has garnered widespread attention owing to its superior signal resolution compared to conventional electroencephalogram (EEG). While ECoG signal acquisition entails invasiveness, the invasive rigid electrode used inevitably inflicts damage on brain tissue. Polymer electrodes that combine conductivity and transparency have garnered great interest because they not only facilitate high-quality signal acquisition but also provide additional insights while preserving the health of the brain, positioning them as the future frontier in the brain-computer interface (BCI). This review summarizes the multifaceted functions of polymers in ECoG thin-film electrodes for the BCI. We present the abilities of sensitive and structural polymers focusing on impedance reduction, signal quality improvement, good flexibility, and transparency. Typically, two sensitive polymers and four structural polymers are analyzed in detail in terms of ECoG electrode properties. Moreover, the underlying mechanism of polymer-based electrodes in signal quality enhancement is revealed. Finally, the remaining challenges and perspectives are discussed.}, }
@article {pmid39588687, year = {2025}, author = {Wang, J and Jiang, Y and Xiong, T and Lu, J and He, X and Yu, P and Mao, L}, title = {Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {64}, number = {7}, pages = {e202418949}, doi = {10.1002/anie.202418949}, pmid = {39588687}, issn = {1521-3773}, support = {Z230022//Natural Science Foundation of Beijing/ ; 22134002//the National Natural Science Foundation of China/ ; 22174146, 22474011//the National Natural Science Foundation of China/ ; }, mesh = {*Transistors, Electronic ; Nanotechnology/instrumentation ; Metal-Organic Frameworks/chemistry ; }, abstract = {Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.}, }
@article {pmid39586499, year = {2025}, author = {Thenmozhi, T and Helen, R and Mythili, S}, title = {Classification of motor imagery EEG with ensemble RNCA model.}, journal = {Behavioural brain research}, volume = {479}, number = {}, pages = {115345}, doi = {10.1016/j.bbr.2024.115345}, pmid = {39586499}, issn = {1872-7549}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Bayes Theorem ; Brain/physiology ; Motor Activity/physiology ; Movement/physiology ; Adult ; }, abstract = {Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22 % and 91.62 % for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75 %. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.}, }
@article {pmid39586422, year = {2025}, author = {Mao, W and Shen, X and Bai, X and Wang, A}, title = {Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.}, journal = {Neuroscience}, volume = {564}, number = {}, pages = {214-225}, doi = {10.1016/j.neuroscience.2024.11.044}, pmid = {39586422}, issn = {1873-7544}, mesh = {Humans ; *Empathy/physiology ; Male ; *Electroencephalography/methods ; Female ; Young Adult ; *Machine Learning ; *Decision Making/physiology ; Adult ; Brain/physiology ; Evoked Potentials/physiology ; Adolescent ; Emotions/physiology ; }, abstract = {Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait empathy influence prosocial behavior. Addressing this knowledge gap is important to understand empathy-driven prosocial behavior. We employed an EEG experiment combined with interpretable machine learning methods to probe these questions. We found that empathic concern (EC) played the most pivotal role in donation decision. Behaviorally, EC negatively moderates the effect of perceived closeness and deservedness of charity projects on the willingness to donate. The machine learning results indicate that EC significantly predicts late positive potential (LPP) and beta-band activity during donation information processing. Further regression analysis results indicate that EC, rather than other dimensions of trait empathy, can positively predict LPP amplitude and negatively predict beta-band activity. These results indicated that participants with higher EC scores may experience heightened emotional arousal and the vicarious experience of others' emotions while processing donation information. Our work adds weight to understanding the relationship between trait empathy and prosocial behavior and provides electrophysiological evidence.}, }
@article {pmid39586421, year = {2025}, author = {Hong, T and Zhou, H and Xi, W and Li, X and Du, Y and Liu, J and Geng, F and Hu, Y}, title = {Acting with awareness is positively correlated with dorsal anterior cingulate cortex glutamate concentration but both are impaired in Internet gaming disorder.}, journal = {Neuroscience}, volume = {564}, number = {}, pages = {226-235}, doi = {10.1016/j.neuroscience.2024.11.054}, pmid = {39586421}, issn = {1873-7544}, mesh = {Humans ; *Gyrus Cinguli/metabolism/diagnostic imaging ; *Glutamic Acid/metabolism ; Male ; *Internet Addiction Disorder/metabolism/physiopathology ; Young Adult ; Adult ; Female ; *Awareness/physiology ; Video Games ; Magnetic Resonance Spectroscopy ; Proton Magnetic Resonance Spectroscopy ; Adolescent ; }, abstract = {Internet gaming disorder (IGD) is increasingly recognized as a public concern for its adverse impacts on cognition and mental health. In IGD, the transition from goal-directed actions to habitual and eventually compulsive behaviors is accompanied by altered neural response within the dorsal anterior cingulate cortex (dACC), a critical region involved in conscious actions. However, the neurochemical profile of the dACC in IGD and its relationship with behavioral awareness remain poorly understood. In this study, [1]H-magnetic resonance spectroscopy was employed to quantify dACC glutamate concentration and examine its association with the capacity for 'acting with awareness' among 21 participants with IGD and 19 recreational game users. Results indicated that dACC glutamate levels and behavioral awareness were significantly lower in the IGD group compared to recreational game users. Moreover, a significant positive correlation between awareness and dACC glutamate concentration emerged in the recreational game users' group, a relationship attenuated in those with IGD. In an independent cohort of 107 participants, the positive association between awareness and dACC glutamate concentration was replicated. These findings suggest that reduced dACC glutamate in IGD may underlie diminished awareness of maladaptive habitual behaviors. Enhancing dACC neural excitability through neuromodulation or mindfulness training could represent a potential intervention to restore behavioral awareness.}, }
@article {pmid39586380, year = {2025}, author = {Higashi, H}, title = {Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.}, journal = {Journal of neuroscience methods}, volume = {414}, number = {}, pages = {110323}, doi = {10.1016/j.jneumeth.2024.110323}, pmid = {39586380}, issn = {1872-678X}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain/physiology ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain-computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.}, }
@article {pmid39583262, year = {2024}, author = {Abo Alzahab, N and Iorio, AD and Apollonio, L and Alshalak, M and Gravina, A and Antognoli, L and Baldi, M and Scalise, L and Alchalabi, B}, title = {Auditory evoked potential electroencephalography-biometric dataset.}, journal = {Data in brief}, volume = {57}, number = {}, pages = {111065}, pmid = {39583262}, issn = {2352-3409}, abstract = {This work aims to assess the use of electroencephalographic (EEG) signals as a means of biometric authentication. More than 240 recordings, each lasting 2 min, were gathered from 20 subjects involved in the data collection. Data include the results of experiments performed both in a resting state and in the presence of auditory stimuli. The resting-state EEG signals were acquired with both open and closed eyes. The auditory stimuli EEG signals consist of six experiments divided into two scenarios. The first scenario considers in-ear stimuli, while the second scenario considers bone-conducting stimuli. For each of the two scenarios, experiments include a native language song, a non-native language song and some neutral music. This data could be used to develop biometric systems for authentication or identification. Additionally, they could be used to study the effect of auditory stimuli such as music on EEG activity and to compare it with the resting state condition.}, }
@article {pmid39582417, year = {2025}, author = {Wang, X and Zhu, K and Wu, W and Zhou, D and Lu, H and Du, J and Cai, L and Yan, X and Li, W and Qian, X and Wang, X and Ma, C and Hu, Y and Tian, C and Sun, B and Fang, Z and Wu, J and Jiang, P and Liu, J and Liu, C and Fan, J and Cui, H and Shen, Y and Duan, S and Bao, A and Yang, Y and Qiu, W and Zhang, J}, title = {Prevalence of mixed neuropathologies in age-related neurodegenerative diseases: A community-based autopsy study in China.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {21}, number = {1}, pages = {e14369}, pmid = {39582417}, issn = {1552-5279}, support = {2021ZD0201100//STI2030-Major Project/ ; 2024C03098//Key R&amp;D Program of Zhejiang province/ ; 2024SSYS0018//Key R&amp;D Program of Zhejiang province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; 2021-I2M-1-025//CAMS Innovation Fund for Medical Sciences/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 81971184//National Natural Science Foundation of China/ ; }, mesh = {Humans ; China/epidemiology ; Prevalence ; Male ; Female ; Aged ; *Neurodegenerative Diseases/pathology/epidemiology ; *Autopsy ; Aged, 80 and over ; *Brain/pathology ; Middle Aged ; Aging/pathology ; Tauopathies/pathology/epidemiology ; Lewy Body Disease/pathology/epidemiology ; Cerebrovascular Disorders/pathology/epidemiology ; Alzheimer Disease/pathology/epidemiology ; }, abstract = {INTRODUCTION: Despite extensive studies on mixed neuropathologies, data from China are limited. This study aims to fill this gap by analyzing brain samples from Chinese brain banks.<br><br>METHODS: A total of 1142 brains from six Chinese brain banks were examined using standardized methods. Independent pathologists conducted evaluations with stringent quality control. Prevalence and correlations of neurological disorders were analyzed.<br><br>RESULTS: Significant proportions of brains displayed primary age-related tauopathy (PART, 35%), limbic-predominant age-related TDP-43 encephalopathy (LATE, 46%), and aging-related tau astrogliopathy (ARTAG, 12%). Alzheimer's disease neuropathological change (ADNC, 48%), Lewy body disease (LBD, 13%), and cerebrovascular disease (CVD, 63%) were also prevalent, often co-occurring with regional variations. CVD emerged as the potential most early contributor to neuropathological changes.<br><br>DISCUSSION: This analysis highlights the prevalence of PART, LATE, ARTAG, ADNC, LBD, and CVD, with regional differences. The findings suggest CVD may be the earliest contributing factor, potentially preceding other neuropathologies. Highlights The prevalence of primary age-related tauopathy (PART), limbic-predominant age-related TDP-43 encephalopathy (LATE), aging-related tau astrogliopathy (ARTAG), Alzheimer's disease neuropathologic change, Lewy body disease, and cerebrovascular disease (CVD) in China, increasing with age, is comparable to other countries. Significant regional differences in the prevalences of diseases are noted. CVD develops prior to any other disorders, including PART, LATE, and ARTAG.}, }
@article {pmid39581346, year = {2025}, author = {Brands, R and Bartsch, J and Thommes, M}, title = {Complemental hard modeling in Raman spectroscopy: A case study on titanium dioxide-free coating in-line monitoring.}, journal = {Journal of pharmaceutical sciences}, volume = {114}, number = {1}, pages = {577-585}, doi = {10.1016/j.xphs.2024.10.044}, pmid = {39581346}, issn = {1520-6017}, mesh = {*Titanium/chemistry ; *Spectrum Analysis, Raman/methods ; Least-Squares Analysis ; Calcium Carbonate/chemistry ; Tablets/chemistry ; Tablets, Enteric-Coated/chemistry ; }, abstract = {Tablets are coated for taste or odor modification, for modified release profiles or as a protective layer to increase the stability. Here, titanium dioxide is frequently added as a coating component due to its opaque properties. Furthermore, its Raman activity makes it an integral part of in-line monitoring models. However, due to the carcinogenic potential of titanium dioxide, calcium carbonate is utilized as a substitute, exhibiting similar opaque properties. Calcium carbonate tends to exhibit overlapped peaks with carbon hydrates in the Raman spectrum. Consequently, new models based on e.g. hard modeling are required instead of peak integration. In this study, tablets were coated with a coating including calcium carbonate. Partial Least Squares Regression (PLS) and Complemental Hard Modeling (CHM) were examined as feasible in-line monitoring approaches. Furthermore, two different measurement positions in the coater were compared, orthogonal and tangential with respect to the moving tablet bed. Cross-validation exhibited improved CHM performance with reduced RMSECV values of about 5 %. The prediction of the coating mass growth occurred comparable with RMSEP values in a similar range of 2-5 %. Despite this, the CHM´s achieved improved performance with reduced training data quantity and quality. The different measurement positions indicated no process-relevant differences.}, }
@article {pmid39581146, year = {2024}, author = {Anwar, F and Zhang, K and Sun, C and Pang, M and Zhou, W and Li, H and He, R and Liu, X and Ming, D}, title = {Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research.}, journal = {Biomedicine &amp; pharmacotherapy = Biomedecine &amp; pharmacotherapie}, volume = {181}, number = {}, pages = {117702}, doi = {10.1016/j.biopha.2024.117702}, pmid = {39581146}, issn = {1950-6007}, mesh = {Animals ; Humans ; *Genetic Therapy/methods ; *Hydrocephalus/genetics/physiopathology/therapy ; }, abstract = {Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus.}, }
@article {pmid39577701, year = {2025}, author = {Xia, G and Wang, L and Xiong, S and Deng, J}, title = {Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis.}, journal = {Journal of neuroscience methods}, volume = {414}, number = {}, pages = {110325}, doi = {10.1016/j.jneumeth.2024.110325}, pmid = {39577701}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Adult ; Male ; Algorithms ; Female ; Young Adult ; }, abstract = {BACKGROUND: In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.<br><br>NEW METHOD: With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.<br><br>RESULTS: The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7&#x202f;s, the average recognition accuracy of the subjects increases by 10.71&#x202f;% on the Benchmark dataset and by 6.98&#x202f;% on the BETA dataset, respectively.<br><br>Our extended method outperforms the state-of-the-art methods by at least 3&#x202f;%, and it effectively suppresses local noise and maintains excellent scalability.<br><br>The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.}, }
@article {pmid39577098, year = {2025}, author = {Oxley, TJ}, title = {A 10-year journey towards clinical translation of an implantable endovascular BCI a keynote lecture given at the BCI society meeting in Brussels.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad9633}, pmid = {39577098}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrocorticography/methods/instrumentation ; Electrodes, Implanted/trends ; Electroencephalography/methods/trends ; *Endovascular Procedures/methods/instrumentation/trends ; Translational Research, Biomedical/methods/trends ; }, abstract = {In the rapidly evolving field of brain-computer interfaces (BCIs), a novel modality for recording electrical brain signals has quietly emerged over the past decade. The technology is endovascular electrocorticography (ECoG), an innovation that stands alongside well-established methods such as electroencephalography, traditional ECoG, and single/multi-unit activity recording. This system was inspired by advancements in interventional cardiology, particularly the integration of electronics into various medical interventions. The breakthrough led to the development of the Stentrode system, which employs stent-mounted electrodes to record electrical brain activity for applications in a motor neuroprosthesis. This perspective explores four key areas in our quest to bring the Stentrode BCI to market: the critical patient need for autonomy driving our efforts, the hurdles and achievements in assessing BCI performance, the compelling advantages of our unique endovascular approach, and the essential steps for clinical translation and product commercialization.}, }
@article {pmid39576681, year = {2024}, author = {Liu, D and Wei, Y}, title = {CVR-BBI: an open-source VR platform for multi-user collaborative brain to brain interfaces.}, journal = {Bioinformatics (Oxford, England)}, volume = {40}, number = {12}, pages = {}, pmid = {39576681}, issn = {1367-4811}, support = {12101570//National Natural Science Foundation of China/ ; 2024C01142//Key Research and Development Program of Zhejiang Province/ ; }, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Brain/physiology ; Virtual Reality ; Software ; Male ; Adult ; User-Computer Interface ; Female ; }, abstract = {SUMMARY: As brain imaging and neurofeedback technologies advance, the brain-to-brain interface (BBI) has emerged as an innovative field, enabling in-depth exploration of cross-brain information exchange and enhancing our understanding of collaborative intelligence. However, no open-source virtual reality (VR) platform currently supports the rapid and efficient configuration of multi-user, collaborative BBIs. To address this gap, we introduce the Collaborative Virtual Reality Brain-to-Brain Interface (CVR-BBI), an open-source platform consisting of a client and server. The CVR-BBI client enables users to participate in collaborative experiments, collect electroencephalogram (EEG) data, and manage interactive multisensory stimuli within the VR environment. Meanwhile, the CVR-BBI server manages multi-user collaboration paradigms, and performs real-time analysis of the EEG data. We evaluated the CVR-BBI platform using the SSVEP paradigm and observed that collaborative decoding outperformed individual decoding, validating the platform's effectiveness in collaborative settings. The CVR-BBI offers a pioneering platform that facilitates the development of innovative BBI applications within collaborative VR environments, thereby enhancing the understanding of brain collaboration and cognition.<br><br>CVR-BBI is released as an open-source platform, with its source code being available at https://github.com/DILIU1/CVR-BBI.}, }
@article {pmid39576281, year = {2025}, author = {Farina, D and Merletti, R and Enoka, RM}, title = {The extraction of neural strategies from the surface EMG: 2004-2024.}, journal = {Journal of applied physiology (Bethesda, Md. : 1985)}, volume = {138}, number = {1}, pages = {121-135}, doi = {10.1152/japplphysiol.00453.2024}, pmid = {39576281}, issn = {1522-1601}, support = {810346//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; RG-2206-39688//National Multiple Sclerosis Society/ ; EP/T020970/1//UKRI | Engineering and Physical Sciences Research Council (EPSRC)/ ; }, mesh = {*Electromyography/methods ; Humans ; *Muscle, Skeletal/physiology ; Muscle Contraction/physiology ; Motor Neurons/physiology ; Algorithms ; Animals ; Movement/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {This review follows two previous papers [Farina et al. Appl Physiol (1985) 96: 1486-1495, 2004; Farina et al. J Appl Physiol (1985) 117: 1215-1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.}, }
@article {pmid39572612, year = {2024}, author = {Lee, AH and Lee, J and Leung, V and Larson, L and Nurmikko, A}, title = {Patterned electrical brain stimulation by a wireless network of implantable microdevices.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10093}, pmid = {39572612}, issn = {2041-1723}, support = {S10 OD025181/OD/NIH HHS/United States ; 232600//National Science Foundation (NSF)/ ; }, mesh = {Animals ; *Wireless Technology/instrumentation ; *Electric Stimulation/instrumentation/methods ; Rats ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Brain/physiology ; Male ; Motor Cortex/physiology ; Behavior, Animal/physiology ; }, abstract = {Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. Here, we introduce a wireless approach to multipoint patterned electrical microstimulation by a spatially distributed epicortically implanted network of silicon microchips to target specific areas of the cortex. Each sub-millimeter-sized microchip harvests energy from an external radio-frequency source and converts this into biphasic current injected focally into tissue by a pair of integrated microwires. The amplitude, period, and repetition rate of injected current from each chip are controlled across the implant network by implementing a pre-scheduled, collision-free bitmap wireless communication protocol featuring sub-millisecond latency. As a proof-of-concept technology demonstration, a network of 30 wireless stimulators was chronically implanted into motor and sensory areas of the cortex in a freely moving rat for three months. We explored the effects of patterned intracortical electrical stimulation on trained animal behavior at average RF powers well below regulatory safety limits.}, }
@article {pmid39572577, year = {2024}, author = {Zhang, Z and Ding, X and Bao, Y and Zhao, Y and Liang, X and Qin, B and Liu, T}, title = {Chisco: An EEG-based BCI dataset for decoding of imagined speech.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1265}, pmid = {39572577}, issn = {2052-4463}, support = {U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; *Imagination ; Adult ; Language ; Brain/physiology ; Young Adult ; }, abstract = {The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to "mind reading". However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject's EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.}, }
@article {pmid39571645, year = {2024}, author = {Ma, J and Li, Z and Zheng, Q and Li, S and Zong, R and Qin, Z and Wan, L and Zhao, Z and Mao, Z and Zhang, Y and Yu, X and Bai, H and Zhang, J}, title = {Investigating unilateral and bilateral motor imagery control using electrocorticography and fMRI in awake craniotomy.}, journal = {NeuroImage}, volume = {303}, number = {}, pages = {120949}, doi = {10.1016/j.neuroimage.2024.120949}, pmid = {39571645}, issn = {1095-9572}, mesh = {Humans ; *Electrocorticography/methods ; *Magnetic Resonance Imaging/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Craniotomy/methods ; Middle Aged ; Wakefulness/physiology ; Motor Cortex/physiology/diagnostic imaging ; Young Adult ; Brain-Computer Interfaces ; Brain Mapping/methods ; Movement/physiology ; }, abstract = {BACKGROUND: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.<br><br>OBJECTIVE: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs.<br><br>METHODS: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions.<br><br>RESULTS: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination.<br><br>CONCLUSION: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.}, }
@article {pmid39571386, year = {2025}, author = {Li, Y and Chen, B and Yoshimura, N and Koike, Y and Yamashita, O}, title = {Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106899}, doi = {10.1016/j.neunet.2024.106899}, pmid = {39571386}, issn = {1879-2782}, mesh = {*Bayes Theorem ; Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; *Algorithms ; Machine Learning ; Muscle, Skeletal/physiology ; }, abstract = {Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms. The goal of this paper is to propose a novel robust implementation of sparse Bayesian learning so that robustness and sparseness can be realized simultaneously. Motivated by the exceptional robustness of maximum correntropy criterion (MCC), we proposed integrating MCC to the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC, and then leveraged it for the likelihood function. Meanwhile, we utilized the automatic relevance determination technique as the sparse prior distribution. To fully evaluate the proposed method, a synthetic example and a real-world muscle activity reconstruction task with two different brain modalities were leveraged. Experimental results showed, our proposed sparse Bayesian correntropy learning framework significantly improves the robustness for the noisy regression tasks. Our proposed algorithm could realize higher correlation coefficients and lower root mean squared errors for the real-world muscle activity reconstruction scenario. Sparse Bayesian correntropy learning provides a powerful approach for brain activity decoding which will promote the development of brain-computer interface technology.}, }
@article {pmid39570849, year = {2024}, author = {Liang, HJ and Li, LL and Cao, GZ}, title = {FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0309706}, pmid = {39570849}, issn = {1932-6203}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagination/physiology ; }, abstract = {Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model.}, }
@article {pmid39570847, year = {2024}, author = {Assiri, FY and Ragab, M}, title = {Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0313261}, pmid = {39570847}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Humans ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Support Vector Machine ; Algorithms ; }, abstract = {Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people with a breakdown or motor damage, delivering a bridge between the brain's intentions and exterior actions. Employing electroencephalography (EEG) or aggressive neural recordings, machine learning (ML) methods are used to interpret patterns of brain action linked with motor image tasks. These models frequently depend upon models like support vector machine (SVM) or deep learning (DL) to distinguish among dissimilar MI classes, such as visualizing left or right limb actions. This procedure allows individuals, particularly those with motor disabilities, to utilize their opinions to command exterior devices like robotic limbs or computer borders. This article presents a Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning (BHHSHO-DL) technique based on Motor Imagery Classification for BCI. The BHHSHO-DL technique mainly exploits the hyperparameter-tuned DL approach for MI identification for BCI. Initially, the BHHSHO-DL technique performs data preprocessing utilizing the wavelet packet decomposition (WPD) model. Besides, the enhanced densely connected networks (DenseNet) model extracts the preprocessed data's complex and hierarchical feature patterns. Meanwhile, the BHHSHO technique-based hyperparameter tuning process is accomplished to elect optimal parameter values of the enhanced DenseNet model. Finally, the classification procedure is implemented by utilizing the convolutional autoencoder (CAE) model. The simulation value of the BHHSHO-DL methodology is performed on a benchmark dataset. The performance validation of the BHHSHO-DL methodology portrayed a superior accuracy value of 98.15% and 92.23% over other techniques under BCIC-III and BCIC-IV datasets.}, }
@article {pmid39569894, year = {2024}, author = {Brannigan, JFM and Liyanage, K and Horsfall, HL and Bashford, L and Muirhead, W and Fry, A}, title = {Brain-computer interfaces patient preferences: a systematic review.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad94a6}, pmid = {39569894}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Patient Preference ; Adult ; Middle Aged ; Spinal Cord Injuries/rehabilitation/psychology/physiopathology ; Amyotrophic Lateral Sclerosis/psychology/rehabilitation/physiopathology ; }, abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to restore motor capabilities and functional independence in individuals with motor impairments. Despite accelerating advances in the performance of implanted devices, few studies have identified patient preferences underlying device design, and each study typically captures a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large cohort across multiple aetiologies.Approach. We performed a systematic review of all published studies reporting patient preferences for BCI devices, including both qualitative and quantitative data. We searched MEDLINE, Embase, and CINAHL from inception to 18 April 2023. Two reviewers independently screened articles and extracted data on demographic information, device use, invasiveness preference, device design, and functional preferences.Main results. From 1316 articles identified, 28 studies met inclusion criteria, capturing preferences from 1701 patients (mean age 42.1-64.3 years). The most represented conditions were amyotrophic lateral sclerosis (n= 15 studies, 53.6%) and spinal cord injury (n= 13 studies 46.4%). Individuals with motor impairments prioritised device accuracy over other design characteristics. In four studies where patients ranked performance characteristics, accuracy was ranked first each time. We found that the speed and accuracy of BCI systems in recent publications exceeds reported patient preferences, however this performance has been achieved with a level of training and setup burden that would not be tolerated by most patients. Preferences varied by disease aetiology and severity; amyotrophic lateral sclerosis patients typically prioritised communication functions, whereas spinal cord injury patients emphasised limb control and sphincteric functions.Significance.Our findings highlight that despite advances in BCI performance exceeding patient expectations, there remains a need to reduce training and setup burdens to enhance usability. Moreover, patient preferences differ across conditions and impairment severities, underscoring the importance of personalised BCI configurations and tailored training regimens to meet individual needs.}, }
@article {pmid39569892, year = {2024}, author = {Estiveira, J and Soares, E and Pires, G and Nunes, UJ and Sousa, T and Ribeiro, S and Castelo-Branco, M}, title = {SSVEP modulation via non-volitional neurofeedback: anin silicoproof of concept.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad94a5}, pmid = {39569892}, issn = {1741-2552}, mesh = {Humans ; *Neurofeedback/methods ; *Evoked Potentials, Visual/physiology ; *Visual Cortex/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Computer Simulation ; Brain-Computer Interfaces ; Young Adult ; Volition/physiology ; }, abstract = {Objective.Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex.Approach.Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100 ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex.Main results. Response models were obtained by analyzing, EEG data (n= 8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the steady-state visual evoked potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controller's linear, time-invariant models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability.Significance. In silicoandin vivodata matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits.}, }
@article {pmid39569866, year = {2024}, author = {Thomson, CJ and Tully, TN and Stone, ES and Morrell, CB and Scheme, EJ and Warren, DJ and Hutchinson, DT and Clark, GA and George, JA}, title = {Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, pmid = {39569866}, issn = {1741-2552}, support = {DP5 OD029571/OD/NIH HHS/United States ; }, mesh = {Humans ; Calibration ; *Electromyography/methods ; Male ; Neural Networks, Computer ; Neural Prostheses ; Amputees/rehabilitation ; Algorithms ; Artificial Limbs ; Female ; Middle Aged ; Adult ; Machine Learning ; }, abstract = {Objective.Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control.Approach.Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings.Main results.Dataset aggregation reduced the root-mean-squared error (RMSE) of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets.Significance.Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.}, }
@article {pmid39567538, year = {2024}, author = {Forenzo, D and Zhu, H and He, B}, title = {A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1256}, pmid = {39567538}, issn = {2052-4463}, support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; NS127849, NS096761, NS131069, NS124564//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS124564/NS/NINDS NIH HHS/United States ; AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; EB029354//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Deep Learning ; Algorithms ; }, abstract = {This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.}, }
@article {pmid39567330, year = {2025}, author = {Pu, Y and Francks, C and Kong, XZ}, title = {Global brain asymmetry.}, journal = {Trends in cognitive sciences}, volume = {29}, number = {2}, pages = {114-117}, doi = {10.1016/j.tics.2024.10.008}, pmid = {39567330}, issn = {1879-307X}, mesh = {Humans ; *Brain/physiology ; *Functional Laterality/physiology ; }, abstract = {Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain.}, }
@article {pmid39567230, year = {2024}, author = {Walters, GI and Foley, H and Huntley, CC and Naveed, A and Nettleton, K and Reilly, C and Thomas, M and Walker, C and Wheeler, K}, title = {Could a behaviour change intervention be used to address under-recognition of work-related asthma in primary care? A systematic review.}, journal = {BJGP open}, volume = {}, number = {}, pages = {}, doi = {10.3399/BJGPO.2024.0094}, pmid = {39567230}, issn = {2398-3795}, abstract = {BACKGROUND: Work-related asthma (WRA) is prevalent yet under-recognized in UK primary care.<br><br>AIM: We aimed to identify behaviour change interventions (BCI) intended for use in primary care to identify WRA, or any other chronic disease (that could be adapted for use in WRA).<br><br>DESIGN &amp; SETTING: Systematic review METHOD: We searched CCRCT, Embase, PsychINFO and Ovid-MEDLINE databases (1946-2023) for studies describing development and/or evaluation of BCIs for case finding any chronic disease in primary care settings, aimed at either healthcare professionals and/or patients. Two blinded, independent reviewers screened abstracts and assessed full text articles. We undertook narrative synthesis for outcomes of usability and effectiveness, and for BCI development processes.<br><br>RESULTS: We included 14 studies from n=768 retrieved citations, comprising 3 randomised control trials, 1 uncontrolled experimental study, and 10 studies employing recognized multi-step BC methodologies. None of the studies were concerned with identification of asthma. BCIs had been developed for facilitating screening programmes (5), implementing guidelines (3) and individual case finding (6). Five studies measured effectiveness, in terms of screening adherence rates, pre-/post-intervention competency, satisfaction and usability, for clinicians, though none measured diagnostic rates.<br><br>CONCLUSION: No single or multi-component BCIs has been developed specifically to aid identification of asthma or WRA, though other chronic diseases have been targeted. Development has used BC methodologies that involved gathering data from a range of sources, and developing content specific to defined at-risk populations, so are not immediately transferable. Such methodologies could be used similarly to develop a primary acre-based BCI for WRA.}, }
@article {pmid39565521, year = {2025}, author = {Guan, Z and Zhang, X and Huang, W and Li, K and Chen, D and Li, W and Sun, J and Chen, L and Mao, Y and Sun, H and Tang, X and Cao, L and Li, Y}, title = {A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.}, journal = {Neuroscience bulletin}, volume = {41}, number = {3}, pages = {434-448}, pmid = {39565521}, issn = {1995-8218}, mesh = {Humans ; Adolescent ; *Electroencephalography/methods ; Male ; Female ; *Depression/diagnosis/physiopathology ; *Brain-Computer Interfaces ; *Brain/physiopathology ; Rest/physiology ; }, abstract = {Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.}, }
@article {pmid39565505, year = {2024}, author = {Wang, XN and Zhang, T and Han, BC and Luo, WW and Liu, WH and Yang, ZY and Disi, A and Sun, Y and Yang, JC}, title = {Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.}, journal = {Current medical science}, volume = {44}, number = {6}, pages = {1141-1147}, pmid = {39565505}, issn = {2523-899X}, mesh = {Humans ; *Neurofeedback/methods/instrumentation ; Child ; Male ; Female ; *Electroencephalography/methods ; Child, Preschool ; *Machine Learning ; *Wearable Electronic Devices ; Autism Spectrum Disorder/therapy/physiopathology ; Autistic Disorder/therapy/physiopathology ; Algorithms ; }, abstract = {OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.<br><br>METHODS: A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites. The neurofeedback group received active mu rhythm neurofeedback training, while the control group received a sham neurofeedback training. Other behavioral intervention programs were similar between the two groups.<br><br>RESULTS: After 60 sessions of treatment, both groups showed significant improvements in several domains including language, social and problem behavior. The neurofeedback group showed significantly greater improvements in expressive language (P=0.013) and cognitive awareness (including joint attention, P=0.003) than did the placebo-controlled group.<br><br>CONCLUSION: Artificial intelligence-powered wearable EEG neurofeedback, as a type of brain-computer interface application, is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.}, }
@article {pmid39561980, year = {2024}, author = {Vacca, F and Galluzzi, F and Blanco-Formoso, M and Gianiorio, T and De Fazio, AF and Tantussi, F and Stürmer, S and Haq, W and Zrenner, E and Chaffiol, A and Joffrois, C and Picaud, S and Benfenati, F and De Angelis, F and Colombo, E}, title = {Solid-State Nanopores for Spatially Resolved Chemical Neuromodulation.}, journal = {Nano letters}, volume = {24}, number = {48}, pages = {15215-15225}, pmid = {39561980}, issn = {1530-6992}, mesh = {Animals ; *Nanopores ; *Neurons/drug effects/metabolism ; Mice ; *Neurotransmitter Agents/chemistry ; Synaptic Transmission/drug effects ; Glutamic Acid/chemistry ; Drug Delivery Systems ; Neural Prostheses ; Ceramics/chemistry ; }, abstract = {Most neural prosthetic devices are based on electrical stimulation, although the modulation of neuronal activity by a localized chemical delivery would better mimic physiological synaptic machinery. In the past decade, various drug delivery approaches attempted to emulate synaptic transmission, although they were hampered by poor retention of their cargo while reaching the target destination, low spatial resolution, and poor biocompatibility and stability of the materials involved. Here, we propose a planar solid-state device for multisite neurotransmitter translocation at the nanoscale consisting of a nanopatterned ceramic membrane connected to a reservoir designed to store neurotransmitters. We achieved diffusion-mediated glutamate stimulation of primary neurons, while we showed the feasibility to translocate other molecules through the pores by either pressure or diffusion, proving the versatility of the proposed technology. Finally, the system proved to be a promising neuronal stimulation interface in mice and nonhuman primates ex vivo, paving the way toward a biomimetic chemical stimulation in neural prosthetics and brain machine interfaces.}, }
@article {pmid39560446, year = {2025}, author = {Xu, F and Shi, W and Lv, C and Sun, Y and Guo, S and Feng, C and Zhang, Y and Jung, TP and Leng, J}, title = {Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.}, journal = {International journal of neural systems}, volume = {35}, number = {1}, pages = {2450069}, doi = {10.1142/S0129065724500692}, pmid = {39560446}, issn = {1793-6462}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Male ; Stroke/physiopathology ; Middle Aged ; Stroke Rehabilitation/methods ; Female ; Adult ; Brain/physiopathology ; Aged ; Signal Processing, Computer-Assisted ; }, abstract = {Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals from stroke patients poses challenges. To address the issues of low accuracy and efficiency in EEG classification, particularly involving MI, the study proposes a residual graph convolutional network (M-ResGCN) framework based on the modified S-transform (MST), and introduces the self-attention mechanism into residual graph convolutional network (ResGCN). This study uses MST to extract EEG time-frequency domain features, derives spatial EEG features by calculating the absolute Pearson correlation coefficient (aPcc) between channels, and devises a method to construct the adjacency matrix of the brain network using aPcc to measure the strength of the connection between channels. Experimental results involving 16 stroke patients and 16 healthy subjects demonstrate significant improvements in classification quality and robustness across tests and subjects. The highest classification accuracy reached 94.91% and a Kappa coefficient of 0.8918. The average accuracy and F1 scores from 10 times 10-fold cross-validation are 94.38% and 94.36%, respectively. By validating the feasibility and applicability of brain networks constructed using the aPcc in EEG signal analysis and feature encoding, it was established that the aPcc effectively reflects overall brain activity. The proposed method presents a novel approach to exploring channel relationships in MI-EEG and improving classification performance. It holds promise for real-time applications in MI-based BCI systems.}, }
@article {pmid39560167, year = {2025}, author = {Shah, AM}, title = {Hopeful progress in artificial vision.}, journal = {Artificial organs}, volume = {49}, number = {1}, pages = {5-6}, doi = {10.1111/aor.14912}, pmid = {39560167}, issn = {1525-1594}, mesh = {Humans ; *Brain-Computer Interfaces ; *Visual Prosthesis ; Eye, Artificial ; Prosthesis Design ; Vision, Ocular ; Vision Disorders/therapy ; }, abstract = {Visual impairment has been augmented by glasses for centuries. With the advent of newer technologies, correction of more severe visual impairment may be possible with brain-computer interface and eye implants.}, }
@article {pmid39556950, year = {2024}, author = {Kılınç Bülbül, D and Walston, ST and Duvan, FT and Garrido, JA and Güçlü, B}, title = {Decoding sensorimotor information from somatosensory cortex by flexible epicorticalμECoG arrays in unrestrained behaving rats.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9405}, pmid = {39556950}, issn = {1741-2552}, mesh = {Animals ; Rats ; *Somatosensory Cortex/physiology ; *Brain-Computer Interfaces ; Male ; *Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Graphite ; Rats, Sprague-Dawley ; Microelectrodes ; }, abstract = {Objective.Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats, and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25μm).Approach.Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies.μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.Main results.Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.Significance.Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by usingμECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.}, }
@article {pmid39556943, year = {2024}, author = {Leng, J and Gao, L and Jiang, X and Lou, Y and Sun, Y and Wang, C and Li, J and Zhao, H and Feng, C and Xu, F and Zhang, Y and Jung, TP}, title = {A multi-feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9403}, pmid = {39556943}, issn = {1741-2552}, mesh = {Humans ; *Spinal Cord Injuries/physiopathology ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; *Intention ; Adult ; Female ; Attention/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Young Adult ; Middle Aged ; }, abstract = {Objective.Electroencephalogram (EEG) signals exhibit temporal-frequency-spatial multi-domain feature, and due to the nonplanar nature of the brain surface, the electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a temporal-frequency-spatial multi-domain feature fusion graph attention network (GAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients.Approach.The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between EEG channels and continuous wavelet transform to extract valid EEG information in the time-frequency domain. It then models as a graph data structure containing multi-domain information. The gated recurrent unit and GAT learn EEG's dynamic temporal-spatial information. Finally, the fully connected layer outputs the MI intention recognition results.Main results.After 10 times 10-fold cross-validation, the proposed model can achieve an average accuracy of 95.82%. Furthermore, this study analyses the event-related desynchronization/event-related synchronization and PLV brain network to explore the brain activity of SCI patients during MI.Significance.This study confirms the potential of the proposed model in terms of EEG decoding performance and provides a reference for the mechanism of neural activity in SCI patients.}, }
@article {pmid39556340, year = {2024}, author = {Sang, Y and Li, B and Su, T and Zhan, H and Xiong, Y and Huang, Z and Wang, C and Cong, X and Du, M and Wu, Y and Yu, H and Yang, X and Ding, K and Wang, X and Miao, X and Gong, W and Wang, L and Zhao, J and Zhou, Y and Liu, W and Hu, X and Sun, Q}, title = {Visualizing ER-phagy and ER architecture in vivo.}, journal = {The Journal of cell biology}, volume = {223}, number = {12}, pages = {}, pmid = {39556340}, issn = {1540-8140}, support = {32025012//National Natural Science Foundation/ ; 2021YFC2700901//Ministry of Science and Technology of the People's Republic of China/ ; }, mesh = {Animals ; *Mice, Transgenic ; *Endoplasmic Reticulum/metabolism/genetics ; Mice ; Green Fluorescent Proteins/metabolism/genetics ; Luminescent Proteins/genetics/metabolism ; Red Fluorescent Protein ; Genes, Reporter ; Mice, Inbred C57BL ; }, abstract = {ER-phagy is an evolutionarily conserved mechanism crucial for maintaining cellular homeostasis. However, significant gaps persist in our understanding of how ER-phagy and the ER network vary across cell subtypes, tissues, and organs. Furthermore, the pathophysiological relevance of ER-phagy remains poorly elucidated. Addressing these questions requires developing quantifiable methods to visualize ER-phagy and ER architecture in vivo. We generated two transgenic mouse lines expressing an ER lumen-targeting tandem RFP-GFP (ER-TRG) tag, either constitutively or conditionally. This approach enables precise spatiotemporal measurements of ER-phagy and ER structure at single-cell resolution in vivo. Systemic analysis across diverse organs, tissues, and primary cultures derived from these ER-phagy reporter mice unveiled significant variations in basal ER-phagy, both in vivo and ex vivo. Furthermore, our investigation uncovered substantial remodeling of ER-phagy and the ER network in different tissues under stressed conditions such as starvation, oncogenic transformation, and tissue injury. In summary, both reporter models represent valuable resources with broad applications in fundamental research and translational studies.}, }
@article {pmid39555298, year = {2024}, author = {Li, J and Shi, W and Li, Y}, title = {An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2689-2707}, pmid = {39555298}, issn = {1871-4080}, abstract = {Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.}, }
@article {pmid39555297, year = {2024}, author = {Yin, Y and Kong, W and Tang, J and Li, J and Babiloni, F}, title = {PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2883-2896}, pmid = {39555297}, issn = {1871-4080}, abstract = {Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.}, }
@article {pmid39555294, year = {2024}, author = {Leng, J and Yu, X and Wang, C and Zhao, J and Zhu, J and Chen, X and Zhu, Z and Jiang, X and Zhao, J and Feng, C and Yang, Q and Li, J and Jiang, L and Xu, F and Zhang, Y}, title = {Functional connectivity of EEG motor rhythms after spinal cord injury.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {3015-3029}, pmid = {39555294}, issn = {1871-4080}, abstract = {Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.}, }
@article {pmid39555292, year = {2024}, author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y}, title = {Affective EEG-based cross-session person identification using hierarchical graph embedding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2897-2908}, pmid = {39555292}, issn = {1871-4080}, abstract = {The electroencephalogram (EEG) signal is being investigated as a more confidential biometric for person identification. Despite recent advancements, a persistent challenge lies in the influence of variations in affective states. Affective states consistently exist during data collection, regardless of the protocol used. Additionally, the inherently non-stationary nature of EEG makes it susceptible to fluctuations in affective states over time. Therefore, it would be highly crucial to perform precise EEG-based person identification under varying affective states. This paper employed an integrated Multi-scale Convolution and Graph Pooling network (MCGP) to mitigate the impact of affective state variations. MCGP utilized multiple 1D convolutions at different scales to dynamically extract and fuse features. Additionally, a graph pooling layer with an attention mechanism was incorporated to generate hierarchical graph embeddings. These embeddings were concatenated as inputs for a fully connected classification layer. Experiments were conducted on the SEED and SEED-V dataset, revealing that MCGP achieved an average accuracy of 85.51% for SEED and 88.69% for SEED-V in cross-session conditions involving mixed affective states. Under single affective state cross-session scenario, MCGP achieved an average accuracy of 85.75% for SEED and 88.06% for SEED-V for the same affective states, while obtaining 79.57% for SEED and 84.52% for SEED-V for different affective states. Results indicated that, compared to the baseline methods, MCGP effectively mitigated the impact of variations in affective states across different sessions. In single affective state cross-session scenario, identification performance for the same affective states was slightly higher than that for different affective states.}, }
@article {pmid39555282, year = {2024}, author = {Chen, L and Gao, H and Wang, Z and Gu, B and Zhou, W and Pang, M and Zhang, K and Liu, X and Ming, D}, title = {Vagus nerve electrical stimulation in the recovery of upper limb motor functional impairment after ischemic stroke.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {3107-3124}, pmid = {39555282}, issn = {1871-4080}, abstract = {Ischemic stroke (IS) is characterized by high mortality, disability rates, and a high risk of recurrence. Motor dysfunction, such as limb hemiparesis, dysphagia, auditory disorders, and speech disorders, usually persists after stroke, which imposes a heavy burden on society and the health care system. Traditional rehabilitation therapies may be ineffective in promoting functional recovery after stroke, and alternative strategies are urgently needed. The Food and Drug Administration (FDA) has approved invasive vagus nerve stimulation (iVNS) for the improvement of refractory epilepsy, treatment-resistant depression, obesity, and moderate to severe upper limb motor impairment following chronic ischemic stroke. Additionally, the FDA has approved transcutaneous vagus nerve stimulation (tVNS) for the improvement of cluster headaches and acute migraines. Recent studies have demonstrated that vagus nerve stimulation (VNS) has neuroprotective effects in both transient and permanent cerebral ischemia animal models, significantly improving upper limb motor impairments, auditory deficits, and swallowing difficulties. Firstly, this article reviews two potential neuronal death pathways following IS, including autophagy and inflammatory responses. Then delves into the current status of preclinical and clinical research on the functional recovery following IS with VNS, as well as the potential mechanisms mediating its neuroprotective effects. Finally, the optimal parameters and timing of VNS application are summarized, and the future challenges and directions of VNS in the treatment of IS are discussed. The application of VNS in stroke rehabilitation research has reached a critical stage, and determining how to safely and effectively translate this technology into clinical practice is of utmost importance. Further preclinical and clinical studies are needed to elucidate the therapeutic mechanisms of VNS.}, }
@article {pmid39555271, year = {2024}, author = {Liu, Y and Yu, S and Li, J and Ma, J and Wang, F and Sun, S and Yao, D and Xu, P and Zhang, T}, title = {Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2455-2470}, pmid = {39555271}, issn = {1871-4080}, abstract = {UNLABELLED: Motor imagery (MI) is a high-level cognitive process that has been widely applied to brain-computer inference (BCI) and motor recovery. In practical applications, however, huge individual differences and unclear neural mechanisms have seriously hindered the application of MI and BCI systems. Thus, it is urgently needed to explore MI from a new perspective. Here, we applied a hidden Markov model (HMM) to explore the dynamic organization patterns of left- and right-hand MI tasks. Eleven distinct HMM states were identified based on MI-related EEG data. We found that these states can be divided into three metastates by clustering analysis, showing a highly organized structure. We also assessed the probability activation of each HMM state across time. The results showed that the state probability activation of task-evoked have similar trends to that of event-related desynchronization/synchronization (ERD/ERS). By comparing the differences in temporal features of HMM states between left- and right-hand MI, we found notable variations in fractional occupancy, mean life time, mean interval time, and transition probability matrix across stages and states. Interestingly, we found that HMM states activated in the left occipital lobe had higher occupancy during the left-hand MI task, and conversely, during the right-hand MI task, HMM states activated in the right occipital lobe had higher occupancy. Moreover, significant correlations were observed between BCI performance and features of HMM states. Taken together, our findings explored dynamic networks underlying the MI-related process and provided a complementary understanding of different MI tasks, which may contribute to improving the MI-BCI systems.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10099-9.}, }
@article {pmid39555270, year = {2024}, author = {Wang, Y and Zhang, M and Li, M and Cui, H and Chen, X}, title = {Development of a humanoid robot control system based on AR-BCI and SLAM navigation.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2857-2870}, pmid = {39555270}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation. An 8-target steady-state visual evoked potential (SSVEP)-based BCI was implemented to enable direct control of the humanoid robot by the user. A Microsoft HoloLens was utilized to display visual stimuli for eliciting SSVEPs. Filter bank canonical correlation analysis (FBCCA), a training-free method, was used to detect SSVEPs in this study. By leveraging SLAM technology, the proposed system alleviates the need for frequent control commands transmission from the user, thereby effectively reducing their workload. Online results from 12 healthy subjects showed this developed BCI system was able to select a command out of eight potential targets with an average accuracy of 94.79%. The autonomous navigation subsystem enabled the humanoid robot to autonomously navigate to a destination chosen utilizing the proposed BCI. Furthermore, all participants successfully completed the experimental task using the developed system without any prior training. These findings illustrate the feasibility of the developed system and its potential to contribute novel insights into humanoid robots control strategies.}, }
@article {pmid39555269, year = {2024}, author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X}, title = {Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2871-2881}, pmid = {39555269}, issn = {1871-4080}, abstract = {With the continuing development of brain-computer interface technology, the analysis and interpretation of brain signals are becoming increasingly important. In the field of brain-computer interfaces, motor imagery (MI) is an important paradigm for generating specific brain signals through thought alone, rather than actual movement, for computer decoding. Functional near-infrared spectroscopy (fNIRS) imaging technology has been increasingly used in brain-computer interfaces due to its advantages of non-invasiveness, low resource requirements, low cost, and high spatial resolution. Scientists have done a lot of work in channel selection, feature selection, and then applying traditional machine learning methods for classification, but the results achieved so far are still insufficient to meet the conditions for realizing fNIRS brain-computer interfaces. To achieve a higher level of classification of fNIRS signals, we propose a method that fuses CNN and attention mechanisms to analyze the near-infrared signals of motor imagery and mental arithmetic data, which is fed into a neural network by deriving signals of changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations through the modified Beer-Lambert law, and then applied to the fNIRS dataset of 29 healthy subjects to validate the proposed method. In the fNIRS-based BCI, the average classification accuracy of the MI signal from HbR and HbO reaches 85.92% and 86.21%, respectively, and the average classification accuracy of the MA signal reaches 89.66% and 88.79%, respectively. The advantage of our approach is that it is lightweight and improves the classification accuracy of current BCI fNIRS signals.}, }
@article {pmid39555266, year = {2024}, author = {Wang, Z and Song, X and Chen, L and Nan, J and Sun, Y and Pang, M and Zhang, K and Liu, X and Ming, D}, title = {Research progress of epileptic seizure prediction methods based on EEG.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2731-2750}, pmid = {39555266}, issn = {1871-4080}, abstract = {At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.}, }
@article {pmid39555257, year = {2024}, author = {Ma, J and Yang, B and Rong, F and Gao, S and Wang, W}, title = {Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2521-2534}, pmid = {39555257}, issn = {1871-4080}, abstract = {Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.}, }
@article {pmid39555252, year = {2024}, author = {Pan, Y and Li, N and Zhang, Y and Xu, P and Yao, D}, title = {Short-length SSVEP data extension by a novel generative adversarial networks based framework.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2925-2945}, pmid = {39555252}, issn = {1871-4080}, abstract = {Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.}, }
@article {pmid39554850, year = {2024}, author = {Dan, Y and Zhou, D and Wang, Z}, title = {Discriminative possibilistic clustering promoting cross-domain emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1458815}, pmid = {39554850}, issn = {1662-4548}, abstract = {The affective Brain-Computer Interface (aBCI) systems strive to enhance prediction accuracy for individual subjects by leveraging data from multiple subjects. However, significant differences in EEG (Electroencephalogram) feature patterns among subjects often hinder these systems from achieving the desired outcomes. Although studies have attempted to address this challenge using subject-specific classifier strategies, the scarcity of labeled data remains a major hurdle. In light of this, Domain Adaptation (DA) technology has gradually emerged as a prominent approach in the field of EEG-based emotion recognition, attracting widespread research interest. The crux of DA learning lies in resolving the issue of distribution mismatch between training and testing datasets, which has become a focal point of academic attention. Currently, mainstream DA methods primarily focus on mitigating domain distribution discrepancies by minimizing the Maximum Mean Discrepancy (MMD) or its variants. Nevertheless, the presence of noisy samples in datasets can lead to pronounced shifts in domain means, thereby impairing the adaptive performance of DA methods based on MMD and its variants in practical applications to some extent. Research has revealed that the traditional MMD metric can be transformed into a 1-center clustering problem, and the possibility clustering model is adept at mitigating noise interference during the data clustering process. Consequently, the conventional MMD metric can be further relaxed into a possibilistic clustering model. Therefore, we construct a distributed distance measure with Discriminative Possibilistic Clustering criterion (DPC), which aims to achieve two objectives: (1) ensuring the discriminative effectiveness of domain distribution alignment by finding a shared subspace that minimizes the overall distribution distance between domains while maximizing the semantic distribution distance according to the principle of "sames attract and opposites repel"; and (2) enhancing the robustness of distribution distance measure by introducing a fuzzy entropy regularization term. Theoretical analysis confirms that the proposed DPC is an upper bound of the existing MMD metric under certain conditions. Therefore, the MMD objective can be effectively optimized by minimizing the DPC. Finally, we propose a domain adaptation in Emotion recognition based on DPC (EDPC) that introduces a graph Laplacian matrix to preserve the geometric structural consistency between data within the source and target domains, thereby enhancing label propagation performance. Simultaneously, by maximizing the use of source domain discriminative information to minimize domain discrimination errors, the generalization performance of the DA model is further improved. Comparative experiments on several representative domain adaptation learning methods using multiple EEG datasets (i.e., SEED and SEED-IV) show that, in most cases, the proposed method exhibits better or comparable consistent generalization performance.}, }
@article {pmid39554511, year = {2024}, author = {Herbozo Contreras, LF and Truong, ND and Eshraghian, JK and Xu, Z and Huang, Z and Bersani-Veroni, TV and Aguilar, I and Leung, WH and Nikpour, A and Kavehei, O}, title = {Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.}, journal = {PNAS nexus}, volume = {3}, number = {11}, pages = {pgae488}, pmid = {39554511}, issn = {2752-6542}, abstract = {Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.}, }
@article {pmid39553842, year = {2024}, author = {Zhou, L and Wu, B and Qin, B and Gao, F and Li, W and Hu, H and Zhu, Q and Qian, Z}, title = {Cortico-muscular coherence of time-frequency and spatial characteristics under movement observation, movement execution, and movement imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {1079-1096}, pmid = {39553842}, issn = {1871-4080}, abstract = {Studies show that movement observation (MO), movement imagery (MI), or movement execution (ME) based brain-computer interface systems are promising in promoting the rehabilitation and reorganization of damaged motor function. This study was aimed to explore and compare the motor function rehabilitation mechanism among MO, MI, and ME. 64-channel electroencephalogram and 4-channel electromyogram data were collected from 39 healthy participants (25 males, 14 females; 18-23 years old) during MO, ME, and MI. We analyzed and compared the inter-cortical, inter-muscular, cortico-muscular, and spatial coherence under MO, ME, and MI. Under MO, ME, and MI, cortico-muscular coherence was strongest at the beta-lh band, which means the beta frequency band for cortical signals and the lh frequency band for muscular signals. 56.25-96.88% of the coherence coefficients were significantly larger than 0.5 (ps < 0.05) at the beta-lh band. MO and ME had a contralateral advantage in the spatial coherence between cortex and muscle, while MI had an ipsilateral advantage in the spatial coherence between cortex and muscle. Our results show that the cortico-muscular beta-lh band plays a critical role in the synchronous coupling between cortex and muscle. Also, our findings suggest that the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and premotor cortex (PMC) are the specific regions of MO, ME, and MI. However, their pathways of regulating muscles are different under MO, ME, and MI. This study is important for better understanding the motor function rehabilitation mechanism in MO, MI, and ME.}, }
@article {pmid39551888, year = {2024}, author = {Lin, RR and Zhang, K}, title = {Survey of real-time brainmedia in artistic exploration.}, journal = {Visual computing for industry, biomedicine, and art}, volume = {7}, number = {1}, pages = {27}, pmid = {39551888}, issn = {2524-4442}, support = {2021JC02G114//Grant #2021JC02G114./ ; }, abstract = {This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.}, }
@article {pmid39551818, year = {2024}, author = {Zhao, H and Zhang, C and Tao, R and Wang, M and Yin, Y and Xu, S}, title = {Dyadic Similarity in Social Value Orientation Modulates Hyper-Brain Network Dynamics During Interpersonal Coordination: An fNIRS-Based Hyperscanning Study.}, journal = {Brain topography}, volume = {38}, number = {1}, pages = {15}, pmid = {39551818}, issn = {1573-6792}, support = {23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 72171151//National Natural Science Foundation of China/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; }, mesh = {Humans ; Male ; Young Adult ; Female ; *Spectroscopy, Near-Infrared/methods ; *Brain/physiology ; *Interpersonal Relations ; Cooperative Behavior ; Adult ; Brain Mapping/methods ; Social Behavior ; Neural Pathways/physiology ; }, abstract = {As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode.}, }
@article {pmid39550056, year = {2024}, author = {Chen, Y and Bai, J and Shi, N and Jiang, Y and Chen, X and Ku, Y and Gao, X}, title = {Intermodulation frequency components in steady-state visual evoked potentials: Generation, characteristics and applications.}, journal = {NeuroImage}, volume = {303}, number = {}, pages = {120937}, doi = {10.1016/j.neuroimage.2024.120937}, pmid = {39550056}, issn = {1095-9572}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Photic Stimulation/methods ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.}, }
@article {pmid39549531, year = {2025}, author = {Imtiaz, MN and Khan, N}, title = {Enhanced cross-dataset electroencephalogram-based emotion recognition using unsupervised domain adaptation.}, journal = {Computers in biology and medicine}, volume = {184}, number = {}, pages = {109394}, doi = {10.1016/j.compbiomed.2024.109394}, pmid = {39549531}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Female ; Male ; Adult ; Algorithms ; }, abstract = {Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain-computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) signals among individuals limit the cross-domain application of EEG-based emotion recognition models. Addressing cross-dataset scenarios poses greater challenges due to changes in subject demographics, recording devices, and stimuli presented. To tackle these challenges, we propose an improved method for classifying EEG-based emotions across domains with different distributions. We propose a Gradual Proximity-guided Target Data Selection (GPTDS) technique, which gradually selects reliable target domain samples for training based on their proximity to the source clusters and the model's confidence in predicting them. This approach avoids negative transfer caused by diverse and unreliable samples. Additionally, we introduce a cost-effective test-time augmentation (TTA) technique named Prediction Confidence-aware Test-Time Augmentation (PC-TTA). Traditional TTA methods often face substantial computational burden, limiting their practical utility. By applying TTA only when necessary, based on the model's predictive confidence, our approach improves the model's performance during inference while minimizing computational costs compared to traditional TTA approaches. Experiments on the DEAP and SEED datasets demonstrate that our method outperforms state-of-the-art approaches, achieving accuracies of 67.44% when trained on DEAP and tested on SEED, and 59.68% vice versa, with improvements of 7.09% and 6.07% over the baseline. It excels in detecting both positive and negative emotions, highlighting its effectiveness for practical emotion recognition in healthcare applications. Moreover, our proposed PC-TTA technique reduces computational time by a factor of 15 compared to traditional full TTA approaches.}, }
@article {pmid39549493, year = {2025}, author = {Hu, P and Zhang, X and Li, M and Zhu, Y and Shi, L}, title = {TSOM: Small object motion detection neural network inspired by avian visual circuit.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106881}, doi = {10.1016/j.neunet.2024.106881}, pmid = {39549493}, issn = {1879-2782}, mesh = {Animals ; *Neural Networks, Computer ; *Motion Perception/physiology ; *Visual Pathways/physiology ; *Columbidae/physiology ; Retina/physiology ; Algorithms ; Neurons/physiology ; Superior Colliculi/physiology ; Photic Stimulation/methods ; Birds/physiology ; }, abstract = {Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial-temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.}, }
@article {pmid39549492, year = {2025}, author = {Borra, D and Magosso, E and Ravanelli, M}, title = {A protocol for trustworthy EEG decoding with neural networks.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106847}, doi = {10.1016/j.neunet.2024.106847}, pmid = {39549492}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Deep Learning ; Algorithms ; Event-Related Potentials, P300/physiology ; Imagination/physiology ; Brain/physiology ; Brain-Computer Interfaces ; Adult ; Male ; }, abstract = {Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is rarely performed and limited to network-related hyperparameters. Moreover, pipelines are highly sensitive to performance fluctuations due to random initialization, hindering their reliability. Here, we design a comprehensive protocol for EEG decoding that explores the hyperparameters characterizing the entire pipeline and that includes multi-seed initialization for providing robust performance estimates. Our protocol is validated on 9 datasets about motor imagery, P300, SSVEP, including 204 participants and 26 recording sessions, and on different deep learning models. We accompany our protocol with extensive experiments on the main aspects influencing it, such as the number of participants used for hyperparameter search, the split into sequential simpler searches (multi-step search), the use of informed vs. non-informed search algorithms, and the number of random seeds for obtaining stable performance. The best protocol included 2-step hyperparameter search via an informed search algorithm, with the final training and evaluation performed using 10 random initializations. The optimal trade-off between performance and computational time was achieved by using a subset of 3-5 participants for hyperparameter search. Our protocol consistently outperformed baseline state-of-the-art pipelines, widely across datasets and models, and could represent a standard approach for neuroscientists for decoding EEG in a trustworthy and reliable way.}, }
@article {pmid39548722, year = {2024}, author = {Farahmand, M and Mousavi, M and Azizi, F and Ramezani Tehrani, F}, title = {Exploring the Influence of Age at Menarche on Metabolic Syndrome and Its Components Across Different Women's Birth Cohorts.}, journal = {Endocrinology, diabetes &amp; metabolism}, volume = {7}, number = {6}, pages = {e70015}, pmid = {39548722}, issn = {2398-9238}, support = {43002333//Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences/ ; }, mesh = {Humans ; *Menarche/physiology ; Female ; *Metabolic Syndrome/epidemiology/etiology ; Cross-Sectional Studies ; Adult ; Adolescent ; Iran/epidemiology ; Age Factors ; Middle Aged ; Child ; Birth Cohort ; Young Adult ; Risk Factors ; Prevalence ; Cohort Studies ; }, abstract = {PURPOSE: Metabolic syndrome (MetS) is the primary cardiovascular risk factor, making it a global issue. Our objective was to assess the association between the age at menarche (AAM) and MetS and its components in different generations of women.<br><br>METHODS: In this cross-sectional study, 5500 eligible women aged &#x2265;&#x2009;20 who participated in the Tehran lipid and glucose study in 2015-2017 were selected. Participants were divided into groups by birth cohorts (BC) (born &#x2264;&#x2009;1959, 1960-1979, and &#x2265;&#x2009;1980) and AAM (&#x2264;&#x2009;11, 12-15, and &#x2265;&#x2009;16&#x2009;years, early, normal, and late, respectively). The status of MetS and its components were compared amongst participants using logistic regression.<br><br>RESULTS: Normal AAM (12-15&#x2009;years) was considered the reference group. The adjusted model revealed that AAM &#x2264;&#x2009;11 is associated with a higher risk of 34% (95% confidence interval (CI): 1.04, 1.71) in MetS, and the prevalence of MetS in the early menarche group was higher in BCI, and BCII (odds ratio (OR): 1.87; 95% CI: 1.04, 3.36 and OR: 1.33; 95% CI: 1.00, 1.89, respectively). Those with late menarche demonstrated a lower risk (OR:0.72; 95% CI: 0.57, 0.91) of abdominal obesity, and early menarche showed a higher risk (OR: 1.45; CI: 1.14, 1.86). This higher risk in early menarche was observed in BCI and BCII (OR: 1.76; 95% CI: 1.16, 2.66 and OR: 1.80; 95% CI: 1.23, 2.64, respectively). However, the protective effect of late menarche was observed in BC II and BC III (OR: 0.74; 95% CI: 0.54, 1.00 and OR: 0.64; 95% CI: 0.44, 0.96, respectively).<br><br>CONCLUSIONS: The influential effect of AAM on metabolic disturbances varies amongst different generations.}, }
@article {pmid39548177, year = {2024}, author = {Ma, J and Ma, W and Zhang, J and Li, Y and Yang, B and Shan, C}, title = {Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {28170}, pmid = {39548177}, issn = {2045-2322}, support = {ZRJY2021-QM02//National High Level Hospital Clinical Research Funding and Elite Medical Professionals Project of China-Japan Friendship Hospital/ ; BYESS2023173//Young Elite Scientist Sponsorship Program By Bast/ ; 82272612//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Neural Networks, Computer ; Algorithms ; Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Adult ; Machine Learning ; }, abstract = {The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb.}, }
@article {pmid39547137, year = {2025}, author = {Wellman, SM and Forrest, AM and Douglas, MM and Subbaraman, A and Zhang, G and Kozai, TDY}, title = {Dynamic changes in the structure and function of brain mural cells around chronically implanted microelectrodes.}, journal = {Biomaterials}, volume = {315}, number = {}, pages = {122963}, doi = {10.1016/j.biomaterials.2024.122963}, pmid = {39547137}, issn = {1878-5905}, mesh = {*Microelectrodes ; *Pericytes/cytology ; Animals ; *Electrodes, Implanted/adverse effects ; *Brain/blood supply ; Male ; Mice ; Calcium/metabolism ; }, abstract = {Integration of neural interfaces with minimal tissue disruption in the brain is ideal to develop robust tools that can address essential neuroscience questions and combat neurological disorders. However, implantation of intracortical devices provokes severe tissue inflammation within the brain, which requires a high metabolic demand to support a complex series of cellular events mediating tissue degeneration and wound healing. Pericytes, peri-vascular cells involved in blood-brain barrier maintenance, vascular permeability, waste clearance, and angiogenesis, have recently been implicated as potential perpetuators of neurodegeneration in brain injury and disease. While the intimate relationship between pericytes and the cortical microvasculature have been explored in other disease states, their behavior following microelectrode implantation, which is responsible for direct blood vessel disruption and dysfunction, is currently unknown. Using two-photon microscopy we observed dynamic changes in the structure and function of pericytes during implantation of a microelectrode array over a 4-week implantation period. Pericytes respond to electrode insertion through transient increases in intracellular calcium and underlying constriction of capillary vessels. Within days following the initial insertion, we observed an influx of new, proliferating pericytes which contribute to new blood vessel formation. Additionally, we discovered a potentially novel population of reactive immune cells in close proximity to the electrode-tissue interface actively engaging in encapsulation of the microelectrode array. Finally, we determined that intracellular pericyte calcium can be modulated by intracortical microstimulation in an amplitude- and frequency-dependent manner. This study provides a new perspective on the complex biological sequelae occurring at the electrode-tissue interface and will foster new avenues of potential research consideration and lead to development of more advanced therapeutic interventions towards improving the biocompatibility of neural electrode technology.}, }
@article {pmid39545725, year = {2025}, author = {Noneman, KK and Patrick Mayo, J}, title = {Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.}, journal = {International journal of neural systems}, volume = {35}, number = {1}, pages = {2450070}, doi = {10.1142/S0129065724500709}, pmid = {39545725}, issn = {1793-6462}, mesh = {Animals ; *Eye Movements/physiology ; *Neurons/physiology ; *Machine Learning ; *Macaca mulatta ; *Neural Networks, Computer ; Action Potentials/physiology ; Eye-Tracking Technology ; Models, Neurological ; Male ; }, abstract = {Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.}, }
@article {pmid39545148, year = {2024}, author = {Wohns, N and Dorfman, N and Klein, E}, title = {Caregivers in implantable brain-computer interface research: a scoping review.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1490066}, pmid = {39545148}, issn = {1662-5161}, support = {R01 MH130457/MH/NIMH NIH HHS/United States ; }, abstract = {INTRODUCTION: While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention.<br><br>OBJECTIVES: This report investigates the extent to which caregivers are mentioned in publications describing implantable BCI (iBCI) research for individuals with motor dysfunction, communication impairment, and blindness.<br><br>METHODS: The scoping review was conducted in June 2024 using the PubMed and Web of Science bibliographic databases. The articles were systematically searched using query terms for caregivers, family members, and guardians, and the results were quantitatively and qualitatively analyzed.<br><br>RESULTS: Our search yielded 315 unique studies, 78 of which were included in this scoping review. Thirty-four (43.6%) of the 78 articles mentioned the study participant's caregivers. We sorted these into 5 categories: Twenty-two (64.7%) of the 34 articles thanked caregivers in the acknowledgement section, 6 (17.6%) articles described the caregiver's role with regard to the consent process, 12 (35.3%) described the caregiver's role in the technical maintenance and upkeep of the BCI system or in other procedural aspects of the study, 9 (26.5%) discussed how the BCI enhanced participant communication and goal-directed behavior with the help of a caregiver, and 3 (8.8%) articles included general comments that did not fit into the other categories but still related to the importance of caregivers in the lives of the research participants.<br><br>DISCUSSION: Caregivers were mentioned in less than half of BCI studies in this review. The studies that offered more robust discussions of caregivers provide valuable insight into the integral role that caregivers play in supporting the study participants and the research process. Attention to the role of caregivers in successful BCI research studies can help guide the responsible development of future BCI study protocols.}, }
@article {pmid39545146, year = {2024}, author = {Wu, X and Ju, X and Dai, S and Li, X and Li, M}, title = {Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1464431}, pmid = {39545146}, issn = {1662-5161}, abstract = {BACKGROUND: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance.<br><br>METHOD: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model.<br><br>RESULT: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively.<br><br>CONCLUSION: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.}, }
@article {pmid39544330, year = {2024}, author = {Wang, HZ and Saeed, S and Zhang, JY and Hu, SH}, title = {Bridging Three Years of Insights: Examining the Association Between Depression and Gallstone Disease.}, journal = {Journal of clinical medicine research}, volume = {16}, number = {10}, pages = {472-482}, pmid = {39544330}, issn = {1918-3003}, abstract = {BACKGROUND: Despite sharing common pathophysiological risk factors, the relationship between gallstones and depression requires further evidence for a clearer understanding. This study combines the National Health and Nutrition Examination Survey 2017 - 2020 observational data and Mendelian randomization (MR) analysis to shed light on the potential correlation between these conditions.<br><br>METHODS: By analyzing the National Health and Nutrition Examination Survey 2017 - 2020 data through weighted multivariable-adjusted logistic regression, we examined the association between depression and gallstone risk. MR was subsequently applied, utilizing genetic instruments from a large genome-wide association study on depression (excluding 23andMe, 500,199 participants) and gallstone data (28,627 cases, 348,373 controls), employing the main inverse variance-weighted method alongside other MR methods to explore the causal relationship. Sensitivity analyses validated the study's conclusions.<br><br>RESULTS: Among the 5,303 National Health and Nutrition Examination Survey participants, a significant association was found between depressive symptoms and increased gallstone risk (initial odds ratio (OR) = 2.001; 95% confidence interval (CI) = 1.523 - 2.598; P < 0.001), with the association persisting after comprehensive adjustments (final OR = 1.687; 95% CI = 1.261 - 2.234; P < 0.001). MR findings also indicated a causal link between genetically predicted depression and higher gallstone risk (OR = 1.164; 95% CI = 1.053 - 1.286; P = 0.003).<br><br>CONCLUSIONS: Depression is significantly associated with a higher risk of gallstones, supported by genetic evidence suggesting a causal link. These findings highlight the importance of considering depression in gallstone risk assessments and management strategies.}, }
@article {pmid39543314, year = {2024}, author = {Rybář, M and Poli, R and Daly, I}, title = {Using data from cue presentations results in grossly overestimating semantic BCI performance.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {28003}, pmid = {39543314}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Semantics ; *Cues ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Brain/physiology ; }, abstract = {Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.}, }
@article {pmid39542998, year = {2024}, author = {Kober, SE and Wood, G and Berger, LM}, title = {Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {39542998}, issn = {1573-3270}, abstract = {The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.}, }
@article {pmid39539351, year = {2024}, author = {Liu, M and Fang, M and Liu, M and Jin, S and Liu, B and Wu, L and Li, Z}, title = {Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1486167}, pmid = {39539351}, issn = {1662-5161}, abstract = {BACKGROUND: Although the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.<br><br>METHODS: The study retrieved relevant publications on BCI technology in rehabilitation from the Web of Science Core Collection (WoSCC) between January 1, 2004, and June 30, 2024. The search was conducted using thematic queries, and the document types included "original articles" and "review articles." Bibliometric analysis and knowledge mapping were performed using the Bibliometrix package in R software and CiteSpace software.<br><br>RESULTS: During the study period, a total of 1,431 publications on BCI technology in rehabilitation were published by 4,932 authors from 1,281 institutions across 79 countries in 386 academic journals. The volume of research literature in this field has shown a steady upward trend. The United States of America (USA) and China are the primary contributors, with Eberhard Karls University of T&#xfc;bingen being the most active research institution. The journal Frontiers in Neuroscience published the most articles, while the Journal of Neural Engineering was the most cited. Niels Birbaumer not only authored the most articles but also received the highest number of citations. The main research areas include neurology, sports medicine, and ophthalmology. The diverse applications of BCI technology in stroke and spinal cord injury rehabilitation, as well as the evaluation of BCI performance, are current research hotspots. Moreover, deep learning has demonstrated significant potential in BCI technology rehabilitation applications.<br><br>CONCLUSION: This bibliometric study provides an overview of the research landscape and developmental trends of BCI technology in rehabilitation, offering valuable reference points for researchers in formulating future research strategies.}, }
@article {pmid39539350, year = {2024}, author = {Xia, R and Yang, S}, title = {Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1423382}, pmid = {39539350}, issn = {1662-5161}, abstract = {This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents (N = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.}, }
@article {pmid39537730, year = {2024}, author = {Tian, P and Xu, G and Han, C and Du, C and Li, H and Chen, R and Xie, J and Wang, J and Jiang, H and Guo, X and Zhang, S and Wu, Q}, title = {A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {27872}, pmid = {39537730}, issn = {2045-2322}, support = {No.20231055//The First Affiliated Hospital of Xi'an Jiaotong University/ ; No.20231056//The First Affiliated Hospital of Xi'an Jiaotong University/ ; Program No. 2023-JC-QN-0501//Natural Science Basic Research Program of Shaanxi Province/ ; No.2021ZD0204300//National Key Research and Development projects/ ; }, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Software ; Adult ; Algorithms ; Female ; Young Adult ; Fatigue/physiopathology ; Asthenopia/physiopathology ; Photic Stimulation ; }, abstract = {With the development of brain-computer interface industry, large amounts of related applications have entered people's vision. BCI applications based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and have high information transmission rates. However, in the actual use of SSVEP stimulus paradigm, the subjects will produce visual fatigue with the use, and fatigue will affect the transmission efficiency. In this experiment, an experimental environment consisting of two paradigm stimulus frequencies (7.5 Hz, 15 Hz), three resolutions (800 × 600, 1280 × 720, 1920 × 1080) and three refresh rates (120 Hz, 240 Hz, 360 Hz) is set up. The Likert scale is used to collect subjective fatigue and preference scores, and the EEG acquisition system and eye tracker are used to collect objective data. Using the proposed information entropy-CRITIC algorithm to combine subjective and objective indicators, a fatigue assessment system (display screen fitness-DSF) is innovated to score different experimental environments. The higher the DSF score, the better the visual experience. The results show that when using the 7.5 Hz SSVEP paradigm, the combination of 360 Hz and 1920 × 1080 can bring the best visual experience. When using the 15 Hz SSVEP paradigm, the combination of 240 Hz and 1280 × 720 is the best. DSF provides powerful help for hardware and software selection guidance and vision protection when using SSVEP-based BCI applications.}, }
@article {pmid39537459, year = {2024}, author = {Lai, W and Sha, L and Li, R and Yu, S and Jin, L and Yang, R and Yang, C and Chen, L}, title = {Urine multi-omics markers to predict seizure one day in advance.}, journal = {Science bulletin}, volume = {69}, number = {24}, pages = {3844-3848}, doi = {10.1016/j.scib.2024.10.035}, pmid = {39537459}, issn = {2095-9281}, }
@article {pmid39536406, year = {2025}, author = {Chen, M and Guo, K and Lu, K and Meng, K and Lu, J and Pang, Y and Zhang, L and Hu, Y and Yu, R and Zhang, R}, title = {Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network.}, journal = {Computer methods and programs in biomedicine}, volume = {258}, number = {}, pages = {108483}, doi = {10.1016/j.cmpb.2024.108483}, pmid = {39536406}, issn = {1872-7565}, mesh = {Humans ; *Drug Resistant Epilepsy/surgery/diagnostic imaging/physiopathology ; Retrospective Studies ; Female ; Male ; *Seizures/surgery/physiopathology/diagnostic imaging ; Adult ; Treatment Outcome ; Electroencephalography/methods ; Young Adult ; Adolescent ; Brain/diagnostic imaging/surgery/physiopathology ; Child ; Electrocorticography/methods ; Algorithms ; }, abstract = {BACKGROUND AND OBJECTIVE: Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.<br><br>METHODS: We conduct a retrospective study on intracranial electroencephalography (iEEG) data from 46 patients with DRE. The interactions between different brain regions are quantified by using the phase transfer entropy (PTE), based on which the causal influence index (CII) is proposed to quantify the degree of influence of nodes on the network. Subsequently, the features extracted by the CII are used to construct a random forest classification model, which the performance in identifying SOZ and the generalizability are validated in patients with successful surgeries. Then, based on the CII features of the clinically labeled SOZ, a logistic regression prediction model is constructed to predict the probability of surgical success. The statistical analysis between patients with successful and failed surgery is conducted with the Mann-Whitney U test. Finally, the consistency between the predicted SOZ and the clinically labeled SOZ is verified across different Engel classes.<br><br>RESULTS: The classification model combining the low-frequency and high-frequency features can achieve an accuracy of 82.18% (sensitivity: 85.01%, specificity: 79.69%) and an area under curve (AUC) of 0.90 in identifying SOZ. Furthermore, the model exhibits strong generalizability in identifying SOZ in patients with MRI lesional and non-lesional, as well as those implanted with electrocorticography (ECOG) and stereotactic EEG (SEEG) electrodes. Moreover, the prediction model could achieve an average accuracy of 79.8% and an AUC of 0.84. Of note, the prediction of surgical success probability is significant between patients with successful and failed surgeries (P<0.001). Correspondingly, the highest consistency between model-predicted SOZ and clinically labeled SOZ can be observed in patients with successful surgeries, but this consistency gradually decreases with increasing Engel classes.<br><br>CONCLUSIONS: These results demonstrate that the CII may be a potential biomarker for identifying the SOZ in patients with DRE, which may provide a new perspective for the treatment of epilepsy.}, }
@article {pmid39535986, year = {2024}, author = {Officer, K and Arango-Sabogal, JC and Dufour, S and Lyashchenko, KP and Cracknell, J and Thomson, S and Cheng, S and Warren, K and Jackson, B}, title = {Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0313007}, pmid = {39535986}, issn = {1932-6203}, mesh = {Animals ; *Ursidae/microbiology ; *Bayes Theorem ; Cambodia/epidemiology ; Vietnam/epidemiology ; Diagnostic Tests, Routine/methods ; Sensitivity and Specificity ; Tuberculosis, Pulmonary/diagnosis/epidemiology/microbiology ; Tuberculosis/diagnosis/epidemiology ; Retrospective Studies ; Mycobacterium tuberculosis/isolation &amp; purification ; Bronchoalveolar Lavage Fluid/microbiology ; Male ; Female ; }, abstract = {Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.}, }
@article {pmid39528522, year = {2024}, author = {Hou, Z and Li, X and Yang, J and Xu, SY}, title = {Enhancing mathematical learning outcomes through a low-cost single-channel BCI system.}, journal = {NPJ science of learning}, volume = {9}, number = {1}, pages = {65}, pmid = {39528522}, issn = {2056-7936}, abstract = {This study investigates the effectiveness of a Low-Cost Single-Channel BCI system in improving mathematical learning outcomes, self-efficacy, and alpha power in university students. Eighty participants were randomly assigned to either a BCI group receiving real-time neurofeedback based on alpha rhythms or a sham feedback group. Results showed that the BCI group had significantly higher mathematical performance, self-efficacy, and alpha power compared to the sham feedback group. Mathematics performance, alpha wave intensity, and self-efficacy showed significant positive correlations after training, indicating that neurofeedback training may have promoted their interaction and integration. These findings demonstrate the potential of BCI technology in enhancing mathematical learning outcomes and highlight the importance of considering pre-test performance and self-efficacy in predicting learning outcomes, with implications for personalized learning interventions and the integration of BCI technology in educational settings.}, }
@article {pmid39534365, year = {2024}, author = {Huang, Z and Wei, Q}, title = {Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {877-892}, pmid = {39534365}, issn = {1871-4080}, abstract = {The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs. A three-way tensor is yielded by wavelet transform of a single-trial EEG signal and decomposed into three factor matrices by a regularized canonical polyadic decomposition (CPD). The channel factor matrix is used for channel selection and the important channels are selected by calculating the correlation between channels. Regularized common spatial pattern (RCSP) is employed for feature extraction and support vector machine (SVM) for classification. The proposed TCS-RCSP algorithm was evaluated on three BCI data sets and compared with the RCSP with all channels (AC-RCSP) and the RCSP with selected channels by correlation-based channel selection method (CCS-RCSP). The results indicate that TCS-RCSP achieved significantly better overall accuracy than AC-RCSP (94.4% vs. 86.3%) with ρ < 0.01 and CCS-RCSP (94.4% vs. 90.2%) with ρ < 0.05, proving the efficacy of the proposed algorithm for classifying MI tasks.}, }
@article {pmid39531567, year = {2024}, author = {Wang, J and Cui, Y and Zhang, H and Wu, H and Yang, C}, title = {An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {4120-4130}, doi = {10.1109/TNSRE.2024.3496727}, pmid = {39531567}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Algorithms ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Signal-To-Noise Ratio ; Probability ; Reproducibility of Results ; }, abstract = {SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.}, }
@article {pmid39530641, year = {2024}, author = {Garcia Cerqueira, EM and de Medeiros, RE and da Silva Fiorin, F and de Arújo E Silva, M and Hypolito Lima, R and Azevedo Dantas, AFO and Rodrigues, AC and Delisle-Rodriguez, D}, title = {Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad9155}, pmid = {39530641}, issn = {2057-1976}, mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Rats, Wistar ; *Seizures/therapy ; *Epilepsy/therapy ; Spinal Cord ; Motor Cortex/physiopathology ; Hippocampus ; Male ; Spinal Cord Stimulation/methods ; Algorithms ; Machine Learning ; Pentylenetetrazole ; Electric Stimulation/methods ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; }, abstract = {Objective.This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.Approach.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, theZ-score-based PLV normalization using both modifiedk-means and Davies-Bouldin's measure for clustering is proposed here. Consequently, a generic seizure's detector is calibrated for detecting seizures on the normalized PLV, and enables the spinal cord stimulation for periods of 30 s in a closed-loop, while the BMI system detects seizure events. To calibrate the proposed BMI, a dataset with LFP signals recorded on five Wistar rats during basal state and epileptic crisis was used. The epileptic crisis was induced by injecting pentylenetetrazol (PTZ). Afterwards, two experiments without/with our BMI were carried out, inducing epileptic crisis by PTZ in Wistar rats.Main results.Stronger seizure events of high LFP amplitudes and long time periods were observed in the rat, when the BMI system was not used. In contrast, short-time seizure events of relative low intensity were observed in the rat, using the proposed BMI. The proposed system detected on unseen data the synchronized seizure activity in the hippocampus and motor cortex, provided stimulation appropriately, and consequently decreased seizure symptoms.Significance.Low-frequency LFP signals from the hippocampus and motor cortex, and cord spinal stimulation can be used to develop accurate closed-loop BMIs for early epileptic seizures inhibition, as an alternative treatment.}, }
@article {pmid39529486, year = {2025}, author = {Blanken, TF and Kok, R and Obbels, J and Lambrichts, S and Sienaert, P and Verwijk, E}, title = {Prediction of electroconvulsive therapy outcome: A network analysis approach.}, journal = {Acta psychiatrica Scandinavica}, volume = {151}, number = {4}, pages = {521-528}, pmid = {39529486}, issn = {1600-0447}, support = {//Flemish Fund for Scientific Research/ ; //Amsterdam Brain and Cognition/ ; }, abstract = {OBJECTIVE: While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach.<br><br>METHOD: We combined individual patient data from two randomized controlled trials (total N&#x2009;=&#x2009;161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD&#x2264;7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios.<br><br>RESULTS: Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR&#x2009;=&#x2009;0.75; bootstrapped confidence interval (bCI)&#x2009;=&#x2009;0.44-1.00) whereas retardation (OR&#x2009;=&#x2009;1.21; bCI&#x2009;=&#x2009;1.00-2.02) and hypochondriasis (OR&#x2009;=&#x2009;1.31; bCI&#x2009;=&#x2009;1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found.<br><br>CONCLUSION: By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.}, }
@article {pmid39529200, year = {2024}, author = {Liu, DY and Li, M and Yu, J and Gao, Y and Zhang, X and Hu, D and Northoff, G and Song, XM and Zhu, J}, title = {Sex differences in the human brain related to visual motion perception.}, journal = {Biology of sex differences}, volume = {15}, number = {1}, pages = {92}, pmid = {39529200}, issn = {2042-6410}, support = {2024SSYS0019//the key R&amp;D program of zhejiang/ ; 2022C03096//the key R&amp;D program of zhejiang/ ; 82272112//the national natural science foundation of china grants/ ; 62076248//the national natural science foundation of china grants/ ; 52293424//the national natural science foundation of china grants/ ; LR23E070001//zhejiang proveincial natural science foundation of china/ ; }, mesh = {Humans ; Female ; Male ; *Motion Perception/physiology ; *Magnetic Resonance Imaging ; *Sex Characteristics ; Adult ; *Brain/physiology/diagnostic imaging ; Young Adult ; Photic Stimulation ; }, abstract = {BACKGROUND: Previous studies have found that the temporal duration required for males to perceive visual motion direction is significantly shorter than that for females. However, the neural correlates of such shortened duration perception remain yet unclear. Given that motion perception is primarily associated with the neural activity of the middle temporal visual complex (MT+), we here test the novel hypothesis that the neural mechanism of these behavioral sex differences is mainly related to the MT+ region.<br><br>METHODS: We utilized ultra-high field (UHF) MRI to investigate sex differences in the MT+ brain region. A total of 95 subjects (48 females) participated in two separate studies. Cohort 1, consisting of 33 subjects (16 females), completed task-fMRI (drafting grating stimuli) experiment. Cohort 2, comprising 62 subjects (32 females), engaged in a psychophysical experiment measuring motion perception along different temporal thresholds as well as conducting structural and functional MRI scanning of MT+.<br><br>RESULTS: Our findings show pronounced sex differences in major brain parameters within the left MT+ (but not the right MT+, i.e., laterality). In particular, males demonstrate (i) larger gray matter volume (GMV) and higher brain's spontaneous activity at the fastest infra-slow frequency band in the left MT+; and (ii) stronger functional connectivity between the left MT+ and the left centromedial amygdala (CM). Meanwhile, both female and male participants exhibited comparable correlations between motion perception ability and the multimodal imaging indexes of the MT+ region, i.e., larger GMV, higher brain's spontaneous activity, and faster motion discrimination.<br><br>CONCLUSIONS: Our findings reveal sex differences of imaging indicators of structure and function in the MT+ region, which also relate to the temporal threshold of motion discrimination. Overall, these results show how behavioral sex differences in visual motion perception are generated, and advocate considering sex as a crucial biological variable in both human brain and behavioral research.}, }
@article {pmid39528479, year = {2024}, author = {Tian, F and Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9742}, doi = {10.1038/s41467-024-54090-8}, pmid = {39528479}, issn = {2041-1723}, }
@article {pmid39527418, year = {2025}, author = {Shi, Y and Jiang, A and Zhong, J and Li, M and Zhu, Y}, title = {Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {2}, pages = {935-947}, doi = {10.1109/JBHI.2024.3496757}, pmid = {39527418}, issn = {2168-2208}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Brain/physiology ; }, abstract = {In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.}, }
@article {pmid39523453, year = {2024}, author = {Shen, B and Yao, Q and Zhang, Y and Jiang, Y and Wang, Y and Jiang, X and Zhao, Y and Zhang, H and Dong, S and Li, D and Chen, Y and Pan, Y and Yan, J and Han, F and Li, S and Zhu, Q and Zhang, D and Zhang, L and Wu, YC}, title = {Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {11}, pages = {e70115}, pmid = {39523453}, issn = {1755-5949}, support = {NO.QRX17026//Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau/ ; //Nanjing Rehabilitation Medicine Center Project/ ; NO.BE2022842//Special Funds of the Jiangsu Provincial Key Research and Development Program/ ; NO.LD2021013//Jiangsu Province Elderly Health Project/ ; NO.81971185//National Natural Science Foundation of China/ ; NO.82171243//National Natural Science Foundation of China/ ; NO.82171249//National Natural Science Foundation of China/ ; NO.82204352//National Natural Science Foundation of China/ ; NO.BJ20005//Jiangsu Provincial Cadre Health Projects/ ; 23-25-2R11//Nanjing Brain Hospital Youth Talent Project/ ; 23-25-2R6//Nanjing Brain Hospital Youth Talent Project/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Parkinson Disease/physiopathology/diagnostic imaging ; *Postural Balance/physiology ; Female ; Male ; Middle Aged ; *Gait Disorders, Neurologic/physiopathology/etiology ; Aged ; *Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; Brain/physiopathology/diagnostic imaging ; Connectome ; Neural Pathways/physiopathology ; }, abstract = {AIMS: The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC).<br><br>METHODS: We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms.<br><br>RESULTS: Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores.<br><br>CONCLUSION: Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.}, }
@article {pmid39523287, year = {2024}, author = {Rosenfeld, JV}, title = {Neurosurgery and the Brain-Computer Interface.}, journal = {Advances in experimental medicine and biology}, volume = {1462}, number = {}, pages = {513-527}, pmid = {39523287}, issn = {0065-2598}, mesh = {*Brain-Computer Interfaces/ethics ; Humans ; Brain/physiology ; Neurosurgical Procedures/methods ; Electrodes, Implanted ; Neurosurgery/methods/instrumentation ; }, abstract = {Brain-computer interfaces (BCIs) are devices that connect the human brain to an effector via a computer and electrode interface. BCIs may also transmit sensory data to the brain. We describe progress with the many types of surgically implanted BCIs, in which electrodes contact or penetrate the cerebral cortex. BCIs developed for restoration of movement in paralyzed limbs or control a robotic arm; restoration of somatic sensation, speech, vision, memory, hearing, and olfaction are also presented. Most devices remain experimental. Commercialization is costly, incurs financial risk, and is time consuming. There are many ethical principles that should be considered by neurosurgeons and by all those responsible for the care of people with serious neurological disability. These considerations are also paramount when the technology is used in for the purpose of enhancement of normal function and where commercial gain is a factor. A new regulatory and legislative framework is urgently required. The evolution of BCIs is occurring rapidly with advances in computer science, artificial intelligence, electronic engineering including wireless transmission, and materials science. The era of the brain-"cloud" interface is approaching.}, }
@article {pmid39521778, year = {2024}, author = {Pacheco-Ramírez, MA and Ramírez-Moreno, MA and Kukkar, K and Rao, N and Huber, D and Brandt, AK and Noble, A and Noble, D and Ealey, B and Contreras-Vidal, JL}, title = {Neural Dynamics of Creative Movements During the Rehearsal and Performance of "LiveWire".}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1208}, pmid = {39521778}, issn = {2052-4463}, support = {IUCRC BRAIN #2137255//National Science Foundation (NSF)/ ; }, mesh = {Humans ; *Dancing/physiology ; *Electroencephalography ; *Brain/physiology ; Electrooculography ; Movement ; Music ; Creativity ; }, abstract = {This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of "LiveWire" - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.}, }
@article {pmid39520382, year = {2025}, author = {Kim, D and Lee, JW and Kim, YT and Choe, J and Kim, G and Ha, CM and Kim, JG and Song, KH and Yang, S}, title = {Minimally Invasive Syringe-Injectable Hydrogel with Angiogenic Factors for Ischemic Stroke Treatment.}, journal = {Advanced healthcare materials}, volume = {14}, number = {6}, pages = {e2403119}, pmid = {39520382}, issn = {2192-2659}, support = {//National Research Foundation of Korea/ ; NRF-2022R1A4A5034121//Korean government/ ; NRF-2022R1A2C1007876//Korean government/ ; NRF-2021R1C1C1010633//Korean government/ ; NRF-RS-2023-00229062//Korean government/ ; 24-BR-03-02//Ministry of Science, ICT and Future Planning/ ; }, mesh = {Animals ; *Hydrogels/chemistry/pharmacology ; *Ischemic Stroke/drug therapy ; Rats ; *Rats, Sprague-Dawley ; Male ; Nanofibers/chemistry/therapeutic use ; Syringes ; Gelatin/chemistry ; Angiogenesis Inducing Agents/pharmacology/administration &amp; dosage/chemistry ; Norbornanes/chemistry ; }, abstract = {Ischemic stroke (IS) accounts for most stroke incidents and causes intractable damage to brain tissue. This condition manifests as diverse aftereffects, such as motor impairment, emotional disturbances, and dementia. However, a fundamental approach to curing IS remains unclear. This study proposes a novel approach for treating IS by employing minimally invasive and injectable jammed gelatin-norbornene nanofibrous hydrogels (GNF) infused with growth factors (GFs). The developed GNF/GF hydrogels are administered to the motor cortex of a rat IS model to evaluate their therapeutic effects on IS-induced motor dysfunction. GNFs mimic a natural fibrous extracellular matrix architecture and can be precisely injected into a targeted brain area. The syringe-injectable jammed nanofibrous hydrogel system increased angiogenesis, inflammation, and sensorimotor function in the IS-affected brain. For clinical applications, the biocompatible GNF hydrogel has the potential to efficiently load disease-specific drugs, enabling targeted therapy for treating a wide range of neurological diseases.}, }
@article {pmid39517980, year = {2024}, author = {Chen, Y and Shi, X and De Silva, V and Dogan, S}, title = {Steady-State Visual Evoked Potential-Based Brain-Computer Interface System for Enhanced Human Activity Monitoring and Assessment.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517980}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Brain/physiology ; }, abstract = {Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.}, }
@article {pmid39517978, year = {2024}, author = {Ma, S and Zhang, D}, title = {A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517978}, issn = {1424-8220}, support = {12271211//the National Natural Science Foundation of China/ ; 2022J011275&#xff0c;2021J01861//the Natural Science Foundation of Fujian Province/ ; ZQ2023022//the Doctoral Research Initiation Fund of Jimei University/ ; FJKX-2023XKB007//the Research Project of Fujian Association for Science and Technology Innovation Think Tank/ ; 2023SXLMMS06//Fujian Alliance Of Mathematics/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic.<br><br>METHODS: We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed.<br><br>RESULTS: Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%.<br><br>CONCLUSIONS: This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.}, }
@article {pmid39517915, year = {2024}, author = {Wei, P and Chen, T and Zhang, J and Li, J and Hong, J and Zhang, L}, title = {Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517915}, issn = {1424-8220}, support = {52305294//National Natural Science Foundation of China/ ; xhj032021010-03//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Lower Extremity/physiology ; *Electroencephalography/methods ; *Walking/physiology ; Male ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; *Movement/physiology ; Female ; Adult ; Gait/physiology ; }, abstract = {Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain-computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time-frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal-occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.}, }
@article {pmid39517862, year = {2024}, author = {Rehman, M and Anwer, H and Garay, H and Alemany-Iturriaga, J and Díez, IT and Siddiqui, HUR and Ullah, S}, title = {Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517862}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Brain/physiology ; *Signal Processing, Computer-Assisted ; Visual Perception/physiology ; Algorithms ; Photic Stimulation ; }, abstract = {The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.}, }
@article {pmid39517744, year = {2024}, author = {Rettore Andreis, F and Meijs, S and Nielsen, TGNDS and Janjua, TAM and Jensen, W}, title = {Comparison of Subdural and Intracortical Recordings of Somatosensory Evoked Responses.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517744}, issn = {1424-8220}, support = {DNRF121//Danish National Research Foundation/ ; }, mesh = {*Evoked Potentials, Somatosensory/physiology ; Animals ; Swine ; *Somatosensory Cortex/physiology ; *Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Electrocorticography/methods ; Microelectrodes ; Electric Stimulation/methods ; Electrodes, Implanted ; }, abstract = {Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. This study aimed to compare somatosensory evoked potentials (SEPs) through the lenses of a µECoG and an intracortical microelectrode array (MEA). The electrodes were implanted in the pig's primary somatosensory cortex, while SEPs were generated by applying electrical stimulation to the ulnar nerve. The SEP amplitude, signal-to-noise ratio (SNR), power spectral density (PSD), and correlation structure were analysed. Overall, SEPs resulting from MEA recordings had higher amplitudes and contained significantly more spectral power, especially at higher frequencies. However, the SNRs were similar between the interfaces. These results demonstrate the feasibility of using µECoG to decode SEPs with wide-range applications in physiology monitoring and brain-computer interfaces.}, }
@article {pmid39517124, year = {2024}, author = {Zhang, A and Jiang, J and Zhang, C and Xu, H and Yu, W and Zhang, ZN and Yuan, L and Lu, Z and Deng, Y and Fan, H and Fang, C and Wang, X and Shao, A and Chen, S and Li, H and Ni, J and Wang, W and Zhang, X and Zhang, J and Luan, B}, title = {Thermogenic Adipocytes Promote M2 Macrophage Polarization through CNNM4-Mediated Mg Secretion.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {47}, pages = {e2401140}, pmid = {39517124}, issn = {2198-3844}, support = {2023YFC2705700//National Key Research and Development Program of China/ ; 32471220//National Natural Science Foundation of China/ ; 82401598//National Natural Science Foundation of China/ ; 82350710799//National Natural Science Foundation of China/ ; 32400982//National Natural Science Foundation of China/ ; 52201300//National Natural Science Foundation of China/ ; 2023ZD0512800//Science and Technology Innovation 2030: Major national science and technology projects/ ; 2021M702090//China Postdoctoral Science Foundation/ ; 2023M742639//China Postdoctoral Science Foundation/ ; BX20240257//China Postdoctoral Science Foundation/ ; 21SG21//Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission/ ; 2023573//Shanghai Post-doctoral Excellence Program/ ; }, mesh = {Animals ; Mice ; *Macrophages/metabolism ; *Thermogenesis ; *Adipocytes/metabolism ; *Magnesium/metabolism ; Mice, Inbred C57BL ; Cation Transport Proteins/metabolism/genetics ; Signal Transduction ; Obesity/metabolism ; Disease Models, Animal ; Male ; }, abstract = {M2 macrophages promote adipose tissue thermogenesis which dissipates energy in the form of heat to combat obesity. However, the regulation of M2 macrophages by thermogenic adipocytes is unclear. Here, it is identified magnesium (Mg) as a thermogenic adipocyte-secreted factor to promote M2 macrophage polarization. Mg transporter Cyclin and CBS domain divalent metal cation transport mediator 4 (CNNM4) induced by ADRB3-PKA-CREB signaling in thermogenic adipocytes during cold exposure mediates Mg efflux and Mg in turn binds to the DFG motif in mTOR to facilitate mTORC2 activation and M2 polarization in macrophages. In obesity, downregulation of CNNM4 expression inhibits Mg secretion from thermogenic adipocytes, which leads to decreased M2 macrophage polarization and thermogenesis. As a result, CNNM4 overexpression in adipocytes or Mg supplementation in adipose tissue ameliorates obesity by promoting thermogenesis. Importantly, an Mg wire implantation (AMI) approach is introduced to achieve adipose tissue-specific long-term Mg supplement. AMI promotes M2 macrophage polarization and thermogenesis and ameliorates obesity in mice. Taken together, a reciprocal regulation of thermogenic adipocytes and M2 macrophages important for thermogenesis is identified, and AMI is offered as a promising strategy against obesity.}, }
@article {pmid39514976, year = {2024}, author = {Ji, L and Yi, L and Huang, C and Li, H and Han, W and Zhang, N}, title = {Classification of hand movements from EEG using a FusionNet based LSTM network.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad905d}, pmid = {39514976}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Hand/physiology ; *Movement/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; }, abstract = {Objective. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.Approach. This paper introduces a novel multi-model fusion approach, FusionNet-Long Short-Term Memory (LSTM), designed to address these issues. Specifically, it integrates Convolutional Neural Networks for spatial feature extraction, Gated Recurrent Units and LSTM networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics.Main results. Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model.Significance. This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems.}, }
@article {pmid39513467, year = {2024}, author = {Nie, A and Li, M and Wang, Q and Zhang, C}, title = {The isolation between part-set cues and social collaboration in episodic memory is dependent: Insight from ongoing and post-collaboration.}, journal = {Scandinavian journal of psychology}, volume = {65}, number = {6}, pages = {981-999}, doi = {10.1111/sjop.13042}, pmid = {39513467}, issn = {1467-9450}, support = {202303021221150//Natural Science Foundation of Shanxi Province of China/ ; 2024-090//Research Project Supported by Shanxi Scholarship Council of China/ ; 21YJA190005//Humanities and Social Sciences, Ministry of Education of China/ ; LY21C090002//Zhejiang Provincial Natural Science Foundation of China/ ; 31300831//National Natural Science Foundation of China/ ; //Initial Scientific Research Fund of Shanxi Normal University/ ; }, mesh = {Humans ; *Cues ; Female ; Male ; *Memory, Episodic ; *Mental Recall/physiology ; Young Adult ; Adult ; *Social Interaction ; Cooperative Behavior ; Stereotyping ; Emotions/physiology ; }, abstract = {It has been demonstrated that both part-set cues and social interaction can produce detrimental effects on memory. Specifically, part-set cues lead to part-set cueing impairment, while social interaction can result in collaborative inhibition. However, there is less evidence on whether these factors have isolated or comparable impacts on memory. Additionally, it is still unknown whether the effects behave similarly on item memory and source memory, whether the effects are comparable between ongoing and post-social collaboration, and whether stimulus features influence their respective roles. To address these issues, we conducted the current experiment where participants were exposed to gender stereotype-consistent or -inconsistent words, categorized as positive, neutral, or negative. The words were read out by either a male or a female. Two recall sessions were conducted: Recall 1 was carried out either individually or collaboratively, whereas Recall 2 was always collaborative. Some participants performed Recall 1 under the part-set cued condition while others were under the no-cued condition. Both item memory and source memory were assessed in both recall sessions. The results have three implications. First, during the ongoing collaborative session, two effects were observed on item memory: part-set cueing impairment and collaborative inhibition. Further, the contributions elicited by part-set cues and social collaboration are isolated. The part-set cueing impairment was influenced by both emotional valence and stereotypical consistency. Second, post-collaboration analysis indicated that both the utilization of part-set cues and collaboration itself enhanced item memory, resulting in the part-set cueing enhancement and post-collaborative memory benefit. Additionally, there was evidence indicating that the mechanisms prompted by these two factors intertwined when emotional valence and stereotypical consistency were considered. Third, in both ongoing and post-collaboration scenarios, the detrimental and beneficial effects on item memory and source memory exhibited different patterns, thereby supporting the dual-process models. These findings enhance our comprehension of the insolation and the interplay between part-set cues and collaboration in memory.}, }
@article {pmid39511344, year = {2025}, author = {Fan, Y and Wang, M and Fang, F and Ding, N and Luo, H}, title = {Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory.}, journal = {Nature human behaviour}, volume = {9}, number = {2}, pages = {360-375}, pmid = {39511344}, issn = {2397-3374}, support = {2023M740124//China Postdoctoral Science Foundation/ ; T2421004//Science Fund for Creative Research Groups (Fund for Creative Research Groups)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32222035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930052//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Memory, Short-Term/physiology ; *Magnetoencephalography ; *Electroencephalography ; Male ; Adult ; Female ; Young Adult ; Prefrontal Cortex/physiology/diagnostic imaging ; Brain Mapping/methods ; Temporal Lobe/physiology/diagnostic imaging ; Parietal Lobe/physiology/diagnostic imaging ; }, abstract = {Working memory (WM) is constructive in nature. Instead of passively retaining information, WM reorganizes complex sequences into hierarchically embedded chunks to overcome capacity limits and facilitate flexible behaviour. Here, to investigate the neural mechanisms underlying hierarchical reorganization in WM, we performed two electroencephalography and one magnetoencephalography experiments, wherein humans retain in WM a temporal sequence of items, that is, syllables, which are organized into chunks, that is, multisyllabic words. We demonstrate that the one-dimensional sequence is represented by two-dimensional neural representational geometry in WM arising from left prefrontal and temporoparietal regions, with separate dimensions encoding item position within a chunk and chunk position in the sequence. Critically, this two-dimensional geometry is observed consistently in different experimental settings, even during tasks not encouraging hierarchical reorganization in WM and correlates with WM behaviour. Overall, these findings strongly support that complex sequences are reorganized into factorized multidimensional neural representational geometry in WM, which also speaks to general structure-based organizational principles given WM's involvement in many cognitive functions.}, }
@article {pmid39511257, year = {2024}, author = {Xue, S and Jin, B and Jiang, J and Guo, L and Liu, J}, title = {A hybrid local-global neural network for visual classification using raw EEG signals.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {27170}, pmid = {39511257}, issn = {2045-2322}, support = {2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Photic Stimulation ; }, abstract = {EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.}, }
@article {pmid39509814, year = {2025}, author = {Li, X and Wei, W and Qiu, S and He, H}, title = {A temporal-spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {181}, number = {}, pages = {106844}, doi = {10.1016/j.neunet.2024.106844}, pmid = {39509814}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Male ; Adult ; Visual Perception/physiology ; Photic Stimulation/methods ; Brain/physiology ; }, abstract = {The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.}, }
@article {pmid39509308, year = {2025}, author = {Liu, D and Ding, Q and Tong, C and Ai, J and Wang, F}, title = {Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {2}, pages = {984-995}, doi = {10.1109/JBHI.2024.3491096}, pmid = {39509308}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Algorithms ; *Principal Component Analysis ; Machine Learning ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.}, }
@article {pmid39508555, year = {2024}, author = {Shah, NP and Phillips, AJ and Madugula, S and Lotlikar, A and Gogliettino, AR and Hays, MR and Grosberg, L and Brown, J and Dusi, A and Tandon, P and Hottowy, P and Dabrowski, W and Sher, A and Litke, AM and Mitra, S and Chichilnisky, EJ}, title = {Precise control of neural activity using dynamically optimized electrical stimulation.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {39508555}, issn = {2050-084X}, support = {2146755//National Science Foundation Graduate Research Fellowship/ ; 1828993//National Science Foundation/ ; F30-EY-030776-03/EY/NEI NIH HHS/United States ; T32MH-020016/MH/NIMH NIH HHS/United States ; F31-EY-033636/EY/NEI NIH HHS/United States ; DEC-2013/10/M/NZ4/00268//Polish Academy of Sciences/ ; R01-EY021271/EY/NEI NIH HHS/United States ; R01-EY029247/EY/NEI NIH HHS/United States ; P30-EY019005/EY/NEI NIH HHS/United States ; NSF/CRCNS//National Science Foundation/ ; }, mesh = {Animals ; Rats ; *Electric Stimulation/methods ; *Retinal Ganglion Cells/physiology ; Photic Stimulation ; Macaca ; Electrodes, Implanted ; Visual Prosthesis ; Macaca mulatta ; }, abstract = {Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.}, }
@article {pmid39508456, year = {2024}, author = {Park, S and Lipton, M and Dadarlat, MC}, title = {Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad83c0}, pmid = {39508456}, issn = {1741-2552}, mesh = {Animals ; *Deep Learning ; Mice ; *Movement/physiology ; *Calcium/metabolism ; Neurons/physiology ; Brain-Computer Interfaces ; Mice, Inbred C57BL ; Male ; Extremities/innervation/physiology ; }, abstract = {Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.}, }
@article {pmid39507693, year = {2024}, author = {Zhang, L and Xia, J and Li, B and Cao, Z and Dong, S}, title = {Multimodal integrated flexible neural probe for in situ monitoring of EEG and lactic acid.}, journal = {RSC advances}, volume = {14}, number = {48}, pages = {35520-35528}, pmid = {39507693}, issn = {2046-2069}, abstract = {In physiological activities, the brain's electroencephalogram (EEG) signal and chemical concentration change are crucial for diagnosing and treating neurological disorders. Despite the advantages of flexible neural probes, such as their flexibility and biocompatibility, it remains a challenge to achieve in situ monitoring of electrophysiological and chemical signals on a small scale simultaneously. This study developed a new method to construct an efficient dual-sided multimodal integrated flexible neural probe, which combines a density electrode array for EEG recordings and an electrochemical sensor for detecting lactic acid. The EEG electrode array includes a 6-channel recording electrode array with each electrode 30 × 50 μm in size, and the lactic acid sensor with overall contact is approximately 100 μm wide. The EEG electrodes have an average impedance of 2.57 kΩ at 1 kHz and remained stable after immersing in NS (normal saline) for 3 months. The lactic acid sensor showed a sensitivity of 52.8 nA mM[-1]. The in vivo experiments demonstrated that the probe can reliably monitor electrophysiological signals. The probe is able to be implanted into the desired site with the help of a guide port. This flexible neural probe can provide more comprehensive insights into brain activity in the field of neuroscience and clinical practices.}, }
@article {pmid39506628, year = {2024}, author = {Theofanopoulou, C and Paez, S and Huber, D and Todd, E and Ramírez-Moreno, MA and Khaleghian, B and Sánchez, AM and Barceló, L and Gand, V and Contreras-Vidal, JL}, title = {Mobile brain imaging in butoh dancers: from rehearsals to public performance.}, journal = {BMC neuroscience}, volume = {25}, number = {1}, pages = {62}, pmid = {39506628}, issn = {1471-2202}, mesh = {Humans ; *Dancing/physiology ; *Electroencephalography/methods ; *Brain/physiology ; Female ; Male ; Adult ; Brain-Computer Interfaces ; Young Adult ; }, abstract = {BACKGROUND: Dissecting the neurobiology of dance would shed light on a complex, yet ubiquitous, form of human communication. In this experiment, we sought to study, via mobile electroencephalography (EEG), the brain activity of five experienced dancers while dancing butoh, a postmodern dance that originated in Japan.<br><br>RESULTS: We report the experimental design, methods, and practical execution of a highly interdisciplinary project that required the collaboration of dancers, engineers, neuroscientists, musicians, and multimedia artists, among others. We explain in detail how we technically validated all our EEG procedures (e.g., via impedance value monitoring) and minimized potential artifacts in our recordings (e.g., via electrooculography and inertial measurement units). We also describe the engineering details and hardware that enabled us to achieve synchronization between signals recorded at different sampling frequencies, along with a signal preprocessing and denoising pipeline that we used for data re-sampling and power line noise removal. As our experiment culminated in a live performance, where we generated a real-time visualization of the dancers' interbrain synchrony on a screen via an artistic brain-computer interface, we outline all the methodology (e.g., filtering, time-windows, equation) we used for online bispectrum estimations. Additionally, we provide access to all the raw EEG data and codes we used in our recordings. We, lastly, discuss how we envision that the data could be used to address several hypotheses, such as that of interbrain synchrony or the motor theory of vocal learning.<br><br>CONCLUSIONS: Being, to our knowledge, the first study to report synchronous and simultaneous recording from five dancers, we expect that our findings will inform future art-science collaborations, as well as dance-movement therapies.}, }
@article {pmid39505863, year = {2024}, author = {Hu, N and Shi, JX and Chen, C and Xu, HH and Chang, ZH and Hu, PF and Guo, D and Zhang, XW and Shao, WW and Fan, X and Zuo, JC and Ming, D and Li, XH}, title = {Constructing organoid-brain-computer interfaces for neurofunctional repair after brain injury.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9580}, pmid = {39505863}, issn = {2041-1723}, mesh = {*Brain-Computer Interfaces ; *Organoids ; Animals ; *Brain Injuries/therapy/physiopathology ; *Brain/physiology ; Humans ; Male ; Neurons/physiology ; Mice ; }, abstract = {The reconstruction of damaged neural circuits is critical for neurological repair after brain injury. Classical brain-computer interfaces (BCIs) allow direct communication between the brain and external controllers to compensate for lost functions. Importantly, there is increasing potential for generalized BCIs to input information into the brains to restore damage, but their effectiveness is limited when a large injured cavity is caused. Notably, it might be overcome by transplantation of brain organoids into the damaged region. Here, we construct innovative BCIs mediated by implantable organoids, coined as organoid-brain-computer interfaces (OBCIs). We assess the prolonged safety and feasibility of the OBCIs, and explore neuroregulatory strategies. OBCI stimulation promotes progressive differentiation of grafts and enhances structural-functional connections within organoids and the host brain, promising to repair the damaged brain via regenerating and regulating, potentially directing neurons to preselected targets and recovering functional neural networks in the future.}, }
@article {pmid39504276, year = {2024}, author = {Yang, X and Xiong, X and Li, X and Lian, Q and Zhu, J and Zhang, J and Qi, Y and Wang, Y}, title = {Reconstructing Multi-Stroke Characters From Brain Signals Toward Generalizable Handwriting Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {4230-4239}, doi = {10.1109/TNSRE.2024.3492191}, pmid = {39504276}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; Male ; *Motor Cortex/physiology ; *Stroke/physiopathology ; Female ; *Algorithms ; Electroencephalography/methods ; Adult ; Young Adult ; Paralysis/rehabilitation ; }, abstract = {Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large character sets. The Chinese characters, including over 3500 commonly used characters with 10.3 strokes per character on average, represent a highly complex writing system. This paper proposes a Chinese handwriting BCI system, which reconstructs multi-stroke handwriting trajectories from brain signals. Through the recording of cortical neural signals from the motor cortex, we reveal distinct neural representations for stroke-writing and pen-lift phases. Leveraging this finding, we propose a stroke-aware approach to decode stroke-writing trajectories and pen-lift movements individually, which can reconstruct recognizable characters (accuracy of 86% with 400 characters). Our approach demonstrates high stability over 5 months, shedding light on generalized and adaptable handwriting BCIs.}, }
@article {pmid39504275, year = {2025}, author = {Jia, T and Mo, L and McGeady, C and Sun, J and Liu, A and Ji, L and Xi, J and Li, C}, title = {Cortical Activation Patterns Determine Effectiveness of rTMS-Induced Motor Imagery Decoding Enhancement in Stroke Patients.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {3}, pages = {1200-1208}, doi = {10.1109/TBME.2024.3492977}, pmid = {39504275}, issn = {1558-2531}, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Middle Aged ; *Stroke/physiopathology/therapy/diagnostic imaging ; *Stroke Rehabilitation/methods ; *Electroencephalography/methods ; Aged ; *Brain-Computer Interfaces ; Imagination/physiology ; Adult ; Motor Cortex/physiopathology/diagnostic imaging/physiology ; }, abstract = {Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients' individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement. We applied four kinds of rTMS treatments respectively to four groups of subacute stroke patients, twenty-six patients in total, and observed their EEG dynamics, MI decoding performance, and Fugl-Meyer assessment changes following 2-week neuromodulation. Four treatments consisted of ipsilesional 10 Hz rTMS, contralesional 1 Hz rTMS, ipsilesional 1 Hz rTMS, and sham stimulation. Results showed stroke patients with different neural reorganization patterns responded individually to rTMS therapy. Patients with cortical lesions mostly showed contralesional recruitment and patients without cortical lesions mostly presented ipsilesional focusing. Significant activation increases in the ipsilesional hemisphere (pre: -15.7% ∓ 8.2%, post: -17.3% ∓ 8.1%, p = 0.037) and MI decoding accuracy enhancement (pre: 76.3 ± 13.8%, post: 86.6 ± 8.2%, p = 0.037) were concurrently found in no-cortical-lesion patients with ipsilesional activation treatment. In the group of patients without cortical lesions, recovery rate in those receiving ipsilesional activation therapy (23.5 ± 10.4%) was higher than those receiving ipsilesional suppression therapy (9.9 ± 9.3%) (p = 0.041). This study reveals that tailoring neuromodulation therapy by recognizing cortical activation patterns is promising for improving effectiveness of the combination therapy with BCI and rTMS.}, }
@article {pmid39504274, year = {2025}, author = {Jin, J and Zhao, X and Daly, I and Li, S and Wang, X and Cichocki, A and Jung, TP}, title = {A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-Related Potentials.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {3}, pages = {1188-1199}, doi = {10.1109/TBME.2024.3492506}, pmid = {39504274}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Algorithms ; Signal Processing, Computer-Assisted ; Evoked Potentials/physiology ; Brain/physiology ; Event-Related Potentials, P300/physiology ; }, abstract = {OBJECTIVE: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.<br><br>METHODS: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF). Additionally, we proposed a "Lock a Target by Two Flashes" (LT2F) method to offer more versatile stimulus flash rules, complementing the row and column (RC) and single character (SC) modes. We applied the "Sub and Global" multi-window mode to EEGNet (mwEEGNet) to enhance classification and explored the performance of eight other representative algorithms.<br><br>RESULTS: Twenty healthy volunteers participated in the experiments. Our analysis revealed that our proposed pattern elicited more pronounced negative peaks in the parietal and occipital brain regions between 200 ms and 230 ms post-stimulus onset compared with the TF pattern. Compared to the TF pattern, the GB pattern yielded a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) when using mwEEGNet. Furthermore, results demonstrated that mwEEGNet outperformed other methods in classification performance.<br><br>CONCLUSION AND SIGNIFICANCE: These results underscore the significance of our work in advancing ERP-based BCIs.}, }
@article {pmid39502788, year = {2024}, author = {Ahn, M and Edelman, BJ and He, B and Müller-Putz, GR and Röhrbein, F}, title = {Editorial: Advances in hybrid and application-driven BCI systems.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1498196}, doi = {10.3389/fnhum.2024.1498196}, pmid = {39502788}, issn = {1662-5161}, }
@article {pmid39501690, year = {2024}, author = {Ji, M and Lee, D and Lee, S}, title = {Effects of wearing a KF94 face mask on performance, perceptual parameters, and physiological responses during resistance exercise.}, journal = {Physical activity and nutrition}, volume = {28}, number = {3}, pages = {17-26}, pmid = {39501690}, issn = {2733-7545}, abstract = {PURPOSE: Wearing face masks in indoor public places, including fitness centers, is an effective strategy for preventing the airborne transmission of viruses. Despite this, limited research has addressed the effects of wearing a mask during resistance exercise, which is primarily performed in indoor fitness centers. This study investigated the effects of wearing a KF94 face mask on exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses during resistance exercise.<br><br>METHODS: Twenty young men (23.8 &#xb1; 0.5 years old) participated in this randomized crossover trial. The participants performed moderate-intensity resistance exercise (60% of 1RM) sessions under two different conditions (KF94 mask vs. no mask). Cardiorespiratory parameters, exercise volume, rating of perceived exertion (RPE), and dyspnea were measured. Blood lactate concentration, blood pressure, arterial stiffness, and perceptual parameters were measured pre- and post-exercise.<br><br>RESULTS: Wearing the KF94 mask significantly reduced exercise volume, ventilation volume, and ventilation efficiency compared to exercising without a mask (p < 0.05). Although blood lactate concentration remained unchanged between the two conditions, RPE and dyspnea were significantly higher with the KF94 mask (p < 0.01). Central arterial stiffness post-exercise was significantly higher with the KF94 mask than without it (p < 0.01).<br><br>CONCLUSION: Wearing a KF94 face mask during resistance exercise affected exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses. These findings suggest that coaches and trainers should consider the individual characteristics when designing exercise prescriptions and modifying resistance exercise variables while wearing KF94 masks.}, }
@article {pmid39500903, year = {2024}, author = {Li, J and Zhang, F and Xia, X and Zhang, K and Wu, J and Liu, Y and Zhang, C and Cai, X and Lu, J and Xu, L and Wan, R and Hazarika, D and Xuan, W and Chen, J and Cao, Z and Li, Y and Jin, H and Dong, S and Zhang, S and Ye, Z and Yang, M and Chen, PY and Luo, J}, title = {An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9557}, pmid = {39500903}, issn = {2041-1723}, mesh = {*Intracranial Pressure/physiology ; Monitoring, Physiologic/instrumentation/methods ; *Heart Rate/physiology ; Animals ; Humans ; Respiratory Rate ; Male ; Transducers, Pressure ; }, abstract = {The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.}, }
@article {pmid39500053, year = {2024}, author = {Kocanaogullari, D and Gall, R and Mak, J and Huang, X and Mullen, K and Ostadabbas, S and Wittenberg, GF and Grattan, ES and Akcakaya, M}, title = {Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8efc}, pmid = {39500053}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Perceptual Disorders/diagnosis/physiopathology/etiology ; *Stroke/physiopathology/complications/diagnosis ; Male ; Female ; Brain-Computer Interfaces ; Middle Aged ; Aged ; Severity of Illness Index ; }, abstract = {Objective.We aim to assess the severity of spatial neglect (SN) through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test-conventional lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined spatio-temporal network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with SN. We also propose a FOV correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.Main results.Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.Significance.These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical settings.}, }
@article {pmid39500051, year = {2024}, author = {Dehais, F and Cabrera Castillos, K and Ladouce, S and Clisson, P}, title = {Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8ef7}, pmid = {39500051}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adult ; Young Adult ; *Photic Stimulation/methods ; }, abstract = {Objective.Reactive brain-computer interfaces typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-orientedGabororRickerpatches that optimize foveal neural response while reducing peripheral distraction.Approach.In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditionalPlainflickers,Gabor-based, orRicker-based flickers. These flickers were part of a five-class code visually evoked potentials paradigm featuring low frequency, short, and aperiodic visual flashes.Main results.Subjective ratings revealed thatGaborandRickerstimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover,GaborandRicker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 s of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 s.Significance.This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.}, }
@article {pmid39500044, year = {2024}, author = {Tremmel, C and Krusienski, DJ and Schraefel, M}, title = {Estimating cognitive workload using a commercial in-ear EEG headset.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8ef8}, pmid = {39500044}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Workload ; Female ; *Cognition/physiology ; Adult ; Young Adult ; }, abstract = {Objective.This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'.Approach.Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance ofγband activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.Main results.Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequencyγband features can improve workload estimation.Significance.The application of EEG-based Brain-Computer Interfaces beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.}, }
@article {pmid39496663, year = {2024}, author = {Agrawal, R and Dhule, C and Shukla, G and Singh, S and Agrawal, U and Alsubaie, N and Alqahtani, MS and Abbas, M and Soufiene, BO}, title = {Design of EEG based thought identification system using EMD &amp; deep neural network.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {26621}, pmid = {39496663}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Brain/physiology/physiopathology ; }, abstract = {Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary &amp; non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database &amp; 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.}, }
@article {pmid39496200, year = {2024}, author = {Bunterngchit, C and Wang, J and Su, J and Wang, Y and Liu, S and Hou, ZG}, title = {Temporal attention fusion network with custom loss function for EEG-fNIRS classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8e86}, pmid = {39496200}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Spectroscopy, Near-Infrared/methods ; *Attention/physiology ; Male ; Adult ; Female ; Young Adult ; Neural Networks, Computer ; Epilepsy/physiopathology/diagnosis ; }, abstract = {Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG-fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main results.Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.}, }
@article {pmid39494592, year = {2024}, author = {Zhu, Y and Ma, J and Li, Y and Gu, M and Feng, X and Shao, Y and Tan, L and Lou, HF and Sun, L and Liu, Y and Zeng, LH and Qiu, Z and Li, XM and Duan, S and Yu, YQ}, title = {Adenosine-Dependent Arousal Induced by Astrocytes in a Brainstem Circuit.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {48}, pages = {e2407706}, pmid = {39494592}, issn = {2198-3844}, support = {2021ZD0203400//STI2030-Major Projects/ ; T2293733//National Natural Science Foundation of China Major Project/ ; T2293730//National Natural Science Foundation of China Major Project/ ; 31970939//National Natural Science Foundation of China Major Project/ ; 82288101//National Natural Science Foundation of China Major Project/ ; 82090033//National Natural Science Foundation of China Major Project/ ; U20A6005//National Natural Science Foundation of China Major Project/ ; 32171007//National Natural Science Foundation of China Major Project/ ; 32100814//National Natural Science Foundation of China Major Project/ ; LZ22H090001//Natural Science Foundation of Zhejiang Province/ ; 2024SSYS0019//Key R&amp;D Program of Zhejiang Province/ ; 2022C03034 to Y.Q.Y//Key R&amp;D Program of Zhejiang Province/ ; 2024SSYS0017//Key R&amp;D Program of Zhejiang Province/ ; 2020C03009//Key R&amp;D Program of Zhejiang Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2019B030335001//Key R&amp;D Program of Guangdong Province/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; }, mesh = {*Astrocytes/metabolism ; Animals ; *Adenosine/metabolism ; *Arousal/physiology ; Mice ; *Brain Stem/metabolism/physiology ; Male ; Sleep/physiology ; Wakefulness/physiology ; Models, Animal ; }, abstract = {Astrocytes play a crucial role in regulating sleep-wake behavior. However, how astrocytes govern a specific sleep-arousal circuit remains unknown. Here, the authors show that parafacial zone (PZ) astrocytes responded to sleep-wake cycles with state-differential Ca[2+] activity, peaking during transitions from sleep to wakefulness. Using chemogenetic and optogenetic approaches, they find that activating PZ astrocytes elicited and sustained wakefulness by prolonging arousal episodes while impeding transitions from wakefulness to non-rapid eye movement (NREM) sleep. Activation of PZ astrocytes specially induced the elevation of extracellular adenosine through the ATP hydrolysis pathway but not equilibrative nucleoside transporter (ENT) mediated transportation. Strikingly, the rise in adenosine levels induced arousal by activating A1 receptors, suggesting a distinct role for adenosine in the PZ beyond its conventional sleep homeostasis modulation observed in the basal forebrain (BF) and cortex. Moreover, at the circuit level, PZ astrocyte activation induced arousal by suppressing the GABA release from the PZ[GABA] neurons, which promote NREM sleep and project to the parabrachial nucleus (PB). Thus, their study unveils a distinctive arousal-promoting effect of astrocytes within the PZ through extracellular adenosine and elucidates the underlying mechanism at the neural circuit level.}, }
@article {pmid39493873, year = {2024}, author = {Han, J and Wang, R and Wang, M and Yu, Z and Zhu, L and Zhang, J and Zhu, J and Zhang, S and Xi, W and Wu, H}, title = {Dynamic lateralization in contralateral-projecting corticospinal neurons during motor learning.}, journal = {iScience}, volume = {27}, number = {11}, pages = {111078}, pmid = {39493873}, issn = {2589-0042}, abstract = {Understanding the adaptability of the motor cortex in response to bilateral motor tasks is crucial for advancing our knowledge of neural plasticity and motor learning. Here we aim to investigate the dynamic lateralization of contralateral-projecting corticospinal neurons (cpCSNs) during such tasks. Utilizing in vivo two-photon calcium imaging, we observe cpCSNs in mice performing a "left-right" lever-press task. Our findings reveal heterogeneous populational dynamics in cpCSNs: a marked decrease in activity during ipsilateral motor learning, in contrast to maintained activity during contralateral motor learning. Notably, individual cpCSNs show dynamic shifts in engagement with ipsilateral and contralateral movements, displaying an evolving pattern of activation over successive days. It suggests that cpCSNs exhibit adaptive changes in activation patterns in response to ipsilateral and contralateral movements, highlighting a flexible reorganization during motor learning This reconfiguration underscores the dynamic nature of cortical lateralization in motor learning and offers insights for neuromotor rehabilitation.}, }
@article {pmid39490945, year = {2024}, author = {Gwon, D and Ahn, M}, title = {Motor task-to-task transfer learning for motor imagery brain-computer interfaces.}, journal = {NeuroImage}, volume = {302}, number = {}, pages = {120906}, doi = {10.1016/j.neuroimage.2024.120906}, pmid = {39490945}, issn = {1095-9572}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; Adult ; Young Adult ; *Electroencephalography/methods ; Transfer, Psychology/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Movement/physiology ; }, abstract = {Motor imagery (MI) is one of the popular control paradigms in the non-invasive brain-computer interface (BCI) field. MI-BCI generally requires users to conduct the imagination of movement (e.g., left or right hand) to collect training data for generating a classification model during the calibration phase. However, this calibration phase is generally time-consuming and tedious, as users conduct the imagination of hand movement several times without being given feedback for an extended period. This obstacle makes MI-BCI non user-friendly and hinders its use. On the other hand, motor execution (ME) and motor observation (MO) are relatively easier tasks, yield lower fatigue than MI, and share similar neural mechanisms to MI. However, few studies have integrated these three tasks into BCIs. In this study, we propose a new task-to-task transfer learning approach of 3-motor tasks (ME, MO, and MI) for building a better user-friendly MI-BCI. For this study, 28 subjects participated in 3-motor tasks experiment, and electroencephalography (EEG) was acquired. User opinions regarding the 3-motor tasks were also collected through questionnaire survey. The 3-motor tasks showed a power decrease in the alpha rhythm, known as event-related desynchronization, but with slight differences in the temporal patterns. In the classification analysis, the cross-validated accuracy (within-task) was 67.05 % for ME, 65.93 % for MI, and 73.16 % for MO on average. Consistently with the results, the subjects scored MI (3.16) as the most difficult task compared with MO (1.42) and ME (1.41), with p < 0.05. In the analysis of task-to-task transfer learning, where training and testing are performed using different task datasets, the ME-trained model yielded an accuracy of 65.93 % (MI test), which is statistically similar to the within-task accuracy (p > 0.05). The MO-trained model achieved an accuracy of 60.82 % (MI test). On the other hand, combining two datasets yielded interesting results. ME and 50 % of the MI-trained model (50-shot) classified MI with a 69.21 % accuracy, which outperformed the within-task accuracy (p < 0.05), and MO and 50 % of the MI-trained model showed an accuracy of 66.75 %. Of the low performers with a within-task accuracy of 70 % or less, 90 % (n = 21) of the subjects improved in training with ME, and 76.2 % (n = 16) improved in training with MO on the MI test at 50-shot. These results demonstrate that task-to-task transfer learning is possible and could be a promising approach to building a user-friendly training protocol in MI-BCI.}, }
@article {pmid39490524, year = {2024}, author = {Pirelli, L and Grubb, KJ and George, I and Goldsweig, AM and Nazif, TM and Dahle, G and Myers, PO and Ouzounian, M and Szeto, WY and Maisano, F and Geirsson, A and Vahl, TP and Kodali, SK and Kaneko, T and Tang, GHL}, title = {The role of cardiac surgeons in transcatheter structural heart disease interventions: The evolution of cardiac surgery.}, journal = {The Journal of thoracic and cardiovascular surgery}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jtcvs.2024.10.037}, pmid = {39490524}, issn = {1097-685X}, }
@article {pmid39490518, year = {2024}, author = {Ahmed Taha, B and Addie, AJ and Saeed, AQ and Haider, AJ and Chaudhary, V and Arsad, N}, title = {Nanostructured Photonics Probes: A Transformative Approach in Neurotherapeutics and Brain Circuitry.}, journal = {Neuroscience}, volume = {562}, number = {}, pages = {106-124}, doi = {10.1016/j.neuroscience.2024.10.046}, pmid = {39490518}, issn = {1873-7544}, mesh = {Humans ; *Nanostructures ; *Brain/physiology ; Animals ; Optics and Photonics/methods/instrumentation ; Nanotechnology/methods ; Optogenetics/methods/instrumentation ; }, abstract = {Neuroprobes that use nanostructured photonic interfaces are capable of multimodal sensing, stimulation, and imaging with unprecedented spatio-temporal resolution. In addition to electrical recording, optogenetic modulation, high-resolution optical imaging, and molecular sensing, these advanced probes combine nanophotonic waveguides, optical transducers, nanostructured electrodes, and biochemical sensors. The potential of this technology lies in unraveling the mysteries of neural coding principles, mapping functional connectivity in complex brain circuits, and developing new therapeutic interventions for neurological disorders. Nevertheless, achieving the full potential of nanostructured photonic neural probes requires overcoming challenges such as ensuring long-term biocompatibility, integrating nanoscale components at high density, and developing robust data-analysis pipelines. In this review, we summarize and discuss the role of photonics in neural probes, trends in electrode diameter for neural interface technologies, nanophotonic technologies using nanostructured materials, advances in nanofabrication photonics interface engineering, and challenges and opportunities. Finally, interdisciplinary efforts are required to unlock the transformative potential of next-generation neuroscience therapies.}, }
@article {pmid39487147, year = {2024}, author = {Wang, X and Wu, S and Yang, H and Bao, Y and Li, Z and Gan, C and Deng, Y and Cao, J and Li, X and Wang, Y and Ren, C and Yang, Z and Zhao, Z}, title = {Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9442}, pmid = {39487147}, issn = {2041-1723}, mesh = {Animals ; *Electrodes, Implanted ; Sheep ; Action Potentials/physiology ; Neurons/physiology ; Brain-Computer Interfaces ; Occipital Lobe/physiology ; }, abstract = {Although intracranial neural electrodes have significantly contributed to both fundamental research and clinical treatment of neurological diseases, their implantation requires invasive surgery to open craniotomies, which can introduce brain damage and disrupt normal brain functions. Recent emergence of endovascular neural devices offers minimally invasive approaches for neural recording and stimulation. However, existing endovascular neural devices are unable to resolve single-unit activity in large animal models or human patients, impeding a broader application as neural interfaces in clinical practice. Here, we present the ultraflexible implantable neural electrode as an intravascular device (uFINE-I) for recording brain activity at single-unit resolution. We successfully implanted uFINE-Is into the sheep occipital lobe by penetrating through the confluence of sinuses and recorded both local field potentials (LFPs) and multi-channel single-unit spiking activity under spontaneous and visually evoked conditions. Imaging and histological analysis revealed minimal tissue damage and immune response. The uFINE-I provides a practical solution for achieving high-resolution neural recording with minimal invasiveness and can be readily transferred to clinical settings for future neural interface applications such as brain-machine interfaces (BMIs) and the treatment of neurological diseases.}, }
@article {pmid39484299, year = {2024}, author = {Premchand, B and Liang, L and Phua, KS and Zhang, Z and Wang, C and Guo, L and Ang, J and Koh, J and Yong, X and Ang, KK}, title = {Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.}, journal = {NeuroSci}, volume = {5}, number = {4}, pages = {407-428}, pmid = {39484299}, issn = {2673-4087}, abstract = {Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.}, }
@article {pmid39484239, year = {2024}, author = {Leinders, S and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Geukes, SH and Schippers, A and Verberne, MSW and van den Boom, M and van der Vijgh, B and Crone, NE and Denison, T and Ramsey, NF and Vansteensel, MJ}, title = {DO NOT LOSE SLEEP OVER IT: IMPLANTED BRAIN-COMPUTER INTERFACE FUNCTIONALITY DURING NIGHTTIME IN LATE-STAGE AMYOTROPHIC LATERAL SCLEROSIS.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39484239}, support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) hold promise as augmentative and alternative communication technology for people with severe motor and speech impairment (locked-in syndrome) due to neural disease or injury. Although such BCIs should be available 24/7, to enable communication at all times, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an individual with amyotrophic lateral sclerosis (ALS) who was implanted with an electrocorticography-based BCI that enabled the generation of click-commands for spelling words and call-caregiver signals.<br><br>METHODS: We investigated nocturnal dynamics of neural signal features used for BCI control, namely low (LFB: 10-30Hz) and high frequency band power (HFB: 65-95Hz). Additionally, we assessed the nocturnal performance of a BCI decoder that was trained on daytime data by quantifying the number of unintentional BCI activations at night. Finally, we developed and implemented a nightmode decoder that allowed the participant to call a caregiver at night, and assessed its performance.<br><br>RESULTS: Power and variance in HFB and LFB were significantly higher at night than during the day in the majority of the nights, with HFB variance being higher in 88% of nights. Daytime decoders caused 245 unintended selection-clicks and 13 unintended caregiver-calls per hour when applied to night data. The developed nightmode decoder functioned error-free in 79% of nights over a period of &#xb1;1.5 years, allowing the user to reliably call the caregiver, with unintended activations occurring only once every 12 nights.<br><br>DISCUSSION: Reliable nighttime use of a BCI requires decoders that are adjusted to sleep-related signal changes. This demonstration of a reliable BCI nightmode and its long-term use by an individual with advanced ALS underscores the importance of 24/7 BCI reliability.<br><br>TRIAL REGISTRATION: This trial is registered in clinicaltrials.gov under number NCT02224469 (https://clinicaltrials.gov/study/NCT02224469?term=NCT02224469&amp;rank=1). Date of submission to registry: August 21, 2014. Enrollment of first participant: September 7, 2015.}, }
@article {pmid39483493, year = {2024}, author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Rivera-Flor, H and Fabriz-Ulhoa, PH and Fragoso-Dias, EA and de Andrade, RM and Delisle-Rodriguez, D and Bastos-Filho, TF}, title = {Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks.}, journal = {NeuroSci}, volume = {5}, number = {2}, pages = {169-183}, pmid = {39483493}, issn = {2673-4087}, abstract = {Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high-85 rpm and low-30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly (p < 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.}, }
@article {pmid39483271, year = {2024}, author = {Yang, Y and Li, Y and Tang, L and Li, J}, title = {Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.}, journal = {Precision chemistry}, volume = {2}, number = {10}, pages = {518-538}, pmid = {39483271}, issn = {2771-9316}, abstract = {Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.}, }
@article {pmid39483192, year = {2024}, author = {Buthut, M and Starke, G and Akmazoglu, TB and Colucci, A and Vermehren, M and van Beinum, A and Bublitz, C and Chandler, J and Ienca, M and Soekadar, SR}, title = {HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1489307}, pmid = {39483192}, issn = {1662-5161}, abstract = {Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses.}, }
@article {pmid39488002, year = {2024}, author = {Xu, Z and Khazaee, M and Duy Truong, N and Havenga, D and Nikpour, A and Ahnood, A and Kavehei, O}, title = {A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8dfe}, pmid = {39488002}, issn = {1741-2552}, mesh = {*Electrocorticography/methods/instrumentation ; *Telemetry/instrumentation/methods ; *Wireless Technology/instrumentation ; *Brain-Computer Interfaces ; Animals ; Endovascular Procedures/methods/instrumentation ; Equipment Design/methods ; Electric Power Supplies ; Electrodes, Implanted ; Humans ; }, abstract = {Objective. Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Currently, solutions for endovascular electrocorticography (ECoG) include a stent in the brain with sensing electrodes, a chest implant to accommodate electronic components to provide power and data telemetry, and a long (tens of centimeters) cable travel through vessels with a set of wires in between. Removing this long cable is the key to the clinical viability of eBCIS as it carries risks and limitations, especially for patients with fragile vasculature.Approach. This work introduces a wireless and leadless telemetry and power transfer solution for ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent, removing the need for long, intrusive cables.Main results. The optical telemetry achieves data transmission speeds of over 2 Mbit/s, capable of supporting 41 ECoG channels at a 2 kHz sampling rate with 24-bit resolution. The FUS power transfer system delivers up to 10 mW of power to the implant through the scalp(6 mm), skull(10 mm), and subdural space(5 mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.Significance. This leadless and wireless solution eliminates the need for long cables and auxiliary implants, potentially reducing complications and enhancing the clinical applicability of eBCIs. The proposed system represents a step forward in enabling safer and more effective ECoG for a broader range of patients.}, }
@article {pmid39486261, year = {2025}, author = {Sun, Y and Gao, Y and Shen, A and Sun, J and Chen, X and Gao, X}, title = {Creating ionic current pathways: A non-implantation approach to achieving cortical electrical signals for brain-computer interface.}, journal = {Biosensors &amp; bioelectronics}, volume = {268}, number = {}, pages = {116882}, doi = {10.1016/j.bios.2024.116882}, pmid = {39486261}, issn = {1873-4235}, mesh = {*Brain-Computer Interfaces ; Animals ; Swine ; Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Biosensing Techniques/instrumentation ; Signal-To-Noise Ratio ; Cerebral Cortex/physiology ; Humans ; Brain/physiology ; Electroencephalography/instrumentation ; }, abstract = {This study introduces a novel method for acquiring brain electrical signals comparable to intracranial recordings without the health risks associated with implanted electrodes. We developed a technique using ultrasonic tools to create micro-holes in the skull and insert hollow implants, preventing natural healing. This approach establishes an artificial ionic current path (AICP) using tissue fluid, facilitating signal transmission from the cortex to the scalp surface. Experiments were conducted on pigs to validate the method's effectiveness. We synchronized our recordings with perforated electrocorticography (ECoG) for comparison. The AICP method yielded signal quality comparable to implanted ECoG in the low-frequency range, with a significant improvement in signal-to-noise ratio for evoked potentials. Our results demonstrate that this non-invasive technique can acquire high-quality brain signals, offering potential applications in neurophysiology, clinical research, and brain-computer interfaces. This innovative approach of utilizing tissue fluid as a natural conduction path opens new avenues for brain signal acquisition and analysis.}, }
@article {pmid39485790, year = {2024}, author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Hao, X and Li, W and Lu, S and Zhu, H and Chen, L and Xu, K and Li, Y and Lu, J}, title = {A brain-to-text framework for decoding natural tonal sentences.}, journal = {Cell reports}, volume = {43}, number = {11}, pages = {114924}, doi = {10.1016/j.celrep.2024.114924}, pmid = {39485790}, issn = {2211-1247}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Brain/physiology ; *Language ; Female ; Adult ; Young Adult ; Bayes Theorem ; }, abstract = {Speech brain-computer interfaces (BCIs) directly translate brain activity into speech sound and text. Despite successful applications in non-tonal languages, the distinct syllabic structures and pivotal lexical information conveyed through tonal nuances present challenges in BCI decoding for tonal languages like Mandarin Chinese. Here, we designed a brain-to-text framework to decode Mandarin sentences from invasive neural recordings. Our framework dissects speech onset, base syllables, and lexical tones, integrating them with contextual information through Bayesian likelihood and a Viterbi decoder. The results demonstrate accurate tone and syllable decoding during naturalistic speech production. The overall word error rate (WER) for 10 offline-decoded tonal sentences with a vocabulary of 40 high-frequency Chinese characters is 21% (chance: 95.3%) averaged across five participants, and tone decoding accuracy reaches 93% (chance: 25%), surpassing previous intracranial Mandarin tonal syllable decoders. This study provides a robust and generalizable approach for brain-to-text decoding of continuous tonal speech sentences.}, }
@article {pmid39481863, year = {2025}, author = {Sellwood, D and McLeod, L and Williams, K and Brown, K and Pullin, G}, title = {Imagining alternative futures with augmentative and alternative communication: a manifesto.}, journal = {Medical humanities}, volume = {50}, number = {4}, pages = {620-623}, pmid = {39481863}, issn = {1473-4265}, support = {/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Humans ; *Disabled Persons ; *Communication Aids for Disabled ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Communication ; Imagination ; Forecasting ; }, abstract = {This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are mainly driven by technological developments-technologies usually being developed for different markets-and are often based on ableist assumptions. In online conversations and a discussion panel at the 2023 International Society for Augmentative and Alternative Communication conference, we explored alternative futures by adopting different starting positions. Our case is presented under five headings: questioning the dominance of predictions that artificial intelligence and brain-computer interfaces will define the future of AAC; resisting disability being framed medically, as a problem to be solved, yet acknowledging both the pleasures and pains of being disabled; declaring that people who use AAC-as cyborgs of necessity rather than choice-should have choice and ownership of our technologies; challenging notions of independence as the necessary end goal for disabled bodies and considering interdependence as a human right; imagining alternative futures in which all people who use AAC are accepted and embraced for our communication and self-expression. This manifesto is an invitation for further discussion, and we welcome responses. While our focus is AAC, and three of the authors use AAC, we believe that our stance could be relevant to other disability communities in turn. This paper is about who gets to imagine disability futures and whose voices are left out. It is about how uncritical these futures can be, often presuming values that disabled people, in all their diversity, may not share.}, }
@article {pmid39480049, year = {2024}, author = {Kaleem, MI and Javeed, S and Plog, BA and Gupta, VP and Ray, WZ}, title = {Restorative Treatments for Cervical Spinal Cord Injury, a Narrative Review.}, journal = {Clinical spine surgery}, volume = {37}, number = {9}, pages = {451-458}, doi = {10.1097/BSD.0000000000001699}, pmid = {39480049}, issn = {2380-0194}, mesh = {Humans ; *Spinal Cord Injuries/therapy ; Cervical Cord/injuries ; Recovery of Function ; Cervical Vertebrae ; Nerve Transfer/methods ; }, abstract = {STUDY DESIGN: A narrative review.<br><br>OBJECTIVE: To summarize relevant data from representative studies investigating upper limb restorative therapies for cervical spinal cord injury.<br><br>SUMMARY OF BACKGROUND DATA: Cervical spinal cord injury (SCI) is a debilitating condition resulting in tetraplegia, lifelong disability, and reduced quality of life. Given the dependence of all activities on hand function, patients with tetraplegia rank regaining hand function as one of their highest priorities. Recovery from cervical SCI is heterogeneous and often incomplete; currently, various novel therapies are under investigation to improve neurological function and eventually better quality of life in patients with tetraplegia.<br><br>METHODS: In this article, a narrative literature review was performed to identify treatment options targeting the restoration of function in patients with cervical SCI. Studies were included from available literature based on the availability of clinical data and whether they are applicable to restoration of arm and hand function in patients with cervical SCI.<br><br>RESULTS: We describe relevant studies including indications and outcomes with a focus on arm and hand function. Different treatment modalities described include nerve transfers, tendon transfers, spinal cord stimulation, functional electrical stimulation, non-invasive brain stimulation, brain-machine interfaces and neuroprosthetics, stem cell therapy, and immunotherapy. As the authors' institution leads one of the largest clinical trials on nerve transfers for cervical SCI, we also describe how patients undergoing nerve transfers are managed and followed at our center.<br><br>CONCLUSIONS: While complete recovery from cervical spinal cord injury may not be possible, novel therapies aimed at the restoration of upper limb motor function have made significant progress toward the realization of complete recovery.}, }
@article {pmid39479901, year = {2024}, author = {Druschel, LN and Kasthuri, NM and Song, SS and Wang, JJ and Hess-Dunning, A and Chan, ER and Capadona, JR}, title = {Cell-specific spatial profiling of targeted protein expression to characterize the impact of intracortical microelectrode implantation on neuronal health.}, journal = {Journal of materials chemistry. B}, volume = {12}, number = {47}, pages = {12307-12319}, pmid = {39479901}, issn = {2050-7518}, support = {I01 RX002611/RX/RRD VA/United States ; R01 NS131502/NS/NINDS NIH HHS/United States ; R01 NS110823/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; }, mesh = {Animals ; *Microelectrodes ; *Neurons/metabolism ; Rats ; Rats, Sprague-Dawley ; Male ; Nerve Tissue Proteins/metabolism ; Microtubule-Associated Proteins/metabolism ; }, abstract = {Intracortical microelectrode arrays (MEAs) can record neuronal activity and advance brain-computer interface (BCI) devices. Implantation of the invasive MEA kills local neurons, which has been documented using immunohistochemistry (IHC). Neuronal nuclear protein (NeuN), a protein that lines the nuclei of exclusively neuronal cells, has been used as a marker for neuronal health and survival for decades in neuroscience and neural engineering. NeuN staining is often used to describe the neuronal response to intracortical microelectrode array (MEA) implantation. However, IHC is semiquantitative, relying on intensity readings rather than directly counting expressed proteins. To supplement previous IHC studies, we evaluated the expression of proteins representing different aspects of neuronal structure or function: microtubule-associated protein 2 (MAP2), neurofilament light (NfL), synaptophysin (SYP), myelin basic protein (MBP), and oligodendrocyte transcription factor 2 (OLIG2) following a neural injury caused by intracortical MEA implantation. Together, these five proteins evaluate the cytoskeletal structure, neurotransmitter release, and myelination of neurons. To fully evaluate neuronal health in NeuN-positive (NeuN+) regions, we only quantified protein expression in NeuN+ regions, making this the first-ever cell-specific spatial profiling evaluation of targeted proteins by multiplex immunochemistry following MEA implantation. We performed our protein quantification along with NeuN IHC to compare the results of the two techniques directly. We found that NeuN immunohistochemical analysis does not show the same trends as MAP2, NfL, SYP, MBP, and OLIG2 expression. Further, we found that all five quantified proteins show a decreased expression pattern that aligns more with historic intracortical MEA recording performance.}, }
@article {pmid39476487, year = {2025}, author = {Craik, A and Dial, H and L Contreras-Vidal, J}, title = {Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad8d0a}, pmid = {39476487}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Scalp/physiology ; Deep Learning ; Neural Networks, Computer ; }, abstract = {Objective. Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eye-tracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech brain-computer interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG.Approach. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks.Main results. Statistically significant participant-independent decoding performance was achieved for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.Significance. These findings contribute significantly to the development of EEG-enabled speech synthesis by demonstrating the feasibility of decoding both discrete and continuous speech features from EEG signals, even in the presence of EMG artifacts. By addressing the challenges of EMG interference and optimizing deep learning models for speech decoding, this study lays a strong foundation for EEG-based speech BCIs.}, }
@article {pmid39476381, year = {2024}, author = {Sun, YH and Hu, BW and Tan, LH and Lin, L and Cao, SX and Wu, TX and Wang, H and Yu, B and Wang, Q and Lian, H and Chen, J and Li, XM}, title = {Posterior Basolateral Amygdala is a Critical Amygdaloid Area for Temporal Lobe Epilepsy.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {48}, pages = {e2407525}, pmid = {39476381}, issn = {2198-3844}, support = {82090030//National Natural Science Foundation of China/ ; 32071022//National Natural Science Foundation of China/ ; 81870898//National Natural Science Foundation of China/ ; 82090031//National Natural Science Foundation of China/ ; 82288101//National Natural Science Foundation of China/ ; 2019B030335001//Key-Area Research and Development Program of Guangdong Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2019YFA0110103//Ministry of Science and Technology/ ; 2021YFA1101700//National Key Research and Development Program of China/ ; LR18H090002//Zhejiang Provincial Natural Science Foundation/ ; 2021ZD0202700//STI2030-Major Projects/ ; 010904005//Grants from Nanhu Brain-computer Interface Institute/ ; }, mesh = {*Epilepsy, Temporal Lobe/physiopathology/pathology ; Animals ; Mice ; *Basolateral Nuclear Complex/metabolism ; *Disease Models, Animal ; Male ; Amygdala ; Neurons/metabolism ; }, abstract = {The amygdaloid complex consists of multiple nuclei and is a key node in controlling temporal lobe epilepsy (TLE) in both human and animal model studies. However, the specific nucleus in the amygdaloid complex and the neural circuitry governing seizures remain unknown. Here, it is discovered that activation of glutamatergic neurons in the posterior basolateral amygdala (pBLA) induces severe seizures and even mortality. The pBLA glutamatergic neurons project collateral connections to multiple brain regions, including the insular cortex (IC), bed nucleus of the stria terminalis (BNST), and central amygdala (CeA). Stimulation of pBLA-targeted IC neurons triggers seizures, whereas ablation of IC neurons suppresses seizures induced by activating pBLA glutamatergic neurons. GABAergic neurons in the BNST and CeA establish feedback inhibition on pBLA glutamatergic neurons. Deleting GABAergic neurons in the BNST or CeA leads to sporadic seizures, highlighting their role in balancing pBLA activity. Furthermore, pBLA neurons receive glutamatergic inputs from the ventral hippocampal CA1 (vCA1). Ablation of pBLA glutamatergic neurons mitigates both acute and chronic seizures in the intrahippocampal kainic acid-induced mouse model of TLE. Together, these findings identify the pBLA as a pivotal nucleus in the amygdaloid complex for regulating epileptic seizures in TLE.}, }
@article {pmid39475413, year = {2024}, author = {Pitt, KM}, title = {Development and preliminary evaluation of a grid design application for adults and children using scanning and bci-based augmentative and alternative communication.}, journal = {Assistive technology : the official journal of RESNA}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/10400435.2024.2415368}, pmid = {39475413}, issn = {1949-3614}, abstract = {Augmentative and alternative communication (AAC) supports offer communication aids for individuals with severe speech and physical impairments. This study presents the development and proof of concept for an iPad application designed to evaluate the design preferences of both adults and children for AAC scanning and emerging P300-brain-computer interface access to AAC (BCI-AAC), both of which utilize item highlighting. Developed through a multidisciplinary and iterative process, the application incorporates customizable highlighting methods and display options for spelling-based and pictorial symbol interfaces. Initial testing involved five participants, including two adults with physical impairments and three children without physical impairments. Participants created unique interface displays using picture overlays, motion, and other highlighting methods. Feedback indicated strong usability and enjoyment during application use. Recommendations included expanded sound options and pre-made templates. This study demonstrates preliminary proof of concept for the application and supports the need for further research to explore user preferences and optimize communication outcomes across various AAC modalities. While BCI-AAC technology remains in its early stages, its integration into this application helps promote user-centered BCI-AAC development.}, }
@article {pmid39473791, year = {2024}, author = {Zhu, Y and Bayin, C and Li, H and Shu, X and Deng, J and Yuan, H and Shen, H and Liang, Z and Li, Y}, title = {A flexible, stable, semi-dry electrode with low impedance for electroencephalography recording.}, journal = {RSC advances}, volume = {14}, number = {46}, pages = {34415-34427}, pmid = {39473791}, issn = {2046-2069}, abstract = {Brain-computer interfaces (BCIs) provide promising prospects for the field of healthcare and rehabilitation, presenting significant advantages for humanity. The development of electrodes that exhibit satisfactory performance characteristics, including high electrical conductivity, optimal comfort, and exceptional stability, is crucial for the effective implementation of electroencephalography (EEG) recording in BCI systems. The present study introduces a novel EEG electrode design that utilizes a composite material consisting of reduced graphene oxide (RGO) and polyurethane (PU) sponge. This electrode is characterized by its low impedance, stability, and flexibility. This work offers a high level of comfort while in touch with the skin and is designed to be user-friendly. Due to its notable moisturizing capacity, adaptable structure, and the presence of conductive RGO networks, the RGOPU semi-dry electrode exhibits a skin-contact impedance of less than 5.6 kΩ. This value is equivalent to that of a wet electrode and lower than that of a commercially available semi-dry electrode. The stability tests have demonstrated the outstanding electrical and mechanical performance of the material, hence confirming its suitability for long-term EEG recording. Additionally, the RGOPU semi-dry electrode demonstrates stable recording of EEG data and accurate detection of action potentials. Furthermore, the correlation coefficient between the RGOPU semi-dry electrode and wet electrodes exceeds 0.9. Additionally, it acquires electroencephalogram signals characterized by high signal-to-noise ratios (SNRs) in the context of alpha-wave and steady-state visual evoked potential (SSVEP) tests. The accuracy of the BCI is similar to that of wet electrodes, indicating a potential capability for sensing EEG in BCI applications.}, }
@article {pmid39469033, year = {2024}, author = {Hu, F and Wang, F and Bi, J and An, Z and Chen, C and Qu, G and Han, S}, title = {HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1479570}, pmid = {39469033}, issn = {1662-4548}, abstract = {INTRODUCTION: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging.<br><br>METHODS: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects.<br><br>RESULTS: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% &#xb1; 1.25% (SEED), 98.93% &#xb1; 1.45% (DEAP-arousal), and 98.57% &#xb1; 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology.<br><br>DISCUSSION: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.}, }
@article {pmid39468119, year = {2024}, author = {Keutayeva, A and Fakhrutdinov, N and Abibullaev, B}, title = {Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {25775}, pmid = {39468119}, issn = {2045-2322}, support = {OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; }, abstract = {Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.}, }
@article {pmid39466869, year = {2025}, author = {Ke, Y and Du, J and Liu, S and Ming, D}, title = {Corrections to "Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework".}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {33}, number = {}, pages = {1169}, doi = {10.1109/TNSRE.2024.3487206}, pmid = {39466869}, issn = {1558-0210}, mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Reproducibility of Results ; }, abstract = {In the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows.}, }
@article {pmid39466862, year = {2025}, author = {Wei, Y and Meng, J and Luo, R and Mai, X and Li, S and Xia, Y and Zhu, X}, title = {Action Observation With Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {3}, pages = {1085-1096}, doi = {10.1109/TBME.2024.3487133}, pmid = {39466862}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Young Adult ; Adult ; *Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {OBJECTIVE: The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding.<br><br>METHODS: Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding.<br><br>RESULTS: The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% &#xb1; 7.61% in the four-class scenario.<br><br>CONCLUSION AND SIGNIFICANCE: We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.}, }
@article {pmid39466848, year = {2024}, author = {Gordon, EC and Seth, AK}, title = {Ethical considerations for the use of brain-computer interfaces for cognitive enhancement.}, journal = {PLoS biology}, volume = {22}, number = {10}, pages = {e3002899}, pmid = {39466848}, issn = {1545-7885}, mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Cognition/physiology ; Brain/physiology ; Privacy ; }, abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of brain activity and the ability to control external devices. While often used for medical purposes, BCIs may also hold great promise for nonmedical purposes to unlock human neurocognitive potential. In this Essay, we discuss the prospects and challenges of using BCIs for cognitive enhancement, focusing specifically on invasive enhancement BCIs (eBCIs). We discuss the ethical, legal, and scientific implications of eBCIs, including issues related to privacy, autonomy, inequality, and the broader societal impact of cognitive enhancement technologies. We conclude that the development of eBCIs raises challenges far beyond practical pros and cons, prompting fundamental questions regarding the nature of conscious selfhood and about who-and what-we are, and ought, to be.}, }
@article {pmid39465107, year = {2024}, author = {Lee, S and Kim, M and Ahn, M}, title = {Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications.}, journal = {Biomedical engineering letters}, volume = {14}, number = {6}, pages = {1433-1443}, pmid = {39465107}, issn = {2093-985X}, abstract = {With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.}, }
@article {pmid39464623, year = {2024}, author = {Tang, Z and Cui, Z and Wang, H and Liu, P and Xu, X and Yang, K}, title = {A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation.}, journal = {IEEE journal of translational engineering in health and medicine}, volume = {12}, number = {}, pages = {622-634}, pmid = {39464623}, issn = {2168-2372}, mesh = {Humans ; *Electroencephalography/methods ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Adult ; Exoskeleton Device ; Evoked Potentials, Visual/physiology ; Signal Processing, Computer-Assisted ; Female ; Neural Networks, Computer ; Young Adult ; }, abstract = {Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.}, }
@article {pmid39463078, year = {2024}, author = {Jin, K and Liu, X and Hu, S and Li, Y and Wu, Y and Li, J and Mo, C}, title = {[Discussion on Magnetic Resonance Compatibility of Implantable Brain-Computer Interface Devices].}, journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation}, volume = {48}, number = {5}, pages = {486-492}, doi = {10.12455/j.issn.1671-7104.240232}, pmid = {39463078}, issn = {1671-7104}, mesh = {*Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Humans ; Brain/diagnostic imaging ; Electrodes, Implanted ; }, abstract = {Brain-computer interface (BCI) devices are crucial tools for neural stimulation and recording, offering broad prospects in the diagnosis and treatment of neurological disorders. Furthermore, magnetic resonance imaging (MRI) is an effective and non-invasive technique for capturing whole-brain signals, providing detailed information on brain structures and activation patterns. Integrating the neural stimulation/recording capabilities of BCI devices with the non-invasive detection function of MRI is considered highly significant for brain function analysis. However, this combination imposes specific requirements on the magnetic and electronic performance of neural interface devices. The interaction between BCI devices and MRI is initially explored. Subsequently, potential safety risks arising from their combination are summarized and organized. Starting from the source of these hazards, such as the metallic electrodes and wires of BCI devices, the issues are analyzed, and current research countermeasures are summarized. In conclusion, the regulatory oversight of BCI's magnetic resonance safety is briefly discussed, and suggestions for enhancing the magnetic resonance compatibility of related BCI devices are proposed.}, }
@article {pmid39460240, year = {2024}, author = {Belwafi, K and Ghaffari, F}, title = {Thought-Controlled Computer Applications: A Brain-Computer Interface System for Severe Disability Support.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460240}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Persons with Disabilities/rehabilitation ; Signal Processing, Computer-Assisted ; Machine Learning ; User-Computer Interface ; Male ; Adult ; Female ; Event-Related Potentials, P300/physiology ; Self-Help Devices ; }, abstract = {This study introduces an integrated computational environment that leverages Brain-Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human-Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system's Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.}, }
@article {pmid40231170, year = {2024}, author = {Cubillos, LH and Revach, G and Mender, MJ and Costello, JT and Temmar, H and Hite, A and Zutshi, D and Wallace, DM and Ni, X and Kelberman, MM and Willsey, MS and van Sloun, RJG and Shlezinger, N and Patil, P and Draelos, A and Chestek, CA}, title = {Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces.}, journal = {Advances in neural information processing systems}, volume = {37}, number = {}, pages = {133975-133998}, pmid = {40231170}, issn = {1049-5258}, support = {R01 NS105132/NS/NINDS NIH HHS/United States ; U41 NS129436/NS/NINDS NIH HHS/United States ; }, abstract = {People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite this trade-off, our analysis successfully integrates traditional controls and modern deep-learning approaches to motivate high-performing yet still explainable BMI designs.}, }
@article {pmid39881836, year = {2024}, author = {Butorova, AS and Koryukin, EA and Khomenko, NM and Sergeev, AP}, title = {Assessment of Accuracy of Spatial Object Localization by Means of Mono and Stereo Modes of Visual-to-Auditory Sensory Substitution in People with Visual Impairments (a Pilot Study).}, journal = {Sovremennye tekhnologii v meditsine}, volume = {16}, number = {4}, pages = {29-36}, pmid = {39881836}, issn = {2309-995X}, mesh = {Humans ; Pilot Projects ; Adult ; Male ; Female ; *Vision Disorders/physiopathology ; Persons with Visual Disabilities ; }, abstract = {UNLABELLED: The aim of the study is to assess the accuracy of spatial object localization in mono and stereo modes of visual-to-auditory sensory substitution by means of the developed system tested on persons with normal or corrected-to-normal vision.<br><br>MATERIALS AND METHODS: A prototype of a visual-to-auditory sensory substitution device based on a video camera with two lenses was prepared. Software to convert the signal from a video camera into an audio signal in mono and stereo modes was developed.To assess the developed system, an experimental study with 30 blindfolded sighted participants was conducted. 15 persons were tested in mono mode, 15 - in stereo mode. All persons were trained to use the visual-to-auditory sensory substitution system. During the experiment, participants were to locate a white plastic cube with dimensions of 4&#xd7;4&#xd7;4 cm[3] on a working surface. The researcher placed the cube in one of 20 positions on the working surface in a pseudo-random order.<br><br>RESULTS: To assess the accuracy of the cube localization, deviations along the X- and Y-axes and absolute deviations were calculated. The general dynamics of localization accuracy was positive both in mono and stereo modes. Absolute deviation and X-axis deviation were significantly higher in stereo mode; there was no significant difference in Y-axis deviation between modes. On average, participants tended to underestimate the distance to the cube when it was on the left, right, or far side of the working surface, and overestimate the distance to the cube when it was on the near side of the working surface.<br><br>CONCLUSION: Tests demonstrated that the accuracy of object localization in stereo mode can be improved by increasing the time for training the participants and by showing them more presentations. The results of the study can be used to develop assistive techniques for people with visual impairments, to manufacture medical equipment, and create brain-computer interfaces.}, }
@article {pmid39483199, year = {2023}, author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Rivera-Flor, H and De Souza, AF and Jaramillo-Isaza, S and Ruiz-Olaya, AF and Bastos-Filho, TF}, title = {Coupling Effects of Cross-Corticomuscular Association during Object Manipulation Tasks on Different Haptic Sensations.}, journal = {NeuroSci}, volume = {4}, number = {3}, pages = {195-210}, pmid = {39483199}, issn = {2673-4087}, abstract = {The effects of corticomuscular connectivity during object manipulation tasks with different haptic sensations have not been quantitatively investigated. Connectivity analyses enable the study of cortical effects and muscle responses during movements, revealing communication pathways between the brain and muscles. This study aims to examine the corticomuscular connectivity of three Electroencephalography (EEG) channels and five muscles during object manipulation tasks involving contact surfaces of Sandpaper, Suede, and Silk. The analyses included 12 healthy subjects performing tasks with their right hand. Power-Based Connectivity (PBC) and Mutual Information (MI) measures were utilized to evaluate significant differences in connectivity between contact surfaces, EEG channels, muscles, and frequency bands. The research yielded the following findings: Suede contact surface exhibited higher connectivity; Mu and Gamma frequency bands exerted greater influence; significant connectivity was observed between the three EEG channels (C 3 , C z , C 4) and the Anterior Deltoid (AD) and Brachioradialis (B) muscles; and connectivity was primarily involved during active movement in the AD muscle compared to the resting state. These findings suggest potential implementation in motor rehabilitation for more complex movements using novel alternative training systems with high effectiveness.}, }
@article {pmid39872550, year = {2023}, author = {Huo, C and Cui, Q and Bai, G}, title = {Uncovering the mystery of genetic heterogeneity in inherited peripheral neuropathies.}, journal = {Life medicine}, volume = {2}, number = {4}, pages = {lnad026}, pmid = {39872550}, issn = {2755-1733}, }
@article {pmid39845389, year = {2023}, author = {Tan, J and Joe, N and Kong, V and Clarke, D and Ko, J and Amey, J and Denize, B and Marsden, G and Yen, DA and Christey, G}, title = {Contemporary management of blunt colonic injuries - Experience from a level one trauma centre in New Zealand.}, journal = {Surgery in practice and science}, volume = {13}, number = {}, pages = {100179}, pmid = {39845389}, issn = {2666-2620}, abstract = {INTRODUCTION: Blunt colonic injury (BCI) is relatively rare, and literature on the topic is sparse. This study reviews our contemporary experience in its management at a level-one trauma centre in New Zealand.<br><br>MATERIALS AND METHODS: This was a retrospective study (January 2012 to December 2020) that included all patients who sustained a BCI managed at Waikato Hospital, New Zealand.<br><br>RESULTS: Of the total of 1181 patients with blunt abdominal trauma, 69 (6%) of them sustained a BCI (49% male, mean age: 36 years). 78 separate colonic injuries were identified in the 69 cases. The most commonly injured segment was the ascending colon 49% (38/78). Eighty percent (55/69) underwent a CT scan, with only 16 showing definite evidence of a colonic injury. AAST Grade 1 was the most common (81%). Fifteen patients underwent damage control surgery. All 11 grade 1 injuries were repaired primarily, whilst the other four grade 4 and 5 colonic injuries were resected, with 3 having a subsequent stoma formation and one delayed anastomosis. There were four mortalities. Patients who had negative or equivocal admission CT findings for colonic injury had delays to the operating theatre and had poorer outcomes.<br><br>CONCLUSION: BCI is rare but is associated with a prolonged hospital stay. The treatment of BCI is similar to that of penetrating colonic injury. CT appeared inaccurate in many cases.}, }
@article {pmid39944367, year = {2023}, author = {Mokienko, OA and Lyukmanov, RK and Bobrov, PD and Suponeva, NA and Piradov, MA}, title = {Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).}, journal = {Sovremennye tekhnologii v meditsine}, volume = {15}, number = {6}, pages = {63-73}, pmid = {39944367}, issn = {2309-995X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Upper Extremity ; *Stroke/therapy/physiopathology ; *Recovery of Function ; }, abstract = {Brain-computer interfaces (BCIs) are a group of technologies that allow mental training with feedback for post-stroke motor recovery. Varieties of these technologies have been studied in numerous clinical trials for more than 10 years, and their construct and software are constantly being improved. Despite the positive treatment results and the availability of registered medical devices, there are currently a number of problems for the wide clinical application of BCI technologies. This review provides information on the most studied types of BCIs and its training protocols and describes the evidence base for the effectiveness of BCIs for upper limb motor recovery after stroke. The main problems of scaling this technology and ways to solve them are also described.}, }
@article {pmid39886316, year = {2022}, author = {Ju, J and Feleke, AG and Luo, L and Fan, X}, title = {Recognition of Drivers' Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {2022}, number = {}, pages = {9847652}, pmid = {39886316}, issn = {2692-7632}, abstract = {In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers' hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.}, }
@article {pmid39483370, year = {2022}, author = {Simistira Liwicki, F and Gupta, V and Saini, R and De, K and Liwicki, M}, title = {Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible.}, journal = {NeuroSci}, volume = {3}, number = {2}, pages = {226-244}, pmid = {39483370}, issn = {2673-4087}, abstract = {This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain-computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.}, }
@article {pmid39460125, year = {2024}, author = {Magruder, RD and Kukkar, KK and Contreras-Vidal, JL and Parikh, PJ}, title = {Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460125}, issn = {1424-8220}, support = {1P2CHD086844-01A1/NH/NIH HHS/United States ; 1R25HD106896-05A1/NH/NIH HHS/United States ; }, mesh = {Humans ; *Postural Balance/physiology ; Male ; Female ; *Electroencephalography/methods ; *Transcranial Magnetic Stimulation/methods ; Adult ; Young Adult ; Motor Cortex/physiology ; }, abstract = {Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.}, }
@article {pmid39460066, year = {2024}, author = {Senadheera, I and Hettiarachchi, P and Haslam, B and Nawaratne, R and Sheehan, J and Lockwood, KJ and Alahakoon, D and Carey, LM}, title = {AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460066}, issn = {1424-8220}, support = {2004443//National Health and Medical Research Council/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Stroke/physiopathology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Recovery of Function/physiology ; Adult ; Robotics/methods ; Machine Learning ; }, abstract = {Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.}, }
@article {pmid39457727, year = {2024}, author = {Calderone, A and Latella, D and Bonanno, M and Quartarone, A and Mojdehdehbaher, S and Celesti, A and Calabrò, RS}, title = {Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.}, journal = {Biomedicines}, volume = {12}, number = {10}, pages = {}, pmid = {39457727}, issn = {2227-9059}, abstract = {Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.}, }
@article {pmid39455586, year = {2024}, author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD}, title = {A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1168}, pmid = {39455586}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Aged ; Imagination ; }, abstract = {This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain-computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real-time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl-Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near-infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.}, }
@article {pmid39454612, year = {2024}, author = {Liu, R and Song, Q and Ma, T and Pan, H and Li, H and Zhao, X}, title = {SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8b6e}, pmid = {39454612}, issn = {1741-2552}, mesh = {Humans ; *Wearable Electronic Devices ; *Shoulder/physiology ; Male ; *Movement/physiology ; Adult ; Wheelchairs ; User-Computer Interface ; Amputees/rehabilitation ; Female ; Brain-Computer Interfaces ; Spinal Cord Injuries/rehabilitation/physiopathology ; Middle Aged ; }, abstract = {Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.}, }
@article {pmid39453797, year = {2024}, author = {Wang, Z and Liu, Y and Huang, S and Huang, H and Wu, W and Wang, Y and An, X and Ming, D}, title = {Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3902-3912}, doi = {10.1109/TNSRE.2024.3486551}, pmid = {39453797}, issn = {1558-0210}, mesh = {Humans ; Male ; *Imagination/physiology ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Fingers ; *Stroke/physiopathology/complications ; *Brain-Computer Interfaces ; *Sensorimotor Cortex/physiopathology ; Aged ; Electroencephalography ; Adult ; Cortical Synchronization ; Movement/physiology ; Algorithms ; }, abstract = {Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A "wider range, stronger intensity, greater connection" ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.}, }
@article {pmid39451800, year = {2024}, author = {Drăgoi, MV and Nisipeanu, I and Frimu, A and Tălîngă, AM and Hadăr, A and Dobrescu, TG and Suciu, CP and Manea, AR}, title = {Real-Time Home Automation System Using BCI Technology.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {10}, pages = {}, pmid = {39451800}, issn = {2313-7673}, abstract = {A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.}, }
@article {pmid39451366, year = {2024}, author = {Caffi, L and Romito, LM and Palmisano, C and Aloia, V and Arlotti, M and Rossi, L and Marceglia, S and Priori, A and Eleopra, R and Levi, V and Mazzoni, A and Isaias, IU}, title = {Adaptive vs. Conventional Deep Brain Stimulation: One-Year Subthalamic Recordings and Clinical Monitoring in a Patient with Parkinson's Disease.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {10}, pages = {}, pmid = {39451366}, issn = {2306-5354}, support = {//Grigioni Foundation for Parkinson's disease/ ; Project-ID 424778381 - TRR 295//Deutsche Forschungsgemeinschaft/ ; //New York University School of Medicine/ ; //The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders/ ; NRRP "Fit4MedRob - Fit for Medical Robotics" Grant (# PNC0000007).//Italian Ministry of Research/ ; Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of bio-medical research in the NHS//European Union/ ; }, abstract = {Conventional DBS (cDBS) for Parkinson's disease uses constant, predefined stimulation parameters, while the currently available adaptive DBS (aDBS) provides the possibility of adjusting current amplitude with respect to subthalamic activity in the beta band (13-30 Hz). This preliminary study on one patient aims to describe how these two stimulation modes affect basal ganglia dynamics and, thus, behavior in the long term. We collected clinical data (UPDRS-III and -IV) and subthalamic recordings of one patient with Parkinson's disease treated for one year with aDBS, alternated with short intervals of cDBS. Moreover, after nine months, the patient discontinued all dopaminergic drugs while keeping aDBS. Clinical benefits of aDBS were superior to those of cDBS, both with and without medications. This improvement was paralleled by larger daily fluctuations of subthalamic beta activity. Moreover, with aDBS, subthalamic beta activity decreased during asleep with respect to awake hours, while it remained stable in cDBS. These preliminary data suggest that aDBS might be more effective than cDBS in preserving the functional role of daily beta fluctuations, thus leading to superior clinical benefit. Our results open new perspectives for a restorative brain network effect of aDBS as a more physiological, bidirectional, brain-computer interface.}, }
@article {pmid39451342, year = {2024}, author = {Kaviri, SM and Vinjamuri, R}, title = {Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {10}, pages = {}, pmid = {39451342}, issn = {2306-5354}, support = {HCC-2053498//National Science Foundation/ ; CNS-2333292//National Science Foundation/ ; }, abstract = {Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain-computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.}, }
@article {pmid39449239, year = {2024}, author = {Hong, J and Cai, M and Qin, X}, title = {Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2024.2417204}, pmid = {39449239}, issn = {1476-8259}, abstract = {Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.}, }
@article {pmid39448109, year = {2024}, author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB}, title = {Relationship of SSVEP response between flash frequency conditions.}, journal = {Progress in brain research}, volume = {290}, number = {}, pages = {123-139}, doi = {10.1016/bs.pbr.2024.07.002}, pmid = {39448109}, issn = {1875-7855}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography ; Adult ; *Photic Stimulation/methods ; Female ; *Brain-Computer Interfaces ; Young Adult ; Visual Cortex/physiology ; }, abstract = {This study delves into the application of Brain-Computer Interfaces (BCIs), focusing on exploiting Steady-State Visual Evoked Potentials (SSVEPs) as communication tools for individuals facing mobility impairments. SSVEP-BCI systems can swiftly transmit substantial volumes of information, rendering them suitable for diverse applications. However, the efficacy of SSVEP responses can be influenced by variables such as the frequency and color of visual stimuli. Through experiments involving participants equipped with electrodes on the brain's visual cortex, visual stimuli were administered at 4, 17, 25, and 40Hz, using white, red, yellow, green, and blue light sources. The results reveal that white and green stimuli evoke higher SSVEP responses at lower frequencies, with color's impact diminishing at higher frequencies. At low light intensity (1W), white and green stimuli elicit significantly higher SSVEP responses, while at high intensity (3W), responses across colors tend to equalize. Notably, due to seizure risks, red and blue lights should be used cautiously, with white and green lights preferred for SSVEP-BCI systems. This underscores the critical consideration of color and frequency in the design of effective and safe SSVEP-BCI systems, necessitating further research to optimize designs for clinical applications.}, }
@article {pmid39448108, year = {2024}, author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB}, title = {Optimizing user experience in SSVEP-BCI systems.}, journal = {Progress in brain research}, volume = {290}, number = {}, pages = {105-121}, doi = {10.1016/bs.pbr.2024.05.010}, pmid = {39448108}, issn = {1875-7855}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Photic Stimulation/methods ; Male ; *Evoked Potentials, Visual/physiology ; Adult ; Female ; Young Adult ; Visual Perception/physiology ; Signal-To-Noise Ratio ; }, abstract = {The emergence of brain-computer interface (BCI) technology provides enormous potential for human medical and daily applications. Therefore, allowing users to tolerate the visual response of SSVEP for a long time has always been an important issue in the SSVEP-BCI system. We recruited three subjects and conducted visual experiments in groups using different frequencies (17 and 25Hz) and 60Hz light. After recording the physiological signal, use FFT to perform a time-frequency analysis on the physiological signal to check whether there is any difference in the signal-to-noise ratio and amplitude of the 60Hz light source compared with a single low-frequency signal source. The results show that combining a 60Hz light source with low-frequency LEDs can reduce participants' eye discomfort while achieving effective light stimulation control. At the same time, there was no significant difference in signal-to-noise ratio and amplitude between the groups. This also means that 60Hz can make vision more continuous and improve the subject's experience and comfort. At the same time, it does not affect the performance of the original SSVEP-induced response. This study highlights the importance of considering technical aspects and user comfort when designing SSVEP-BCI systems to increase the usability of SSVEP systems for long-term flash viewing.}, }
@article {pmid39446156, year = {2024}, author = {Muirhead, WR and Layard Horsfall, H and Aicardi, C and Carolan, J and Akram, H and Vanhoestenberghe, A and Schaefer, AT and Marcus, HJ}, title = {Implanted cortical neuroprosthetics for speech and movement restoration.}, journal = {Journal of neurology}, volume = {271}, number = {11}, pages = {7156-7168}, pmid = {39446156}, issn = {1432-1459}, support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Cerebral Cortex/physiopathology/physiology ; Neural Prostheses ; Movement Disorders/therapy/physiopathology ; Movement/physiology ; Speech/physiology ; Speech Disorders/etiology/therapy/physiopathology ; }, abstract = {Implanted cortical neuroprosthetics (ICNs) are medical devices developed to replace dysfunctional neural pathways by creating information exchange between the brain and a digital system which can facilitate interaction with the external world. Over the last decade, researchers have explored the application of ICNs for diverse conditions including blindness, aphasia, and paralysis. Both transcranial and endovascular approaches have been used to record neural activity in humans, and in a laboratory setting, high-performance decoding of the signals associated with speech intention has been demonstrated. Particular progress towards a device which can move into clinical practice has been made with ICNs focussed on the restoration of speech and movement. This article provides an overview of contemporary ICNs for speech and movement restoration, their mechanisms of action and the unique ethical challenges raised by the field.}, }
@article {pmid39443437, year = {2024}, author = {Heinonen, GA and Carmona, JC and Grobois, L and Kruger, LS and Velazquez, A and Vrosgou, A and Kansara, VB and Shen, Q and Egawa, S and Cespedes, L and Yazdi, M and Bass, D and Saavedra, AB and Samano, D and Ghoshal, S and Roh, D and Agarwal, S and Park, S and Alkhachroum, A and Dugdale, L and Claassen, J}, title = {A Survey of Surrogates and Health Care Professionals Indicates Support of Cognitive Motor Dissociation-Assisted Prognostication.}, journal = {Neurocritical care}, volume = {}, number = {}, pages = {}, pmid = {39443437}, issn = {1556-0961}, support = {R01 LM011826/LM/NLM NIH HHS/United States ; UL1TR001873 from NCATS/NIH//Clinical and Translational Science Awards/ ; R01 NS106014/NS/NINDS NIH HHS/United States ; LM011826//U.S. National Library of Medicine/ ; NS106014/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Prognostication of patients with acute disorders of consciousness is imprecise but more accurate technology-supported predictions, such as cognitive motor dissociation (CMD), are emerging. CMD refers to the detection of willful brain activation following motor commands using functional magnetic resonance imaging or machine learning-supported analysis of the electroencephalogram in clinically unresponsive patients. CMD is associated with long-term recovery, but acceptance by surrogates and health care professionals is uncertain. The objective of this study was to determine receptiveness for CMD to inform goals of care (GoC) decisions and research participation among health care professionals and surrogates of behaviorally unresponsive patients.<br><br>METHODS: This was a two-center study of surrogates of and health care professionals caring for unconscious patients with severe neurological injury who were enrolled in two prospective US-based studies. Participants completed a 13-item survey to assess demographics, religiosity, minimal acceptable level of recovery, enthusiasm for research participation, and receptiveness for CMD to support GoC decisions.<br><br>RESULTS: Completed surveys were obtained from 196 participants (133 health care professionals and 63 surrogates). Across all respondents, 93% indicated that they would want their loved one or the patient they cared for to participate in a research study that supports recovery of consciousness if CMD were detected, compared to 58% if CMD were not detected. Health care professionals were more likely than surrogates to change GoC with a positive (78% vs. 59%, p&#x2009;=&#x2009;0.005) or negative (83% vs. 59%, p&#x2009;=&#x2009;0.0002) CMD result. Participants who reported religion was the most important part of their life were least likely to change GoC with or without CMD. Participants who identified as Black (odds ratio [OR] 0.12, 95% confidence interval [CI] 0.04-0.36) or Hispanic/Latino (OR 0.39, 95% CI 0.2-0.75) and those for whom religion was the most important part of their life (OR 0.18, 95% CI 0.05-0.64) were more likely to accept a lower minimum level of recovery.<br><br>CONCLUSIONS: Technology-supported prognostication and enthusiasm for clinical trial participation was supported across a diverse spectrum of health care professionals and surrogate decision-makers. Education for surrogates and health care professionals should accompany integration of technology-supported prognostication.}, }
@article {pmid39441155, year = {2024}, author = {Demnati, B and Chabihi, Z and Boumediane, EM and Dkhissi, S and Idarrha, F and Fath Elkhir, Y and Benhima, MA and Abkari, I and Rafai, M and Ibn Moussa, S and Rahmi, M}, title = {Psychological impact of peri-implant fractures: A cross-sectional study.}, journal = {La Tunisie medicale}, volume = {102}, number = {10}, pages = {708-714}, pmid = {39441155}, issn = {2724-7031}, mesh = {Humans ; Cross-Sectional Studies ; Male ; Female ; Middle Aged ; *Quality of Life/psychology ; Adult ; *Adaptation, Psychological ; Aged ; *Depression/psychology/epidemiology/etiology ; Anxiety/psychology/etiology/epidemiology ; Periprosthetic Fractures/psychology/epidemiology/surgery ; Surveys and Questionnaires ; Stress Disorders, Post-Traumatic/psychology/epidemiology/etiology/diagnosis ; Stress, Psychological/psychology/epidemiology/etiology ; Orthopedic Procedures/psychology/adverse effects ; }, abstract = {INTRODUCTION: Peri-implant fractures (PIFs) are uncommon yet critical complications following orthopedic surgery. These complications can significantly impact a patient's psychological well-being and overall quality of life.<br><br>AIM: This study aimed to investigate the psychological effects of PIFs.<br><br>METHODS: This was a cross-sectional study that involved 136 patients who underwent surgery for PIFs between 2018 and 2022. We utilized various validated scales and questionnaires such as Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Impact of Event Scale Revised (IES-R), 36-Item Short Form Survey (SF-36), and Brief COPE Inventory (BCI) to assess their psychological state.<br><br>RESULT: The results revealed that patients with PIFs experienced higher levels of anxiety, depression, stress, and post-traumatic stress compared to the general population. Additionally, they reported lower physical and mental health. Factors such as the number of surgeries, treatment delay, post-operative pain levels, and complications significantly influenced their psychological outcomes. Notably, acceptance, positive reframing, and seeking emotional support were the most common coping mechanisms employed by these patients. Conversely, denial, substance use, and self blame were the least employed strategies.<br><br>CONCLUSION: This study suggests that psychological interventions could significantly benefit patients with PIFs, potentially reducing their distress and improving their quality of life.}, }
@article {pmid39440972, year = {2024}, author = {Tong, C and Xiao, D and Li, Q and Gou, J and Wang, S and Zeng, Z and Xiong, W}, title = {First insights into the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus from generations worldwide.}, journal = {mSphere}, volume = {9}, number = {11}, pages = {e0070224}, pmid = {39440972}, issn = {2379-5042}, support = {2020B0301030005//Guangdong Major Project of Basic and Applied Basic Research/ ; 2023A1515030137//Guangdong Basic and Applied Basic Research Foundation/ ; }, mesh = {*Bacillus cereus/genetics/pathogenicity ; Virulence/genetics ; *Whole Genome Sequencing ; Prevalence ; *Biofilms/growth &amp; development ; Humans ; Genome, Bacterial ; Virulence Factors/genetics ; Global Health ; Gram-Positive Bacterial Infections/microbiology/epidemiology ; Foodborne Diseases/microbiology/epidemiology ; Phylogeny ; Asia/epidemiology ; Europe/epidemiology ; Oceania/epidemiology ; }, abstract = {Bacillus cereus, a global threat, is one of the major causes of toxin-induced foodborne diseases. However, a comprehensive assessment of the prevalence and characteristics of B. cereus worldwide is still lacking. Here, we applied whole-genome sequence analysis to 191 B. cereus collected in Africa, America, Asia, Europe, and Oceania from the 1900s to 2022, finding that CC142 dominated the global B. cereus clonal complex. The results provided direct evidence that B. cereus could spread through the food chain and intercontinentally. B. cereus from different generations worldwide showed coherence in the antibiotic-resistant gene and virulence and biofilm gene profiles, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.IMPORTANCEThis study first emphasized the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus worldwide from the 1900s to 2022 using whole-genome sequence analysis. The CC142 dominated the global Bacillus cereus clonal complex. Moreover, we revealed a close evolutionary relationship between the isolates from different sources. B. cereus isolates from different generations worldwide showed coherence in potential pathogenicity, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.}, }
@article {pmid39439491, year = {2024}, author = {Xu, S and Liu, Y and Lee, H and Li, W}, title = {Neural interfaces: Bridging the brain to the world beyond healthcare.}, journal = {Exploration (Beijing, China)}, volume = {4}, number = {5}, pages = {20230146}, pmid = {39439491}, issn = {2766-2098}, abstract = {Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed-loop brain-computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non-invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high-resolution recording and stimulation capabilities.}, }
@article {pmid39438593, year = {2024}, author = {Miklós, G and Halász, L and Hasslberger, M and Toth, E and Manola, L and Hagh Gooie, S and van Elswijk, G and Várkuti, B and Erőss, L}, title = {Sensory-substitution based sound perception using a spinal computer-brain interface.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {24879}, pmid = {39438593}, issn = {2045-2322}, mesh = {Humans ; Male ; Female ; Middle Aged ; *Auditory Perception/physiology ; *Brain-Computer Interfaces ; Adult ; Spinal Cord Stimulation/methods ; Aged ; Acoustic Stimulation ; Spinal Cord/physiology ; Hearing Aids ; }, abstract = {Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task-where chance-level performance was 33.3%-participants (n = 8) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task (n = 5) confirmed that reduced bitrates significantly impaired participants' ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.}, }
@article {pmid39437806, year = {2024}, author = {Zuo, M and Yu, B and Sui, L}, title = {Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad89c5}, pmid = {39437806}, issn = {2057-1976}, mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Virtual Reality ; Male ; Adult ; Female ; Support Vector Machine ; Machine Learning ; Young Adult ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.}, }
@article {pmid39435615, year = {2025}, author = {Song, X and Li, R and Chu, X and Li, Q and Li, R and Li, Q and Tong, KY and Gu, X and Ming, D}, title = {Multilevel analysis of the central-peripheral-target organ pathway: contributing to recovery after peripheral nerve injury.}, journal = {Neural regeneration research}, volume = {20}, number = {10}, pages = {2807-2822}, pmid = {39435615}, issn = {1673-5374}, abstract = {Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities. Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites, neglecting multilevel pathological analysis of the overall nervous system and target organs. This has led to restrictions on current therapeutic approaches. In this paper, we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective, covering the central nervous system, peripheral nervous system, and target organs. After peripheral nerve injury, the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves; changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord. The nerve will undergo axonal regeneration, activation of Schwann cells, inflammatory response, and vascular system regeneration at the injury site. Corresponding damage to target organs can occur, including skeletal muscle atrophy and sensory receptor disruption. We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury. The main current treatments are conducted passively and include physical factor rehabilitation, pharmacological treatments, cell-based therapies, and physical exercise. However, most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway. Therefore, we should further explore multilevel treatment options that produce effective, long-lasting results, perhaps requiring a combination of passive (traditional) and active (novel) treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.}, }
@article {pmid39435350, year = {2024}, author = {Beauchemin, N and Charland, P and Karran, A and Boasen, J and Tadson, B and Sénécal, S and Léger, PM}, title = {Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1416683}, pmid = {39435350}, issn = {1662-5161}, abstract = {Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.}, }
@article {pmid39434212, year = {2024}, author = {Ahmadi-Dastgerdi, N and Hosseini-Nejad, H and Alinejad-Rokny, H}, title = {A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.}, journal = {International journal of neural systems}, volume = {34}, number = {12}, pages = {2450067}, doi = {10.1142/S0129065724500679}, pmid = {39434212}, issn = {1793-6462}, mesh = {*Brain-Computer Interfaces ; *Action Potentials/physiology ; *Brain/physiology ; Neurons/physiology ; Signal Processing, Computer-Assisted ; Humans ; Algorithms ; }, abstract = {Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm[2] per channel.}, }
@article {pmid39433844, year = {2024}, author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR}, title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {1363}, pmid = {39433844}, issn = {2399-3642}, support = {T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; N2864C, A2295R, A2827R, A3803R//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2NS095548, U01NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology ; Adult ; Female ; Middle Aged ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.}, }
@article {pmid39433597, year = {2024}, author = {Candrea, DN and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, GW and Nathan, KC and Wester, BA and Anderson, WS and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE}, title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.}, journal = {Communications medicine}, volume = {4}, number = {1}, pages = {207}, pmid = {39433597}, issn = {2730-664X}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.<br><br>METHODS: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44&#x2009;min across 4&#x2009;days) collected up to 21&#x2009;days prior to BCI use, and then tested it over a period of 90&#x2009;days without any retraining or updating.<br><br>RESULTS: Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15&#x2009;min, within 1&#x2009;day).<br><br>CONCLUSIONS: These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.}, }
@article {pmid39433073, year = {2024}, author = {Zeng, P and Fan, L and Luo, Y and Shen, H and Hu, D}, title = {Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8963}, pmid = {39433073}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Signal-To-Noise Ratio ; Adult ; Male ; *Neural Networks, Computer ; Female ; Young Adult ; Photic Stimulation/methods ; }, abstract = {Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.}, }
@article {pmid39433072, year = {2024}, author = {Guetschel, P and Ahmadi, S and Tangermann, M}, title = {Review of deep representation learning techniques for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8962}, pmid = {39433072}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; *Electroencephalography/methods ; }, abstract = {In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest.Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art.Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations.Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.}, }
@article {pmid39433071, year = {2024}, author = {Chen, X and Meng, L and Xu, Y and Wu, D}, title = {Adversarial artifact detection in EEG-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad8964}, pmid = {39433071}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Artifacts ; Neural Networks, Computer ; Algorithms ; Machine Learning ; }, abstract = {Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.}, }
@article {pmid39432989, year = {2025}, author = {Yang, H and Zhu, Z and Ni, S and Wang, X and Nie, Y and Tao, C and Zou, D and Jiang, W and Zhao, Y and Zhou, Z and Sun, L and Li, M and Tao, TH and Liu, K and Wei, X}, title = {Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording.}, journal = {Biosensors &amp; bioelectronics}, volume = {267}, number = {}, pages = {116853}, doi = {10.1016/j.bios.2024.116853}, pmid = {39432989}, issn = {1873-4235}, mesh = {*Fibroins/chemistry ; Animals ; *Biosensing Techniques/instrumentation ; Mice ; Bombyx/chemistry ; Biocompatible Materials/chemistry ; Equipment Design ; Signal-To-Noise Ratio ; }, abstract = {Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200-500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.}, }
@article {pmid39432777, year = {2024}, author = {Jiang, L and Qi, X and Lai, M and Zhou, J and Yuan, M and You, J and Liu, Q and Pan, J and Zhao, L and Ying, M and Ji, J and Li, K and Zhang, Y and Pan, W and He, Q and Yang, B and Cao, J}, title = {WDR20 prevents hepatocellular carcinoma senescence by orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {44}, pages = {e2407904121}, pmid = {39432777}, issn = {1091-6490}, support = {2021YFA1300604//National Key R&amp;D Program of China/ ; 82304514//Youth Fund of the National Natural Science Foundation of China/ ; LQ23H310004//Zhejiang Provincial Natural Science Foundation of China/ ; 226-2023-00059//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Carcinoma, Hepatocellular/metabolism/genetics/pathology ; *Liver Neoplasms/metabolism/genetics/pathology ; Animals ; *Proto-Oncogene Proteins c-myc/metabolism/genetics ; *Cellular Senescence ; Mice ; *Ubiquitination ; *Ubiquitin Thiolesterase/metabolism/genetics ; Cell Line, Tumor ; Cell Proliferation ; Gene Expression Regulation, Neoplastic ; Mice, Transgenic ; Carrier Proteins ; }, abstract = {The dysfunction of the ubiquitin-proteasome system (UPS) facilitates the malignant progression of hepatocellular carcinoma (HCC). While targeting the UPS for HCC therapy has been proposed, identifying effective targets has been challenging. In this study, we conducted a focused screen of siRNA libraries targeting UPS-related WD40 repeat (WDR) proteins and found that silencing WDR20, a deubiquitinating enzyme activating factor, selectively inhibited the proliferation of HCC cells without affecting normal hepatocytes. Moreover, the downregulation of WDR20 expression induced HCC cellular senescence and suppressed tumor progression in xenograft, sleeping beauty transposon/transposase, and hydrodynamic tail vein injection-induced HCC models, and Alb-Cre[+]/MYC[+] HCC transgenic mouse models. Mechanistically, we found that WDR20 silencing disturbed the protein stability of c-Myc, orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc, thereby promoting the transcriptional activation of CDKN1A. Further investigation revealed a positive coexpression of WDR20 and c-Myc in a tissue microarray with 88 HCC clinical samples. By employing three patient-derived organoids from individuals with HCC, we have validated the decrease in c-Myc expression and the significant induction of senescence and growth inhibition following silencing of WDR20. This study not only uncovers the biological function of WDR20 and elucidates the molecular mechanism underlying its negative regulation of HCC cellular senescence but also highlight the potential of WDR20 as a promising target for HCC therapy.}, }
@article {pmid39430565, year = {2024}, author = {Ratasukharom, N and Niwitpong, SA and Niwitpong, S}, title = {Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.}, journal = {PeerJ}, volume = {12}, number = {}, pages = {e18272}, pmid = {39430565}, issn = {2167-8359}, mesh = {Thailand ; *Wind ; Models, Statistical ; Monte Carlo Method ; Air Pollution/analysis ; Humans ; Particulate Matter/analysis ; Confidence Intervals ; Computer Simulation ; }, abstract = {Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.}, }
@article {pmid39429223, year = {2024}, author = {Zhu, L and Xu, M and Huang, A and Zhang, J and Tan, X}, title = {Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2024.2417212}, pmid = {39429223}, issn = {1476-8259}, abstract = {Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.}, }
@article {pmid39429090, year = {2024}, author = {Wang, D and Zhou, H and Zhou, XL and Hu, YZ and Xu, HZ and Fu, JF}, title = {[Research advances of food addiction and obesity in children].}, journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics}, volume = {62}, number = {11}, pages = {1121-1124}, doi = {10.3760/cma.j.cn112140-20240415-00268}, pmid = {39429090}, issn = {0578-1310}, mesh = {Humans ; Child ; *Food Addiction/psychology ; *Pediatric Obesity ; Weight Gain ; Feeding Behavior ; Behavior, Addictive ; }, }
@article {pmid39428886, year = {2024}, author = {Ruiz, S and Valera, L and Ramos, P and Sitaram, R}, title = {Neurorights in the Constitution: from neurotechnology to ethics and politics.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230098}, pmid = {39428886}, issn = {1471-2970}, support = {//National Agency for Research and Development Millennium Science Initiative/ ; //Fondo Nacional de Desarrollo Cient&#xed;fico y Tecnol&#xf3;gico/ ; }, mesh = {Chile ; Humans ; *Politics ; *Neurosciences/ethics ; Neuroimaging/ethics ; Brain-Computer Interfaces/ethics ; Neurofeedback ; }, abstract = {Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428881, year = {2024}, author = {Sulzer, J and Papageorgiou, TD and Goebel, R and Hendler, T}, title = {Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230081}, pmid = {39428881}, issn = {1471-2970}, mesh = {*Neurofeedback/methods ; Humans ; *Brain/physiology ; Neurotransmitter Agents/metabolism ; Cognition ; }, abstract = {Neurofeedback (NF) is endogenous neuromodulation of circumscribed brain circuitry. While its use of real-time brain activity in a closed-loop system is similar to brain-computer interfaces, instead of controlling an external device like the latter, the goal of NF is to change a targeted brain function. In this special issue on NF, we present current and future methods for extracting and manipulating neural function, how these methods may reveal new insights about brain function, applications, and rarely discussed ethical considerations of guiding and interpreting the brain activity of others. Together, the articles in this issue outline the possibilities of NF use and impact in the real world, poising to influence the development of more effective and personalized NF protocols, improving the understanding of underlying psychological and neurological mechanisms and enhancing treatment precision for various neurological and psychiatric conditions.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428875, year = {2024}, author = {Sitaram, R and Sanchez-Corzo, A and Vargas, G and Cortese, A and El-Deredy, W and Jackson, A and Fetz, E}, title = {Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230093}, pmid = {39428875}, issn = {1471-2970}, support = {//National Institute of Health/ ; //ATR Computational Neuroscience Laboratories/ ; }, mesh = {Humans ; *Neurofeedback ; *Brain/physiology ; *Self-Control ; Cognition/physiology ; Animals ; Brain-Computer Interfaces ; Models, Neurological ; }, abstract = {While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428037, year = {2025}, author = {V, HM and Begum, BS}, title = {Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems.}, journal = {Behavioural brain research}, volume = {477}, number = {}, pages = {115295}, doi = {10.1016/j.bbr.2024.115295}, pmid = {39428037}, issn = {1872-7549}, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; *Speech/physiology ; Adult ; Male ; Female ; Young Adult ; Machine Learning ; Brain/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: The electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination.<br><br>NEW METHOD: This study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data.<br><br>RESULTS: In the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60&#x202f;% for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92&#x202f;% for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02&#x202f;% for pairwise classification and 55.58&#x202f;% for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (&#x3b8;) and delta (&#x3b4;) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states.<br><br>CONCLUSION: The findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.}, }
@article {pmid39426564, year = {2025}, author = {Sleziona, D and Ely, DR and Thommes, M}, title = {Mechanisms of drug release from a melt-milled, poorly soluble drug substance.}, journal = {Journal of pharmaceutical sciences}, volume = {114}, number = {1}, pages = {394-401}, doi = {10.1016/j.xphs.2024.10.016}, pmid = {39426564}, issn = {1520-6017}, mesh = {*Solubility ; *Griseofulvin/chemistry ; *Drug Liberation ; *Particle Size ; Excipients/chemistry ; Xylitol/chemistry ; Chemistry, Pharmaceutical/methods ; Kinetics ; Crystallization/methods ; Drug Compounding/methods ; }, abstract = {Increasing the dissolution kinetics of low aqueous soluble drugs is one of the main priorities in drug formulation. New strategies must be developed, which should consider the two main dissolution mechanisms: surface reaction and diffusion. One promising tool is the so-called solid crystal suspension, a solid dispersion consisting of purely crystalline substances. In this concept, reducing the drug particle size and embedding the particles in a hydrophilic excipient increases the dissolution kinetics. Therefore, a solid crystal suspension containing submicron drug particles was produced via a modified stirred media milling process. A geometrical phase-field approach was used to model the dissolution behavior of the drug particles. A carrier material, xylitol, and the model drug substance, griseofulvin, were ground in a pearl mill. The in-vitro dissolution profile of the product was modeled to gain a deep physical understanding of the dissolution process. The used numerical tool has the potential to be a valuable approach for predicting the dissolution behavior of newly developed formulation strategies.}, }
@article {pmid39426071, year = {2024}, author = {Chowdhury, RS and Bose, S and Ghosh, S and Konar, A}, title = {Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.}, journal = {Computers in biology and medicine}, volume = {183}, number = {}, pages = {109260}, doi = {10.1016/j.compbiomed.2024.109260}, pmid = {39426071}, issn = {1879-0534}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; }, abstract = {In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier's performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.}, }
@article {pmid39425602, year = {2024}, author = {Mahalungkar, SP and Shrivastava, R and Angadi, S}, title = {A brief survey on human activity recognition using motor imagery of EEG signals.}, journal = {Electromagnetic biology and medicine}, volume = {43}, number = {4}, pages = {312-327}, doi = {10.1080/15368378.2024.2415089}, pmid = {39425602}, issn = {1536-8386}, mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Motor Activity/physiology ; Human Activities ; Brain/physiology ; }, abstract = {Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.}, }
@article {pmid39424369, year = {2024}, author = {Berling, D and Baroni, L and Chaffiol, A and Gauvain, G and Picaud, S and Antolík, J}, title = {Optogenetic Stimulation Recruits Cortical Neurons in a Morphology-Dependent Manner.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {49}, pages = {}, pmid = {39424369}, issn = {1529-2401}, mesh = {Animals ; *Optogenetics/methods ; Cats ; Male ; Female ; Neurons/physiology ; Photic Stimulation/methods ; Pyramidal Cells/physiology ; Cerebral Cortex/physiology/cytology ; Models, Neurological ; }, abstract = {Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bears promise for brain-machine interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cats of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than the preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.}, }
@article {pmid39423832, year = {2024}, author = {Karpowicz, BM and Bhaduri, B and Nason-Tomaszewski, SR and Jacques, BG and Ali, YH and Flint, RD and Bechefsky, PH and Hochberg, LR and AuYong, N and Slutzky, MW and Pandarinath, C}, title = {Reducing power requirements for high-accuracy decoding in iBCIs.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, pmid = {39423832}, issn = {1741-2552}, support = {R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Animals ; *Action Potentials/physiology ; Macaca mulatta ; Male ; Models, Neurological ; Electric Power Supplies ; }, abstract = {Objective.Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.Approach.Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.Main results.In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.Significance.Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.}, }
@article {pmid39423831, year = {2025}, author = {Carrara, I and Aristimunha, B and Corsi, MC and de Camargo, RY and Chevallier, S and Papadopoulo, T}, title = {Geometric neural network based on phase space for BCI-EEG decoding.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad88a2}, pmid = {39423831}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Algorithms ; Deep Learning ; }, abstract = {Objective.The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain-computer interface (BCI), where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes.Approach.Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the Phase-SPDNet architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark framework.Main results.The results of our Phase-SPDNet demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding.Significance.This new architecture is explainable and with a low number of trainable parameters.}, }
@article {pmid39423829, year = {2024}, author = {Valle, C and Mendez-Orellana, C and Herff, C and Rodriguez-Fernandez, M}, title = {Identification of perceived sentences using deep neural networks in EEG.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad88a3}, pmid = {39423829}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Speech Perception/physiology ; *Neural Networks, Computer ; Young Adult ; Deep Learning ; Brain-Computer Interfaces ; }, abstract = {Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data.Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area.Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension.Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.}, }
@article {pmid39423826, year = {2025}, author = {Chen, X and Li, S and Tu, Y and Wang, Z and Wu, D}, title = {User-wise perturbations for user identity protection in EEG-based BCIs.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad88a5}, pmid = {39423826}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; Machine Learning ; Neural Networks, Computer ; Computer Security ; Algorithms ; }, abstract = {Objective. An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g. user identity, emotion, disorders, etc which should be protected.Approach. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e. random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.Main results. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.Significance. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.}, }
@article {pmid39423083, year = {2024}, author = {Kostoglou, K and Muller-Putz, GR}, title = {Motor-Related EEG Analysis Using a Pole Tracking Approach.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3837-3847}, doi = {10.1109/TNSRE.2024.3483294}, pmid = {39423083}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Spinal Cord Injuries/physiopathology ; Female ; *Movement/physiology ; Adult ; *Algorithms ; *Imagination/physiology ; Young Adult ; Reproducibility of Results ; Motor Cortex/physiology ; Middle Aged ; Healthy Volunteers ; Evoked Potentials, Motor/physiology ; Sensitivity and Specificity ; }, abstract = {This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.}, }
@article {pmid39421856, year = {2024}, author = {Klee, D and Memmott, T and Oken, B}, title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain-Computer Interface Rapid Serial Visual Presentation Paradigm.}, journal = {Signals}, volume = {5}, number = {1}, pages = {18-39}, pmid = {39421856}, issn = {2624-6120}, support = {P30 AG066518/AG/NIA NIH HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain-computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or "jittered" stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.}, }
@article {pmid39421849, year = {2024}, author = {Deng, L and Tang, H and Roy, K}, title = {Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.}, journal = {Frontiers in computational neuroscience}, volume = {18}, number = {}, pages = {1455530}, pmid = {39421849}, issn = {1662-5188}, }
@article {pmid39421767, year = {2024}, author = {Wang, Z and Xiao, X and Zhou, Z and Chen, Y and Xia, T and Sheng, X and Han, Y and Gong, W and Si, K}, title = {FLUID: a fluorescence-friendly lipid-compatible ultrafast clearing method.}, journal = {Biomedical optics express}, volume = {15}, number = {10}, pages = {5609-5624}, pmid = {39421767}, issn = {2156-7085}, abstract = {Many clearing methods achieve high transparency by removing lipid components from tissues, which damages microstructure and limits their application in lipid research. As for methods which preserve lipid, it is difficult to balance transparency, fluorescence preservation and clearing speed. In this study, we propose a rapid water-based clearing method that is fluorescence-friendly and preserves lipid components. FLUID allows for preservation of endogenous fluorescence over 60 days. It shows negligible tissue distortion and is compatible with various types of fluorescent labeling and tissue staining methods. High quality imaging of human brain tissue and compatibility with pathological staining demonstrated the potential of our method for three-dimensional (3D) biopsy and clinical pathological diagnosis.}, }
@article {pmid39421626, year = {2024}, author = {Mokienko, OA}, title = {Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.}, journal = {Sovremennye tekhnologii v meditsine}, volume = {16}, number = {1}, pages = {78-89}, pmid = {39421626}, issn = {2309-995X}, mesh = {*Brain-Computer Interfaces ; Humans ; Electrodes, Implanted ; Prostheses and Implants ; }, abstract = {Brain-computer interfaces allow the exchange of data between the brain and an external device, bypassing the muscular system. Clinical studies of invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has been a continuous improvement of approaches to neuronal signal processing in order to improve the quality of control of external devices. Currently, brain-computer interfaces with intracortical implants allow completely paralyzed patients to control robotic limbs for self-service, use a computer or a tablet, type text, and reproduce speech at an optimal speed. Studies of invasive brain-computer interfaces regularly provide new fundamental data on functioning of the central nervous system. In recent years, breakthrough discoveries and achievements have been annually made in this sphere. This review analyzes the results of clinical experiments of brain-computer interfaces with intracortical implants, provides information on the stages of this technology development, its main discoveries and achievements.}, }
@article {pmid39421603, year = {2024}, author = {Ye, M and Yang, C and Cheng, JX and Lee, HJ and Jiang, Y and Shi, L}, title = {Editorial: Neuromodulation technology: advances in optics and acoustics.}, journal = {Frontiers in cellular neuroscience}, volume = {18}, number = {}, pages = {1494457}, pmid = {39421603}, issn = {1662-5102}, support = {P41 EB031772/EB/NIBIB NIH HHS/United States ; }, }
@article {pmid39420525, year = {2024}, author = {Shang, S and Shi, Y and Zhang, Y and Liu, M and Zhang, H and Wang, P and Zhuang, L}, title = {Artificial intelligence for brain disease diagnosis using electroencephalogram signals.}, journal = {Journal of Zhejiang University. Science. B}, volume = {25}, number = {10}, pages = {914-940}, pmid = {39420525}, issn = {1862-1783}, support = {2021ZD0200405//the National Key Research and Development Project of China/ ; 62271443, 32250008 and 82330064//the National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Brain Diseases/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Brain/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.}, }
@article {pmid39419976, year = {2024}, author = {Chen, D and Zhao, Z and Shi, J and Li, S and Xu, X and Wu, Z and Tang, Y and Liu, N and Zhou, W and Ni, C and Ma, B and Wang, J and Zhang, J and Huang, L and You, Z and Zhang, P and Tang, Z}, title = {Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {440}, pmid = {39419976}, issn = {2158-3188}, support = {92148206//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82071330//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Substance-Related Disorders/therapy ; *Brain-Computer Interfaces ; *Deep Brain Stimulation/methods ; Brain/physiopathology ; Behavior, Addictive/therapy/physiopathology ; }, abstract = {Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.}, }
@article {pmid39419932, year = {2025}, author = {Deak, F}, title = {Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.}, journal = {GeroScience}, volume = {47}, number = {2}, pages = {1557-1568}, pmid = {39419932}, issn = {2509-2723}, support = {R01 AG062655/AG/NIA NIH HHS/United States ; NIH NIA R01 AG062655/AG/NIA NIH HHS/United States ; SAGA23-1142437/ALZ/Alzheimer's Association/United States ; }, mesh = {Humans ; *Alzheimer Disease/history ; History, 20th Century ; *Artificial Intelligence/history ; History, 21st Century ; *Memory Disorders/history ; Aged ; }, abstract = {Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intelligence (AI). Both can be described as growing exponentially and presenting serious challenges. Although many questions have been addressed about the dangers of AI, we want to go beyond the fearful aspects of this topic and focus on the possible contribution of AI to solve the problem of chronic disorders of the elderly leading to cognitive impairment, like Alzheimer's disease, Parkinson's disease, and Lewy body dementia. Our second goal is to look at the ways in which modern neuroscience can influence the future design of computers and the development of AI. We wish to honor the memory of Dr. John von Neumann, who came up with many breakthrough details of the first electronic computer. Remarkably, Dr. von Neumann dedicated his last book to the comparison of the human brain and the computer as it stood in those years of the mid-1950s. We will point out how his ideas are more relevant than ever in the age of supercomputers, AI and brain implants.}, }
@article {pmid39419108, year = {2024}, author = {de Seta, V and Colamarino, E and Pichiorri, F and Savina, G and Patarini, F and Riccio, A and Cincotti, F and Mattia, D and Toppi, J}, title = {Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8838}, pmid = {39419108}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; *Hand/physiopathology/physiology ; Middle Aged ; *Stroke/physiopathology ; Aged ; Adult ; *Muscle, Skeletal/physiopathology/physiology ; *Electroencephalography/methods ; Brain/physiopathology ; Movement/physiology ; Electromyography/methods ; }, abstract = {Objective.Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols.Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand (AH). Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their AH. Interestingly, brain features performed better in this latter condition with respect to healthy subjects.Significance.Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.}, }
@article {pmid39419104, year = {2024}, author = {Afonso, M and Sánchez-Cuesta, F and González-Zamorano, Y and Pablo Romero, J and Vourvopoulos, A}, title = {Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad8836}, pmid = {39419104}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; Aged ; *Stroke/physiopathology/therapy ; *Electroencephalography/methods ; Virtual Reality ; Adult ; Chronic Disease ; Neurofeedback/methods ; Treatment Outcome ; }, abstract = {Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.}, }
@article {pmid39419091, year = {2024}, author = {Liu, J and Younk, R and M Drahos, L and S Nagrale, S and Yadav, S and S Widge, A and Shoaran, M}, title = {Neural decoding and feature selection methods for closed-loop control of avoidance behavior.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, pmid = {39419091}, issn = {1741-2552}, support = {R01 MH123634/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; Rats ; *Avoidance Learning/physiology ; Male ; Rats, Sprague-Dawley ; Algorithms ; Amygdala/physiology ; }, abstract = {Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.}, }
@article {pmid39419024, year = {2024}, author = {Agudelo-Toro, A and Michaels, JA and Sheng, WA and Scherberger, H}, title = {Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.}, journal = {Neuron}, volume = {112}, number = {24}, pages = {4115-4129.e8}, doi = {10.1016/j.neuron.2024.09.018}, pmid = {39419024}, issn = {1097-4199}, mesh = {Animals ; *Brain-Computer Interfaces ; *Posture/physiology ; *Hand Strength/physiology ; *Hand/physiology ; *Macaca mulatta ; Male ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Prostheses and Implants ; }, abstract = {Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.}, }
@article {pmid39416663, year = {2024}, author = {Tang, Q and Yang, X and Sun, M and He, M and Sa, R and Zhang, K and Zhu, B and Li, T}, title = {Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1449729}, pmid = {39416663}, issn = {1664-2295}, abstract = {BACKGROUND: The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body's recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke.<br><br>METHODS: A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction.<br><br>RESULTS: This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy.<br><br>CONCLUSION: Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.}, }
@article {pmid39416032, year = {2024}, author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC}, title = {Non-invasive brain-machine interface control with artificial intelligence copilots.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39416032}, issn = {2692-8205}, support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; }, abstract = {Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3× in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.}, }
@article {pmid39415112, year = {2024}, author = {Guo, X and Kong, L and Wen, Y and Chen, L and Hu, S}, title = {Impact of second-generation antipsychotics monotherapy or combined therapy in cytokine, lymphocyte subtype, and thyroid antibodies for schizophrenia: a retrospective study.}, journal = {BMC psychiatry}, volume = {24}, number = {1}, pages = {695}, pmid = {39415112}, issn = {1471-244X}, support = {2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation team for precision diagnosis and treatment of major brain diseases/ ; 226-2022-00193, 226-2022-00002//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Schizophrenia/drug therapy/immunology/blood ; Male ; Female ; Retrospective Studies ; Adult ; *Antipsychotic Agents/therapeutic use ; *Cytokines/blood/immunology ; Middle Aged ; *Autoantibodies/blood/immunology ; *Drug Therapy, Combination ; Lymphocyte Subsets/immunology/drug effects ; Iodide Peroxidase/immunology ; }, abstract = {BACKGROUND: Schizophrenia (SCZ) shares high clinical relevance with the immune system, and the potential interactions of psychopharmacological drugs with the immune system are still an overlooked area. Here, we aimed to identify whether the second-generation antipsychotics (SGA) monotherapy or combined therapy of SGA with other psychiatric medications influence the routine blood immunity biomarkers of patients with SCZ.<br><br>METHODS: Medical records of inpatients with SCZ from January 2019 to June 2023 were retrospectively screened from June 2023 to August 2023. The demographic data and peripheral levels of cytokines (IL-2, IL-4, IL-6, TNF-&#x3b1;, INF-&#x3b3;, and IL-17&#xa0;A), lymphocyte subtype proportions (CD3+, CD4+, CD8&#x2009;+&#x2009;T-cell, and natural killer (NK) cells), and thyroid autoimmune antibodies (thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb)) were collected and analyzed.<br><br>RESULTS: 30 drug-na&#xef;ve patients, 64 SGA monotherapy (20 for first-episode SCZ, 44 for recurrent SCZ) for at least one week, 39 combined therapies for recurrent SCZ (18 with antidepressant, 10 with benzodiazepine, and 11 with mood stabilizer) for at least two weeks, and 23 used to receive SGA monotherapy (had withdrawn for at least two weeks) were included despite specific medication. No difference in cytokines was found between the SGA monotherapy sub-groups (p&#x2009;>&#x2009;0.05). Of note, SGA monotherapy appeared to induce a down-regulation of IFN-&#x3b3; in both first (mean [95% confidence interval]: 1.08 [0.14-2.01] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.020) and recurrent (1.88 [0.71-3.05] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.027) episodes compared to drug-na&#xef;ve patients. However, the lymphocyte proportions and thyroid autoimmune antibodies remained unchanged after at least two weeks of SGA monotherapy (p&#x2009;>&#x2009;0.05). In combined therapy groups, results mainly resembled the SGA monotherapy for recurrent SCZ (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: The study demonstrated that SGA monotherapy possibly achieved its comfort role via modulating IFN-&#x3b3;, and SGA combined therapy showed an overall resemblance to monotherapy.}, }
@article {pmid39413730, year = {2024}, author = {Wang, J and Chen, ZS}, title = {Closed-loop neural interfaces for pain: Where do we stand?.}, journal = {Cell reports. Medicine}, volume = {5}, number = {10}, pages = {101662}, pmid = {39413730}, issn = {2666-3791}, support = {R01 NS121776/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; }, mesh = {Animals ; Humans ; Brain-Computer Interfaces ; *Chronic Pain/therapy/physiopathology ; Pain Management/methods ; }, abstract = {Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.}, }
@article {pmid39413689, year = {2024}, author = {Rebouillat, B and Barascud, N and Kouider, S}, title = {Partial awareness during voluntary endogenous decision.}, journal = {Consciousness and cognition}, volume = {125}, number = {}, pages = {103769}, doi = {10.1016/j.concog.2024.103769}, pmid = {39413689}, issn = {1090-2376}, mesh = {Humans ; *Awareness/physiology ; Male ; Adult ; Female ; *Decision Making/physiology ; Young Adult ; *Metacognition/physiology ; Electroencephalography ; }, abstract = {Despite our feeling of control over decisions, our ability to consciously access choices before execution remains debated. Recent research reveals prospective access to intention to act, allowing potential vetoes of impending decisions. However, whether the content of impending decision can be accessed remain debated. Here we track neural signals during participants' early deliberation in free decisions. Participants chose freely between two options but sometimes had to reject their current decision just before execution. The initially preferred option, tracked in real time, significantly predicts the upcoming choice, but remain mostly outside of conscious awareness. Participants often display overconfidence in their access to this content. Instead, confidence is associated with a neural marker of self-initiated decision, indicating a qualitative confusion in the confidence evaluation process. Our results challenge the notion of complete agency over choices, suggesting inflated awareness of forthcoming decisions and providing insights into metacognitive processes in free decision-making.}, }
@article {pmid39413360, year = {2025}, author = {Trott, J and Slaymaker, C and Niznik, G and Althoff, T and Netherton, B}, title = {Brain Computer Interfaces: An Introduction for Clinical Neurodiagnostic Technologists.}, journal = {The Neurodiagnostic journal}, volume = {65}, number = {1}, pages = {32-45}, doi = {10.1080/21646821.2024.2408501}, pmid = {39413360}, issn = {2164-6821}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; }, abstract = {Brain-computer interface (BCI) is a term used to describe systems that translate biological information into commands that can control external devices such as computers, prosthetics, and other machinery. While BCI is used in military applications, home control systems, and a wide array of entertainment, much of its modern interest and funding can be attributed to its utility in the medical community, where it has rapidly propelled advancements in the restoration or replacement of critical functions robbed from victims of disease, stroke, and traumatic injury. BCI devices can allow patients to move prosthetic limbs, operate devices such as wheelchairs or computers, and communicate through writing and speech-generating devices. In this article, we aim to provide an introductory summary of the historical context and modern growing utility of BCI, with specific interest in igniting the conversation of where and how the neurodiagnostics community and its associated parties can embrace and contribute to the world of BCI.}, }
@article {pmid39412979, year = {2024}, author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Zeng, LL and Hu, D}, title = {MASER: Enhancing EEG Spatial Resolution With State Space Modeling.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3858-3868}, doi = {10.1109/TNSRE.2024.3481886}, pmid = {39412979}, issn = {1558-0210}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Brain-Computer Interfaces ; Reproducibility of Results ; Electrodes ; }, abstract = {Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.}, }
@article {pmid39412966, year = {2025}, author = {OLeary, G and Koerner, J and Kanchwala, M and Filho, JS and Xu, J and Valiante, TA and Genov, R}, title = {BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {19}, number = {1}, pages = {55-67}, doi = {10.1109/TBCAS.2024.3481160}, pmid = {39412966}, issn = {1940-9990}, mesh = {Humans ; *Electroencephalography/instrumentation ; *Brain/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted/instrumentation ; Machine Learning ; Brain-Computer Interfaces ; Neurons/physiology ; Algorithms ; }, abstract = {Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6µW, typical: 118µW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.}, }
@article {pmid39409506, year = {2024}, author = {Xu, H and Haider, W and Aziz, MZ and Sun, Y and Yu, X}, title = {Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409506}, issn = {1424-8220}, support = {52172387//National Natural Science Foundation of China/ ; U2033202, U1333119//Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Principal Component Analysis ; *Machine Learning ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Support Vector Machine ; }, abstract = {This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).}, }
@article {pmid39409418, year = {2024}, author = {Borirakarawin, M and Siribunyaphat, N and Aung, ST and Punsawad, Y}, title = {The Development of a Multicommand Tactile Event-Related Potential-Based Brain-Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409418}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Wearable Electronic Devices ; *Touch/physiology ; Adult ; Female ; *Vibration ; Evoked Potentials/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; }, abstract = {A tactile event-related potential (ERP)-based brain-computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain-computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.}, }
@article {pmid39409405, year = {2024}, author = {Silvoni, S and Occhigrossi, C and Di Giorgi, M and Lulé, D and Birbaumer, N}, title = {Brain Function, Learning, and Role of Feedback in Complete Paralysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409405}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Learning/physiology ; *Brain/physiology/physiopathology ; Paralysis/physiopathology/psychology ; Communication ; Feedback ; }, abstract = {The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental-clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain-computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought-action-consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the 'extinction of thinking' theory and to identify the actual barriers preventing communication in these patients.}, }
@article {pmid39409342, year = {2024}, author = {Liu, M and Liu, Y and Feleke, AG and Fei, W and Bi, L}, title = {Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409342}, issn = {1424-8220}, support = {JCKY2022602C024//Basic Research Plan/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Aircraft ; Young Adult ; Female ; Emergencies ; }, abstract = {Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.}, }
@article {pmid39409201, year = {2024}, author = {Shokri, R and Koolivand, Y and Shoaei, O and Caviglia, DD and Aiello, O}, title = {A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409201}, issn = {1424-8220}, mesh = {*Analog-Digital Conversion ; Humans ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; Neurons/physiology ; Equipment Design ; Nonlinear Dynamics ; }, abstract = {Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain-machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm[2]. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.}, }
@article {pmid39405625, year = {2025}, author = {Kasprzyk-Hordern, B and Jagadeesan, K and Sims, N and Farkas, K and Proctor, K and Bagnall, J and Robertson, M and Jones, DL and Wade, MJ}, title = {Multi-biomarker approach for estimating population size in a national-scale wastewater-based epidemiology study.}, journal = {Water research}, volume = {268}, number = {Pt A}, pages = {122527}, doi = {10.1016/j.watres.2024.122527}, pmid = {39405625}, issn = {1879-2448}, mesh = {*Biomarkers ; Humans ; *Wastewater ; Population Density ; England ; Wastewater-Based Epidemiological Monitoring ; }, abstract = {This study identifies biochemical markers (BCIs) that can be used as population markers in wastewater-based epidemiology (WBE) and compares their estimates with other established population size estimation (PE) methods, including census data (PECEN). Several groups of BCIs (64 targets: genetic and chemical markers) were investigated in an intercity study, including 10 cities/towns within England equating to a population of ∼7 million people. Several selection criteria were applied to identify the best BCIs to provide robust estimation of population size at a catchment level: (1) excellent performance with analytical methods; (2) excellent fit of the linear regression model which indicates PE-driven BCI daily loads; (3) low temporal variability in usage; (4) human-linked origin. Only a few tested BCIs showed a strong positive linear correlation between daily BCI loads and PE indicating their low spatiotemporal variability. These are: cimetidine, clarithromycin, metformin, cotinine, bezafibrate, metronidazole and hydroxymetronidazole, diclofenac, and benzophenone 1. However, only high/long term usage pharmaceuticals: cimetidine and metformin as well as cotinine (metabolite of nicotine) performed well when tested in two independent datasets and catchments accounting for both spatial and temporal scales. Strong seasonal usage trends were observed for antihistamines, NSAIDs (anti-inflammatories), antibiotics and UV filters, invalidating them as PE markers. Key conclusions from the study are: (1) Cimetidine is the best performing BCI; (2) Chemical markers outperform genetic markers as PE BCIs; (3) Water utility PE estimates (PEWW) align well with PECEN and PEBCI values; (4) Ammonium/orthophosphate as well as viral PE markers suffer from high temporal variability, hence, they are not recommended as PEBCI markers, and, most importantly, (5) PEBCI calibration/validation at the country/region level is advised in order to establish the best PE markers suited for local/national needs and accounting for site/region specific uncertainties.}, }
@article {pmid39402900, year = {2025}, author = {Zhang, X and Pei, X and Shi, Y and Yang, Y and Bai, X and Chen, T and Zhao, Y and Yang, Q and Ye, J and Leng, X and Yang, Q and Bai, R and Wang, Y and Sui, B}, title = {Unveiling connections between venous disruption and cerebral small vessel disease using diffusion tensor image analysis along perivascular space (DTI-ALPS): A 7-T MRI study.}, journal = {International journal of stroke : official journal of the International Stroke Society}, volume = {20}, number = {4}, pages = {497-506}, doi = {10.1177/17474930241293966}, pmid = {39402900}, issn = {1747-4949}, mesh = {Humans ; *Cerebral Small Vessel Diseases/diagnostic imaging ; Male ; Middle Aged ; Female ; Cross-Sectional Studies ; *Diffusion Tensor Imaging/methods ; Aged ; *Glymphatic System/diagnostic imaging/pathology ; White Matter/diagnostic imaging/pathology ; Cerebral Veins/diagnostic imaging/pathology ; Prospective Studies ; Brain/diagnostic imaging/pathology/blood supply ; Magnetic Resonance Imaging/methods ; }, abstract = {BACKGROUND: Cerebral venous disruption is one of the characteristic findings in cerebral small vessel disease (CSVD), and its disruption may impede perivascular glymphatic drainage. And lower diffusivity along perivascular space (DTI-ALPS) index has been suggested to be with the presence and severity of CSVD. However, the relationships between venous disruption, DTI-ALPS index, and CSVD neuroimaging features remain unclear.<br><br>AIMS: To investigate the association between venous integrity and perivascular diffusion activity, and explore the mediating role of DTI-ALPS index between venous disruption and CSVD imaging features.<br><br>METHODS: In this cross-sectional study, 31 patients (mean age, 59.0&#x2009;&#xb1;&#x2009;9.9&#x2009;years) were prospectively enrolled and underwent 7-T magnetic resonance (MR) imaging. DTI-ALPS index was measured to quantify the perivascular diffusivity. The visibility and continuity of deep medullary veins (DMVs) were evaluated based on a brain region-based visual score on high-resolution susceptibility-weighted imaging. White matter hyperintensity (WMH) and perivascular space (PVS) were assessed using qualitative and quantitative methods. Linear regression and mediation analysis were performed to analyze the relationships among DMV scores, DTI-ALPS index, and CSVD features.<br><br>RESULTS: The DTI-ALPS index was significantly associated with the parietal DMV score (&#x3b2;&#x2009;=&#x2009;-0.573, p corrected&#x2009;=&#x2009;0.004). Parietal DMV score was associated with WMH volume (&#x3b2;&#x2009;=&#x2009;0.463, p corrected&#x2009;=&#x2009;0.013) and PVS volume in basal ganglia (&#x3b2;&#x2009;=&#x2009;0.415, p corrected&#x2009;=&#x2009;0.028). Mediation analyses showed that DTI-ALPS index manifested a full mediating effect on the association between parietal DMV score and WMH (indirect effect&#x2009;=&#x2009;0.115, Pm&#x2009;=&#x2009;43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect&#x2009;=&#x2009;0.161, Pm&#x2009;=&#x2009;42.8%).<br><br>CONCLUSION: Cerebral venous disruption is associated with glymphatic activity, and with WMH and PVS volumes. Our results suggest cerebral venous integrity may play a critical role in preserving perivascular glymphatic activity; while disruption of small veins may impair the perivascular diffusivity, thereby contributing to the development of WMH and PVS enlargement.}, }
@article {pmid39401512, year = {2024}, author = {Eisma, YB and van Vliet, ST and Nederveen, AJ and de Winter, JCF}, title = {Assessing the influence of visual stimulus properties on steady-state visually evoked potentials and pupil diameter.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {6}, pages = {}, doi = {10.1088/2057-1976/ad865d}, pmid = {39401512}, issn = {2057-1976}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Pupil/physiology ; *Photic Stimulation ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Signal-To-Noise Ratio ; }, abstract = {Steady-State Visual Evoked Potentials (SSVEPs) are brain responses measurable via electroencephalography (EEG) in response to continuous visual stimulation at a constant frequency. SSVEPs have been instrumental in advancing our understanding of human vision and attention, as well as in the development of brain-computer interfaces (BCIs). Ongoing questions remain about which type of visual stimulus causes the most potent SSVEP response. The current study investigated the effects of color, size, and flicker frequency on the signal-to-noise ratio of SSVEPs, complemented by pupillary light reflex measurements obtained through an eye-tracker. Six participants were presented with visual stimuli that differed in terms of color (white, red, green), shape (circles, squares, triangles), size (10,000 to 30,000 pixels), flicker frequency (8 to 25 Hz), and grouping (one stimulus at a time versus four stimuli presented in a 2 × 2 matrix to simulate a BCI). The results indicated that larger stimuli elicited stronger SSVEP responses and more pronounced pupil constriction. Additionally, the results revealed an interaction between stimulus color and flicker frequency, with red being more effective at lower frequencies and white at higher frequencies. Future SSVEP research could focus on the recommended waveform, interactions between SSVEP and power grid frequency, a wider range of flicker frequencies, a larger sample of participants, and a systematic comparison of the information transfer obtained through SSVEPs, pupil diameter, and eye movements.}, }
@article {pmid39400422, year = {2025}, author = {Vírseda-Chamorro, M and Salinas-Casado, J and Adot-Zurbano, JM and Méndez-Rubio, S and Moreno-Sierra, J}, title = {Can We Differentiate Between Organic and Functional Bladder Outlet Obstruction in Males With Parkinson's Disease?.}, journal = {Neurourology and urodynamics}, volume = {44}, number = {1}, pages = {171-177}, doi = {10.1002/nau.25599}, pmid = {39400422}, issn = {1520-6777}, support = {//The authors received no specific funding for this work./ ; }, mesh = {Humans ; *Urinary Bladder Neck Obstruction/physiopathology/diagnosis ; Male ; *Parkinson Disease/physiopathology/complications/diagnosis ; Aged ; Case-Control Studies ; *Urodynamics ; *Prostatic Hyperplasia/physiopathology/complications/diagnosis ; Middle Aged ; Urinary Bladder/physiopathology ; Pressure ; Aged, 80 and over ; Diagnosis, Differential ; }, abstract = {OBJECTIVES: To determine the type of bladder outlet obstruction (BOO) in patients with Parkinson's disease (PD).<br><br>MATERIAL AND METHOD: A case-control study was carried out in 46 patients divided into two groups. Group 1 formed by 23 PD patients with BOO (a URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Group 2 formed by 23 patients with benign prostatic hyperplasia (BPH) and compressive obstruction (an opening pressure >&#x2009;35&#x2009;cm H2O) and URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Both groups underwent a pressure-flow study to calculate Dynamic Urethral Resistance Relationship (DURR) patterns. Based on previous research, we describe two types of DURR pattern. Pattern A typical of dynamic or functional obstruction and pattern B typical of static or organic obstruction.<br><br>RESULTS: We found that PD patients had a significantly higher frequency of pattern A (70%) than BPH patients (4%). Other significant differences between groups were age (greater in PD group), bladder compliance (greater in PD group), maximum flow rate [Qmax (greater in BPH group)], maximum detrusor pressure [Pmax (greater in BPH group)], detrusor pressure at maximum flow rate [PQmax (greater in BPH group)], opening detrusor pressure (greater in BPH group), and the bladder contractility parameters BCI and Wmax (greater in BPH group). There were no significant differences in perineal voiding electromyography (EMG) activity between groups nor relationship between voiding EMG activity and the type of DURR pattern.<br><br>CONCLUSIONS: Our results are consistent with the usefulness of the DURR pattern to differentiate between functional and organic BOO in PD patients. Most PD patients have functional obstruction although a minority has organic obstruction consistent with BPH.}, }
@article {pmid39399473, year = {2024}, author = {Zhang, K and Zhou, W and Yu, H and Pang, M and Gao, H and Anwar, F and Yu, K and Zhou, Z and Guo, F and Liu, X and Ming, D}, title = {Insights on pathophysiology of hydrocephalus rats induced by kaolin injection.}, journal = {FASEB bioAdvances}, volume = {6}, number = {9}, pages = {351-364}, pmid = {39399473}, issn = {2573-9832}, abstract = {Hydrocephalus can affect brain function and motor ability. Current treatments mostly involve invasive surgeries, with a high risk of postoperative infections and failure. A successful animal model plays a significant role in developing new treatments for hydrocephalus. Hydrocephalus was induced in Sprague-Dawley rats by injecting 25% kaolin into the subarachnoid space at the cerebral convexities with different volumes of 30, 60 and 90 μL. Magnetic resonance imaging (MRI) was performed 1 month and 4 months after kaolin injection. The behavioral performance was assessed weekly, lasting for 7 weeks. The histopathological analyses were conducted to the lateral ventricles by hematoxylin-eosin (HE) staining. Transcriptomic analysis was used between Normal Pressure Hydrocephalus (NPH) patients and hydrocephalus rats. MRI showed a progressive enlargement of ventricles in hydrocephalus group. Kaolin-60 μL and kaolin-90 μL groups showed larger ventricular size, higher anxiety level, bigger decline in body weight, motor ability and cognitive competence. These symptoms may be due to higher-grade inflammatory infiltrate and the damage of the structure of ependymal layer of the ventricles, indicated by HE staining. The overlap upregulated genes and pathways mainly involve immunity and inflammation. Transcriptomic revealed shared pathogenic genes CD40, CD44, CXCL10, and ICAM1 playing a dominance role. 60 μL injection might be recommended for the establishment of hydrocephalus animal model, with a high successful rate and high stability. The hydrocephalus model was able to resemble the inflammatory mechanism and behavioral performance observed in human NPH patients, providing insights for identifying therapeutic targets for hydrocephalus.}, }
@article {pmid39398473, year = {2024}, author = {Hanada, GM and Kalabic, M and Ferris, DP}, title = {Mobile brain-body imaging data set of indoor treadmill walking and outdoor walking with a visual search task.}, journal = {Data in brief}, volume = {57}, number = {}, pages = {110968}, doi = {10.1016/j.dib.2024.110968}, pmid = {39398473}, issn = {2352-3409}, abstract = {To fully understand brain processes in the real world, it is necessary to record and quantitatively analyse brain processes during real world human experiences. Mobile electroencephalography (EEG) and physiological data sensors provide new opportunities for studying humans outside of the laboratory. The purpose of this study was to document data from high-density EEG and mobile physiological sensors while humans performed a visual search task both on a treadmill in a laboratory setting and overground in a natural outdoor setting. The data set includes 49 young, healthy participants on an outdoor arboretum path and on a treadmill in a laboratory with a large virtual reality screen. The data provide a valuable research tool for scientists interested in signal processing, electrocortical brain processes, mobile brain imaging, and brain-computer interfaces based on mobile EEG. Given the comparison data between laboratory and real world conditions, researchers can test the viability of new processing algorithms across conditions or investigate changes in electrocortical activity related to behavioural dynamics coded into the data.}, }
@article {pmid39397592, year = {2025}, author = {Deepika, D and Rekha, G}, title = {A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer Interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {28}, number = {1}, pages = {90-106}, doi = {10.1080/10255842.2024.2410221}, pmid = {39397592}, issn = {1476-8259}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Attention/physiology ; }, abstract = {Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.}, }
@article {pmid39397321, year = {2024}, author = {Gokhale, SM and Bhatia, M}, title = {Lymphoscintigraphy With SPECT-CT in Detecting the Site of Chyle Leak in Postoperative Patient.}, journal = {Clinical nuclear medicine}, volume = {49}, number = {12}, pages = {e664-e667}, doi = {10.1097/RLU.0000000000005496}, pmid = {39397321}, issn = {1536-0229}, mesh = {Humans ; Chyle/diagnostic imaging ; Esophagectomy/adverse effects ; *Lymphoscintigraphy ; Postoperative Complications/diagnostic imaging ; *Single Photon Emission Computed Tomography Computed Tomography ; }, abstract = {Here is a case of chyle leak post McKeown esophagectomy. Lymphoscintigraphy with 99m Tc-filtered sulfur colloid revealed tracer accumulation along the thoracic duct and in the left hemithorax. Precise localization of leak was done by SPECT-CT imaging. This enabled timely surgical intervention and reduced further morbidity. This procedure is not only precise but also cost-effective as compared with the other available investigations.}, }
@article {pmid39397043, year = {2024}, author = {Shi, C and Jiang, J and Li, C and Chen, C and Jian, W and Song, J}, title = {Precision-induced localized molten liquid metal stamps for damage-free transfer printing of ultrathin membranes and 3D objects.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {8839}, pmid = {39397043}, issn = {2041-1723}, support = {U21A20502//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; U20A6001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12321002//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12302214//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Transfer printing, a crucial technique for heterogeneous integration, has gained attention for enabling unconventional layouts and high-performance electronic systems. Elastomer stamps are typically used for transfer printing, where localized heating for elastomer stamp can effectively control the transfer process. A key challenge is the potential damage to ultrathin membranes from the contact force of elastic stamps, especially with fragile inorganic nanomembranes. Herein, we present a precision-induced localized molten technique that employs either laser-induced transient heating or hotplate-induced directional heating to precisely melt solid gallium (Ga). By leveraging the fluidity of localized molten Ga, which provides gentle contact force and exceptional conformal adaptability, this technique avoids damage to fragile thin films and improves operational reliability compared to fully liquefied Ga stamps. Furthermore, the phase transition of Ga provides a reversible adhesion with high adhesion switchability. Once solidified, the Ga stamp hardens and securely adheres to the micro/nano-membrane during the pick-up process. The solidified stamp also exhibits the capability to maneuver arbitrarily shaped objects by generating a substantial grip force through the interlocking effects. Such a robust, damage-free, simply operable protocol illustrates its promising capabilities in transfer printing diverse ultrathin membranes and objects on complex surfaces for developing high-performance unconventional electronics.}, }
@article {pmid39396768, year = {2024}, author = {Zhang, J and Wang, L and Guo, H and Kong, S and Li, W and He, Q and Ding, L and Yang, B}, title = {The role of Tim-3 blockade in the tumor immune microenvironment beyond T cells.}, journal = {Pharmacological research}, volume = {209}, number = {}, pages = {107458}, doi = {10.1016/j.phrs.2024.107458}, pmid = {39396768}, issn = {1096-1186}, mesh = {*Hepatitis A Virus Cellular Receptor 2/antagonists &amp; inhibitors/immunology/metabolism ; *Tumor Microenvironment/immunology ; Humans ; Animals ; *Neoplasms/immunology/drug therapy/pathology ; Killer Cells, Natural/immunology ; T-Lymphocytes/immunology/drug effects ; Macrophages/immunology ; Dendritic Cells/immunology ; }, abstract = {Numerous preclinical studies have demonstrated the inhibitory function of T cell immunoglobulin mucin domain-containing protein 3 (Tim-3) on T cells as an inhibitory receptor, leading to the clinical development of anti-Tim-3 blocking antibodies. However, recent studies have shown that Tim-3 is expressed not only on T cells but also on multiple cell types in the tumor microenvironment (TME), including dendritic cells (DCs), natural killer (NK) cells, macrophages, and tumor cells. Therefore, Tim-3 blockade in the immune microenvironment not only affect the function of T cells but also influence the functions of other cells. For example, Tim-3 blockade can enhance the ability of DCs to regulate innate and adaptive immunity. The role of Tim-3 blockade in NK cells function is controversial, as it can enhance the antitumor function of NK cells under certain conditions while having the opposite effect in other situations. Additionally, Tim-3 blockade can promote the reversal of macrophage polarization from the M2 phenotype to the M1 phenotype. Furthermore, Tim-3 blockade can inhibit tumor development by suppressing the proliferation and metastasis of tumor cells. In summary, increasing evidence has shown that Tim-3 in other cell types also plays a critical role in the efficacy of anti-Tim-3 therapy. Understanding the function of anti-Tim-3 therapy in non-T cells can help elucidate the diverse responses observed in clinical patients, leading to better development of relevant therapeutic strategies. This review aims to discuss the role of Tim-3 in the TME and emphasize the impact of Tim-3 blockade in the tumor immune microenvironment beyond T cells.}, }
@article {pmid39396567, year = {2025}, author = {Su, H and Zhan, G and Lin, Y and Wang, L and Jia, J and Zhang, L and Gan, Z and Kang, X}, title = {Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG.}, journal = {Brain research}, volume = {1846}, number = {}, pages = {149261}, doi = {10.1016/j.brainres.2024.149261}, pmid = {39396567}, issn = {1872-6240}, mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain/physiopathology/physiology ; *Stroke Rehabilitation/methods ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; Nerve Net/physiopathology/physiology ; Movement/physiology ; Adult ; Motor Cortex/physiology/physiopathology ; }, abstract = {Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.}, }
@article {pmid39396023, year = {2024}, author = {Zhou, H and Hong, T and Chen, X and Su, C and Teng, B and Xi, W and Cadet, JL and Yang, Y and Geng, F and Hu, Y}, title = {Glutamate concentration of medial prefrontal cortex is inversely associated with addictive behaviors: a translational study.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {433}, pmid = {39396023}, issn = {2158-3188}, support = {81971245//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {*Glutamic Acid/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; Male ; Animals ; Rats ; Humans ; *Internet Addiction Disorder/physiopathology/metabolism ; *Behavior, Addictive/physiopathology/metabolism ; *Drug-Seeking Behavior/physiology ; Adult ; *Methamphetamine ; Young Adult ; Gyrus Cinguli/metabolism/physiopathology ; Female ; Disease Models, Animal ; Rats, Sprague-Dawley ; Translational Research, Biomedical ; Self Administration ; }, abstract = {In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.}, }
@article {pmid39395910, year = {2025}, author = {Fan, J and Wang, X and Xu, H}, title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.}, journal = {Neuroscience bulletin}, volume = {41}, number = {1}, pages = {192-194}, pmid = {39395910}, issn = {1995-8218}, }
@article {pmid39387251, year = {2024}, author = {Zhang, W and Bai, L and Xu, W and Liu, J and Chen, Y and Lin, W and Lu, H and Wang, B and Luo, B and Peng, G and Zhang, K and Shen, C}, title = {Sirt6 Mono-ADP-Ribosylates YY1 to Promote Dystrophin Expression for Neuromuscular Transmission.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {44}, pages = {e2406390}, pmid = {39387251}, issn = {2198-3844}, support = {LZ22C110002//Natural Science Foundation of Zhejiang Province/ ; 2021YFA1101100//National Key R&amp;D Program of China/ ; 2022YFF1000500//National Key R&amp;D Program of China/ ; 32271031//National Natural Science Foundation of China/ ; 82230038//National Natural Science Foundation of China/ ; 31871203//National Natural Science Foundation of China/ ; 32071032//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Sirtuins/metabolism/genetics ; *YY1 Transcription Factor/metabolism/genetics ; Mice ; *Neuromuscular Junction/metabolism/drug effects ; *Dystrophin/genetics/metabolism ; Synaptic Transmission/drug effects ; Muscle, Skeletal/metabolism/drug effects ; Disease Models, Animal ; Male ; ADP-Ribosylation ; }, abstract = {The degeneration of the neuromuscular junction (NMJ) and the decline in motor function are common features of aging, but the underlying mechanisms have remained largely unclear. This study reveals that Sirt6 is reduced in aged mouse muscles. Ablation of Sirt6 in skeletal muscle causes a reduction of Dystrophin levels, resulting in premature NMJ degeneration, compromised neuromuscular transmission, and a deterioration in motor performance. Mechanistic studies show that Sirt6 negatively regulates the stability of the Dystrophin repressor YY1 (Yin Yang 1). Specifically, Sirt6 mono-ADP-ribosylates YY1, causing its disassociation from the Dystrophin promoter and allowing YY1 to bind to the SMURF2 E3 ligase, leading to its degradation. Importantly, supplementation with nicotinamide mononucleotide (NMN) enhances the mono-ADP-ribosylation of YY1 and effectively delays NMJ degeneration and the decline in motor function in elderly mice. These findings provide valuable insights into the intricate mechanisms underlying NMJ degeneration during aging. Targeting Sirt6 could be a potential therapeutic approach to mitigate the detrimental effects on NMJ degeneration and improve motor function in the elderly population.}, }
@article {pmid39394849, year = {2024}, author = {Li, Z and Meng, M}, title = {An SCA-based classifier for motor imagery EEG classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2024.2414069}, pmid = {39394849}, issn = {1476-8259}, abstract = {Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.}, }
@article {pmid39391754, year = {2024}, author = {Salari, V and O'Connor, R and Rodrigues, S and Oblak, D}, title = {Editorial: New approaches in Brain-Machine Interfaces with implants.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1485472}, pmid = {39391754}, issn = {1662-4548}, }
@article {pmid39391265, year = {2024}, author = {Ren, C and Li, X and Gao, Q and Pan, M and Wang, J and Yang, F and Duan, Z and Guo, P and Zhang, Y}, title = {The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1438095}, pmid = {39391265}, issn = {1662-5161}, abstract = {INTRODUCTION: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.<br><br>METHODS: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.<br><br>RESULTS: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD&#x2009;=&#x2009;0.50, 95% CI: 0.26-0.73, I[2] =&#x2009;0%, p <&#x2009;0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD&#x2009;=&#x2009;0.37, 95% CI: 0.00-0.74, I[2] =&#x2009;21%, p =&#x2009;0.05), and the BCI-FES&#x2009;+&#x2009;CR vs. CR group (SMD&#x2009;=&#x2009;0.61, 95% CI: 0.28-0.95, I[2] =&#x2009;0%, p =&#x2009;0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD&#x2009;=&#x2009;0.56, 95% CI: 0.25-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0004) and chronic groups (SMD&#x2009;=&#x2009;0.42, 95% CI: 0.05-0.78, I[2] =&#x2009;45%, p =&#x2009;0.02). Subgroup analysis showed that both adjusting (SMD&#x2009;=&#x2009;0.55, 95% CI: 0.24-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0006) and fixing (SMD&#x2009;=&#x2009;0.43, 95% CI: 0.07-0.78, I[2] =&#x2009;46%, p =&#x2009;0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD&#x2009;=&#x2009;0.41 95% CI: 0.12-0.71, I[2] =&#x2009;13%, p =&#x2009;0.006) and action observation (AO) (SMD&#x2009;=&#x2009;0.73, 95% CI: 0.26-1.20, I[2] =&#x2009;0%, p =&#x2009;0.002) as mental tasks significantly improved upper limb function in stroke patients.<br><br>DISCUSSION: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.<br><br>Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.}, }
@article {pmid39391047, year = {2024}, author = {Jang, M and Hays, M and Yu, WH and Lee, C and Caragiulo, P and Ramkaj, A and Wang, P and Phillips, AJ and Vitale, N and Tandon, P and Yan, P and Mak, PI and Chae, Y and Chichilnisky, EJ and Murmann, B and Muratore, DG}, title = {A 1024-Channel 268 nW/pixel 36×36 μm[2]/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.}, journal = {IEEE journal of solid-state circuits}, volume = {59}, number = {4}, pages = {1123-1136}, pmid = {39391047}, issn = {0018-9200}, support = {R01 EY021271/EY/NEI NIH HHS/United States ; R01 EY032900/EY/NEI NIH HHS/United States ; }, abstract = {This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm[2]) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.}, }
@article {pmid39389052, year = {2024}, author = {Zheng, Z and Liu, Y and Mu, R and Guo, X and Feng, Y and Guo, C and Yang, L and Qiu, W and Zhang, Q and Yang, W and Dong, Z and Qiu, S and Dong, Y and Cui, Y}, title = {A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice.}, journal = {Neuron}, volume = {112}, number = {23}, pages = {3924-3939.e5}, doi = {10.1016/j.neuron.2024.09.012}, pmid = {39389052}, issn = {1097-4199}, mesh = {Animals ; *Habenula/physiology ; *Depression ; *Stress, Psychological ; Mice ; *Neurons/physiology ; *Hypothalamus/metabolism ; Male ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Hypothalamic Area, Lateral/physiology ; }, abstract = {Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.}, }
@article {pmid39386879, year = {2024}, author = {Huang, Y and Yang, L and Yang, L and Xu, Z and Li, M and Shang, Z}, title = {Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy.}, journal = {Heliyon}, volume = {10}, number = {19}, pages = {e38113}, pmid = {39386879}, issn = {2405-8440}, abstract = {Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.}, }
@article {pmid39385316, year = {2024}, author = {Wang, D and Guo, X and Huang, Q and Wang, Z and Chen, J and Hu, S}, title = {Efficacy and Safety of Transcranial Direct Current Stimulation as an Add-On Trial Treatment for Acute Bipolar Depression Patients With Suicidal Ideation.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {10}, pages = {e70077}, pmid = {39385316}, issn = {1755-5949}, support = {81971271//National Natural Science Foundation of China/ ; 82201675//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province/ ; 2020KY134//Health and Family Planning Commission of Zhejiang Province/ ; }, mesh = {Humans ; *Bipolar Disorder/therapy/psychology ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; Adult ; Double-Blind Method ; *Suicidal Ideation ; Middle Aged ; Treatment Outcome ; Psychiatric Status Rating Scales ; Quetiapine Fumarate/therapeutic use ; Young Adult ; Antipsychotic Agents/therapeutic use/adverse effects ; Combined Modality Therapy/methods ; }, abstract = {AIMS: Bipolar depression poses an overwhelming suicide risk. We aimed to examine the efficacy and safety of transcranial direct current stimulation (tDCS) combined with quetiapine in bipolar patients as a suicidal intervention.<br><br>METHODS: In a single-center, double-blind, treatment-naive bipolar depression patients with suicidal ideation were randomly assigned to quetiapine in combination with either active (n&#x2009;=&#x2009;16) or sham (n&#x2009;=&#x2009;15) tDCS over the left dorsolateral prefrontal cortex for three consecutive weeks. The 30-min, 2-mA tDCS was conducted twice a day on the weekday of the first week and then once a day on the weekdays of the two following weeks. Primary efficacy outcome measure was the change in the Beck Scale for Suicidal Ideation (BSSI). Secondary outcomes included changes on the 17-item Hamilton Depression Rating Scale (HDRS-17) and Montgomery-Asberg Depression Rating Scale (MADRS). Outcome was evaluated on Day 3 and weekend. Safety outcome was based on the reported adverse reactions.<br><br>RESULTS: Active tDCS was superior to sham tDCS on the BSSI at Day 3 and tended to sustain every weekend during the treatment process, compared to baseline. However, no difference between active and sham in HDRS-17 and MADRS was found. Response and remission rate also supported the antisuicide effect of tDCS, with higher response and remission rate in BSSI, but no antidepressant effect, compared to sham, over time. Regarding safety, active tDCS was well tolerated and all the adverse reactions reported were mild and limited to transient scalp discomfort.<br><br>CONCLUSION: The tDCS was effective as an antisuicide treatment for acute bipolar depression patients with suicidal ideation, with minimal side effects reported.}, }
@article {pmid39384601, year = {2024}, author = {Li, D and Li, K and Xia, Y and Dong, J and Lu, R}, title = {Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {23549}, pmid = {39384601}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Imagination ; Brain/physiology ; }, abstract = {In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.}, }
@article {pmid39383883, year = {2024}, author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Wagner, L and van Dijk, JP and Kubben, PL and Herff, C}, title = {Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad851c}, pmid = {39383883}, issn = {1741-2552}, mesh = {Humans ; Male ; Female ; Adult ; *Movement/physiology ; *Brain/physiology ; *Electroencephalography/methods ; Electrodes, Implanted ; Young Adult ; Hand Strength/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Electrocorticography/methods ; Epilepsy/physiopathology ; Middle Aged ; }, abstract = {Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.}, }
@article {pmid39383715, year = {2024}, author = {Li, K and Qian, L and Zhang, C and Li, R and Zeng, J and Xue, C and Deng, W}, title = {Deep transcranial magnetic stimulation for treatment-resistant obsessive-compulsive disorder: A meta-analysis of randomized-controlled trials.}, journal = {Journal of psychiatric research}, volume = {180}, number = {}, pages = {96-102}, doi = {10.1016/j.jpsychires.2024.09.043}, pmid = {39383715}, issn = {1879-1379}, mesh = {Humans ; *Obsessive-Compulsive Disorder/therapy ; Outcome Assessment, Health Care ; *Randomized Controlled Trials as Topic ; *Transcranial Magnetic Stimulation/methods ; }, abstract = {BACKGROUND: Deep transcranial magnetic stimulation (dTMS), an advancement of transcranial magnetic stimulation, was created to reach wider and possibly more profound regions of the brain. At present, there is insufficient high-quality evidence to support the effectiveness and safety of dTMS in treating obsessive-compulsive disorder (OCD).<br><br>OBJECTIVE: This study used a meta-analysis to evaluate the effectiveness and safety of dTMS for treating OCD.<br><br>METHODS: Four randomized controlled trials were found by searching PubMed, Embase, Web of Science, and Cochrane Library up to February 2024. The fixed effects meta-analysis model was used for the purpose of data merging in Stata17. The risk ratio (RR) value was used as the measure of effect size to compare response rates and dropout rates between active and sham dTMS.<br><br>RESULTS: The meta-analysis included four randomized-controlled trials involving 252 patients with treatment-resistant OCD. Active dTMS showed a notably greater rate of response on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) in comparison to sham dTMS after treatment (Y-BOCS: RR&#xa0;=&#xa0;3.71, 95% confidence interval [CI] 2.06 to 6.69) and at the one-month follow-up (Y-BOCS: RR&#xa0;=&#xa0;2.60, 95% CI 1.59 to 4.26). Subgroup analysis revealed that active dTMS with H-coils was more effective than sham dTMS (RR&#xa0;=&#xa0;3.57, 95%CI 1.93 to 6.60). No serious adverse events were documented in the studies that were included.<br><br>CONCLUSION: The findings suggest that dTMS demonstrates notable efficacy and safety in treating patients with treatment-resistant OCD compared to sham dTMS, with sustained effectiveness noted throughout the one-month post-treatment period.}, }
@article {pmid39381774, year = {2024}, author = {Pilacinski, A and Christ, L and Boshoff, M and Iossifidis, I and Adler, P and Miro, M and Kuhlenkötter, B and Klaes, C}, title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1383089}, pmid = {39381774}, issn = {1662-5218}, abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.}, }
@article {pmid39379912, year = {2024}, author = {Chen, B and Dong, J and Guo, W and Li, T}, title = {Sex-specific associations between levels of high-sensitivity C-reactive protein and severity of depression: retrospective cross-sectional analysis of inpatients in China.}, journal = {BMC psychiatry}, volume = {24}, number = {1}, pages = {667}, pmid = {39379912}, issn = {1471-244X}, support = {82230046//Key Project of the National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; *C-Reactive Protein/analysis ; Retrospective Studies ; China/epidemiology ; Middle Aged ; Cross-Sectional Studies ; Adult ; *Severity of Illness Index ; Sex Factors ; *Inpatients ; Aged ; Depression/blood ; Depressive Disorder/blood/epidemiology ; }, abstract = {BACKGROUND: We aimed to clarify the controversial relationship between levels of high-sensitivity C-reactive protein (hs-CRP) and severity of depression in men and women.<br><br>METHODS: Medical records were retrospectively analyzed for 1,236 inpatients at our medical center who were diagnosed with depression at discharge between January 2018 and August 2022. Depression severity was assessed during hospitalization using the 24-item Hamilton Depression Rating Scale. Potential associations between severity scores and hs-CRP levels were explored using multivariate linear regression as well as smooth curve fitting to detect non-linear patterns.<br><br>RESULTS: In male patients, hs-CRP levels between 2.00&#xa0;mg/L and 10.00&#xa0;mg/L showed a non-linear association with depression severity overall (fully adjusted &#x3b2;&#x2009;=&#x2009;1.69, 95% CI 0.65 to 2.72), as well as with severity of specific symptoms such as hopelessness, sluggishness, and cognitive disturbance. In female patients, hs-CRP levels showed a linear association with severity of cognitive disturbance (fully adjusted &#x3b2;&#x2009;=&#x2009;0.07, 95% CI 0.01 to 0.12). These results remained significant after adjusting for age, body mass index, diabetes, hypertension, history of drinking, history of smoking, and estradiol levels.<br><br>DISCUSSION: Levels of hs-CRP show sex-specific associations with depression severity, particularly levels between 2.00 and 10.00&#xa0;mg/L in men. These findings may help develop personalized anti-inflammatory treatments for depression, particularly for men with hs-CRP levels of 2.00-10.00&#xa0;mg/L.}, }
@article {pmid39378126, year = {2024}, author = {Li, J and Wu, W and Chen, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P}, title = {Development and safety of investigational and approved drugs targeting the RAS function regulation in RAS mutant cancers.}, journal = {Toxicological sciences : an official journal of the Society of Toxicology}, volume = {202}, number = {2}, pages = {167-178}, doi = {10.1093/toxsci/kfae129}, pmid = {39378126}, issn = {1096-0929}, support = {82373968//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Neoplasms/drug therapy/genetics ; *Antineoplastic Agents/therapeutic use ; *ras Proteins/genetics/metabolism ; *Mutation ; Animals ; Drug Approval ; Signal Transduction/drug effects ; Drugs, Investigational/therapeutic use/adverse effects ; Drug Development ; }, abstract = {The RAS gene family holds a central position in controlling key cellular activities such as migration, survival, metabolism, and other vital biological processes. The activation of RAS signaling cascades is instrumental in the development of various cancers. Although several RAS inhibitors have gained approval from the US Food and Drug Administration for their substantial antitumor effects, their widespread and severe adverse reactions significantly curtail their practical usage in the clinic. Thus, there exists a pressing need for a comprehensive understanding of these adverse events, ensuring the clinical safety of RAS inhibitors through the establishment of precise management guidelines, suitable intermittent dosing schedules, and innovative combination regimens. This review centers on the evolution of RAS inhibitors in cancer therapy, delving into the common adverse effects associated with these inhibitors, their underlying mechanisms, and the potential strategies for mitigation.}, }
@article {pmid39376540, year = {2024}, author = {Giove, F and Zuo, XN and Calhoun, VD}, title = {Editorial: Insights in brain imaging methods: 2023.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1488845}, doi = {10.3389/fnins.2024.1488845}, pmid = {39376540}, issn = {1662-4548}, }
@article {pmid39375394, year = {2024}, author = {Katoozian, D and Hosseini-Nejad, H and Dehaqani, MA}, title = {A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {23291}, pmid = {39375394}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; *Macaca mulatta ; Neurons/physiology ; Male ; Humans ; }, abstract = {In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm[2], and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.}, }
@article {pmid39374625, year = {2024}, author = {Abbasi, MAA and Abbasi, HF and Yu, X and Aziz, MZ and Yih, NTJY and Fan, Z}, title = {E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad83f4}, pmid = {39374625}, issn = {1741-2552}, abstract = {The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .}, }
@article {pmid39374272, year = {2025}, author = {Lv, R and Chang, W and Yan, G and Nie, W and Zheng, L and Guo, B and Sadiq, MT}, title = {A Novel Recognition and Classification Approach for Motor Imagery Based on Spatio-Temporal Features.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {1}, pages = {210-223}, doi = {10.1109/JBHI.2024.3464550}, pmid = {39374272}, issn = {2168-2208}, support = {//National Natural Science Foundation of China/ ; //Major Science and Technology Projects of Gansu Province/ ; //Young Doctoral Program of Gansu Department of Education/ ; //Science and Technology Program of Gansu Province/ ; }, mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Adult ; Male ; Young Adult ; Female ; Movement/physiology ; Algorithms ; Neural Networks, Computer ; }, abstract = {Motor imagery, as a paradigm of brain-computer interface, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-computer interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.}, }
@article {pmid39373084, year = {2024}, author = {Kong, F and He, F and Chisholm, RA}, title = {High beta diversity of gaps contributes to plot-level tree diversity in a tropical forest.}, journal = {Ecology}, volume = {105}, number = {11}, pages = {e4443}, doi = {10.1002/ecy.4443}, pmid = {39373084}, issn = {1939-9170}, support = {//China Scholarship Council/ ; }, mesh = {*Biodiversity ; *Forests ; *Trees/classification ; *Tropical Climate ; Panama ; }, abstract = {Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non-gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non-gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non-gap sites in the 50-ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non-gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non-gaps. Species composition showed small differences between individual gaps and non-gaps but differed significantly between collections of gaps and non-gaps. Specifically, 55 species specialized in 7-year-old gaps compared with 24 in non-gaps; of these, 23 gap-specialized species and zero non-gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap-specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities.}, }
@article {pmid39372246, year = {2024}, author = {Sakel, M and Saunders, K and Ozolins, C and Biswas, R}, title = {Feasibility and Safety of a Home-based Electroencephalogram Neurofeedback Intervention to Reduce Chronic Neuropathic Pain: A Cohort Clinical Trial.}, journal = {Archives of rehabilitation research and clinical translation}, volume = {6}, number = {3}, pages = {100361}, pmid = {39372246}, issn = {2590-1095}, abstract = {OBJECTIVE: To evaluate the feasibility, safety, and potential health benefits of an 8-week home-based neurofeedback intervention.<br><br>DESIGN: Single-group preliminary study.<br><br>SETTING: Community-based.<br><br>PARTICIPANTS: Nine community dwelling adults with chronic neuropathic pain, 6 women and 3 men, with an average age of 51.9 years (range, 19-78 years) and with a 7-day average minimum pain score of 4 of 10 on the visual analog pain scale.<br><br>INTERVENTIONS: A minimum of 5 neurofeedback sessions per week (40min/session) for 8 consecutive weeks was undertaken with a 12-week follow-up baseline electroencephalography recording period.<br><br>MAIN OUTCOME MEASURES: Primary feasibility outcomes: accessibility, tolerability, safety (adverse events and resolution), and human and information technology (IT) resources required. Secondary outcomes: pain, sensitization, catastrophization, anxiety, depression, sleep, health-related quality of life, electroencephalographic activity, and simple participant feedback.<br><br>RESULTS: Of the 23 people screened, 11 were eligible for recruitment. One withdrew and another completed insufficient sessions for analysis, which resulted in 9 datasets analyzed. Three participants withdrew from the follow-up baselines, leaving 6 who completed the entire trial protocol. Thirteen adverse events were recorded and resolved: 1 was treatment-related, 4 were equipment-related, and 8 were administrative-related (eg, courier communication issues). The human and IT resources necessary for trial implementation were identified. There were also significant improvements in pain levels, depression, and anxiety. Six of 9 participants perceived minimal improvement or no change in symptoms after the trial, and 5 of 9 participants were satisfied with the treatment received.<br><br>CONCLUSIONS: It is feasible and safe to conduct a home-based trial of a neurofeedback intervention for people with chronic neuropathic pain, when the human and IT resources are provided and relevant governance processes are followed. Improvements in secondary outcomes merit investigation with a randomized controlled trial.}, }
@article {pmid39371523, year = {2024}, author = {Jin, W and Zhu, X and Qian, L and Wu, C and Yang, F and Zhan, D and Kang, Z and Luo, K and Meng, D and Xu, G}, title = {Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.}, journal = {Frontiers in computational neuroscience}, volume = {18}, number = {}, pages = {1431815}, pmid = {39371523}, issn = {1662-5188}, abstract = {Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.}, }
@article {pmid39371161, year = {2024}, author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE}, title = {Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39371161}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {OBJECTIVE: Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice.<br><br>APPROACH: In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings.<br><br>MAIN RESULTS: Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms.<br><br>SIGNIFICANCE: To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome.<br><br>CLINICAL TRIAL INFORMATION: ClinicalTrials.gov, registration number NCT03567213.}, }
@article {pmid39369803, year = {2024}, author = {Jin, C and Li, Y and Yin, Y and Ma, T and Hong, W and Liu, Y and Li, N and Zhang, X and Gao, JH and Zhang, X and Zha, R}, title = {The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective.}, journal = {NeuroImage}, volume = {301}, number = {}, pages = {120879}, doi = {10.1016/j.neuroimage.2024.120879}, pmid = {39369803}, issn = {1095-9572}, mesh = {Humans ; *Prefrontal Cortex/physiology/diagnostic imaging ; Male ; Female ; *Self-Control ; Adult ; *Transcranial Direct Current Stimulation ; Young Adult ; *Connectome ; Magnetic Resonance Imaging ; Reward ; Delay Discounting/physiology ; }, abstract = {The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.}, }
@article {pmid39369514, year = {2025}, author = {Wang, Y and Han, M and Jing, L and Jia, Q and Lv, S and Xu, Z and Liu, J and Cai, X}, title = {Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel.}, journal = {Biosensors &amp; bioelectronics}, volume = {267}, number = {}, pages = {116837}, doi = {10.1016/j.bios.2024.116837}, pmid = {39369514}, issn = {1873-4235}, mesh = {*Alginates/chemistry ; *Chitosan/chemistry ; *Dexamethasone/pharmacology/chemistry/administration &amp; dosage/analogs &amp; derivatives ; *Microelectrodes ; Animals ; *Hydrogels/chemistry ; *Biosensing Techniques/instrumentation ; *Neurons/drug effects/physiology ; Anti-Inflammatory Agents/pharmacology/chemistry ; Dopamine/chemistry/pharmacology/analysis ; Brain/drug effects/physiology ; Metal Nanoparticles/chemistry ; Platinum/chemistry ; Rats ; }, abstract = {Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.}, }
@article {pmid39369011, year = {2024}, author = {Peng, Z and Tong, L and Shi, W and Xu, L and Huang, X and Li, Z and Yu, X and Meng, X and He, X and Lv, S and Yang, G and Hao, H and Jiang, T and Miao, X and Ye, L}, title = {Multifunctional human visual pathway-replicated hardware based on 2D materials.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {8650}, pmid = {39369011}, issn = {2041-1723}, support = {62222404, 62304084 and 92248304//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Visual Pathways/physiology ; *Retina/physiology ; *Brain-Computer Interfaces ; Visual Cortex/physiology ; Tungsten/chemistry ; Robotics/instrumentation ; Selenium/chemistry ; Artificial Intelligence ; }, abstract = {Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.}, }
@article {pmid39368632, year = {2024}, author = {Hu, J and Chen, C and Wu, M and Zhang, J and Meng, F and Li, T and Luo, B}, title = {Assessing consciousness in acute coma using name-evoked responses.}, journal = {Brain research bulletin}, volume = {218}, number = {}, pages = {111091}, doi = {10.1016/j.brainresbull.2024.111091}, pmid = {39368632}, issn = {1873-2747}, mesh = {Humans ; Male ; Female ; *Coma/physiopathology ; *Electroencephalography/methods ; Adult ; Middle Aged ; *Electromyography/methods ; *Acoustic Stimulation/methods ; *Consciousness/physiology ; Aged ; Glasgow Coma Scale ; Names ; Brain/physiopathology ; Young Adult ; Evoked Potentials/physiology ; Evoked Potentials, Auditory/physiology ; }, abstract = {Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.}, }
@article {pmid39368606, year = {2024}, author = {Takemi, M and Tia, B and Kosugi, A and Castagnola, E and Ansaldo, A and Ricci, D and Fadiga, L and Ushiba, J and Iriki, A}, title = {Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation.}, journal = {Neuroscience}, volume = {560}, number = {}, pages = {263-271}, doi = {10.1016/j.neuroscience.2024.09.047}, pmid = {39368606}, issn = {1873-7544}, mesh = {Animals ; *Callithrix ; *Motor Cortex/physiology ; Male ; *Posture/physiology ; *Forelimb/physiology ; *Brain Mapping/methods ; *Electric Stimulation/methods ; Electrodes, Implanted ; Electromyography ; Muscle, Skeletal/physiology ; Epidural Space/physiology ; }, abstract = {Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.}, }
@article {pmid39366386, year = {2025}, author = {Zhang, L and Wang, HL and Zhang, YF and Mao, XT and Wu, TT and Huang, ZH and Jiang, WJ and Fan, KQ and Liu, DD and Yang, B and Zhuang, MH and Huang, GM and Liang, Y and Zhu, SJ and Zhong, JY and Xu, GY and Li, XM and Cao, Q and Li, YY and Jin, J}, title = {Stress triggers irritable bowel syndrome with diarrhea through a spermidine-mediated decline in type I interferon.}, journal = {Cell metabolism}, volume = {37}, number = {1}, pages = {87-103.e10}, doi = {10.1016/j.cmet.2024.09.002}, pmid = {39366386}, issn = {1932-7420}, mesh = {*Irritable Bowel Syndrome/metabolism ; *Diarrhea/metabolism ; *Spermidine/pharmacology/metabolism ; Animals ; Humans ; Male ; Stress, Psychological/complications/metabolism ; Mice, Inbred C57BL ; Interferon Type I/metabolism ; Mice ; Dendritic Cells/metabolism ; }, abstract = {Irritable bowel syndrome with diarrhea (IBS-D) is a common and chronic gastrointestinal disorder that is characterized by abdominal discomfort and occasional diarrhea. The pathogenesis of IBS-D is thought to be related to a combination of factors, including psychological stress, abnormal muscle contractions, and inflammation and disorder of the gut microbiome. However, there is still a lack of comprehensive analysis of the logical regulatory correlation among these factors. In this study, we found that stress induced hyperproduction of xanthine and altered the abundance and metabolic characteristics of Lactobacillus murinus in the gut. Lactobacillus murinus-derived spermidine suppressed the basal expression of type I interferon (IFN)-α in plasmacytoid dendritic cells by inhibiting the K63-linked polyubiquitination of TRAF3. The reduction in IFN-α unrestricted the contractile function of colonic smooth muscle cells, resulting in an increase in bowel movement. Our findings provided a theoretical basis for the pathological mechanism of, and new drug targets for, stress-exposed IBS-D.}, }
@article {pmid39366088, year = {2024}, author = {Pan, Y and Sequestro, M and Golkar, A and Olsson, A}, title = {Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses.}, journal = {Behaviour research and therapy}, volume = {183}, number = {}, pages = {104641}, doi = {10.1016/j.brat.2024.104641}, pmid = {39366088}, issn = {1873-622X}, mesh = {Humans ; *Prefrontal Cortex/physiology ; Male ; Female ; Young Adult ; *Hemodynamics/physiology ; *Extinction, Psychological/physiology ; Adult ; *Memory/physiology ; Fear/psychology/physiology ; Magnetic Resonance Imaging ; Touch/physiology ; Adolescent ; }, abstract = {Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.}, }
@article {pmid39367153, year = {2024}, author = {Miroshnikov, A and Yakovlev, L and Syrov, N and Vasilyev, A and Berkmush-Antipova, A and Golovanov, F and Kaplan, A}, title = {Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.}, journal = {Brain topography}, volume = {38}, number = {1}, pages = {4}, pmid = {39367153}, issn = {1573-6792}, support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; }, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Hemodynamics/physiology ; Young Adult ; *Brain Mapping/methods ; Touch Perception/physiology ; Touch/physiology ; Somatosensory Cortex/physiology/diagnostic imaging ; Brain/physiology/diagnostic imaging ; Motor Cortex/physiology/diagnostic imaging ; }, abstract = {Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.}, }
@article {pmid39366955, year = {2024}, author = {Ullah, R and Xue, C and Wang, S and Qin, Z and Rauf, N and Zhan, S and Khan, NU and Shen, Y and Zhou, YD and Fu, J}, title = {Alternate-day fasting delays pubertal development in normal-weight mice but prevents high-fat diet-induced obesity and precocious puberty.}, journal = {Nutrition &amp; diabetes}, volume = {14}, number = {1}, pages = {82}, pmid = {39366955}, issn = {2044-4052}, support = {82350410491//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82370863//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Diet, High-Fat/adverse effects ; *Puberty, Precocious/etiology/prevention &amp; control ; Female ; Mice ; *Fasting ; *Obesity/prevention &amp; control/etiology ; Sexual Maturation/physiology ; Pregnancy ; Mice, Inbred C57BL ; Growth Hormone/blood ; }, abstract = {BACKGROUND/OBJECTIVES: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice.<br><br>METHODS: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed.<br><br>RESULTS: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty.<br><br>CONCLUSIONS: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice.}, }
@article {pmid39365711, year = {2025}, author = {Wang, Y and Wang, J and Wang, W and Su, J and Bunterngchit, C and Hou, ZG}, title = {TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {2}, pages = {810-821}, doi = {10.1109/TBME.2024.3474049}, pmid = {39365711}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Adult ; Male ; Databases, Factual ; Young Adult ; Female ; }, abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject &amp; cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.<br><br>METHODS: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.<br><br>RESULTS: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p = 2.4e-5 < 0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.<br><br>CONCLUSION/SIGNIFICANCE: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.}, }
@article {pmid39362975, year = {2024}, author = {Yu, H and Cao, W and Fang, T and Jin, J and Pei, G}, title = {EEG β oscillations in aberrant data perception under cognitive load modulation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22995}, pmid = {39362975}, issn = {2045-2322}, support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; LQ22C090007//Zhejiang Provincial Natural Science Foundation of China/ ; 72271166//National Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; }, mesh = {Humans ; Male ; *Cognition/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Decision Making/physiology ; Beta Rhythm/physiology ; Brain/physiology ; Perception/physiology ; }, abstract = {Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.}, }
@article {pmid39358539, year = {2024}, author = {Drew, L}, title = {United States sets the pace for implantable brain-computer interfaces.}, journal = {Nature}, volume = {634}, number = {8032}, pages = {S8-S10}, doi = {10.1038/d41586-024-03046-5}, pmid = {39358539}, issn = {1476-4687}, mesh = {Humans ; *Brain-Computer Interfaces/statistics &amp; numerical data/trends ; *Electrodes, Implanted/statistics &amp; numerical data/trends ; United States ; }, }
@article {pmid39358411, year = {2024}, author = {Andreu-Sánchez, C and Martín-Pascual, M&#xc1; and Gruart, A and Delgado-García, JM}, title = {Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22921}, pmid = {39358411}, issn = {2045-2322}, support = {PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; BIO-122//Junta de Andaluc&#xed;a/ ; BIO-122//Junta de Andaluc&#xed;a/ ; }, mesh = {Humans ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; Young Adult ; Psychomotor Performance/physiology ; Photic Stimulation ; Brain Waves/physiology ; Visual Perception/physiology ; Brain-Computer Interfaces ; Motor Activity/physiology ; }, abstract = {Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.}, }
@article {pmid39358021, year = {2024}, author = {Graczyk, E and Hutchison, B and Valle, G and Bjanes, D and Gates, D and Raspopovic, S and Gaunt, R}, title = {Clinical Applications and Future Translation of Somatosensory Neuroprostheses.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {40}, pages = {}, pmid = {39358021}, issn = {1529-2401}, mesh = {Humans ; *Somatosensory Cortex/physiology ; Feedback, Sensory/physiology ; Translational Research, Biomedical/trends/methods ; Neural Prostheses ; Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Prostheses and Implants/trends ; }, abstract = {Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.}, }
@article {pmid39353205, year = {2024}, author = {Zhang, Y and Xing, H and Li, J and Han, F and Fan, S and Zhang, Y}, title = {Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.}, journal = {ACS sensors}, volume = {9}, number = {10}, pages = {5312-5321}, doi = {10.1021/acssensors.4c01568}, pmid = {39353205}, issn = {2379-3694}, mesh = {*Fibroins/chemistry ; Humans ; Wearable Electronic Devices ; Nociceptors/physiology ; Biomimetic Materials/chemistry ; Biomimetics/instrumentation/methods ; Robotics/instrumentation ; }, abstract = {With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.}, }
@article {pmid39352826, year = {2025}, author = {Jin, J and Chen, W and Xu, R and Liang, W and Wu, X and He, X and Wang, X and Cichocki, A}, title = {Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {1}, pages = {198-209}, doi = {10.1109/JBHI.2024.3472097}, pmid = {39352826}, issn = {2168-2208}, support = {//National Natural Science Foundation of China/ ; //Shanghai Municipal Science and Technology Major Project/ ; //Fundamental Research Funds for the Central Universities/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.}, }
@article {pmid39356668, year = {2024}, author = {Simony, E and Grossman, S and Malach, R}, title = {Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {41}, pages = {e2319709121}, pmid = {39356668}, issn = {1091-6490}, mesh = {*Brain/physiology ; *Biological Evolution ; *Models, Neurological ; Humans ; Entorhinal Cortex/physiology ; Animals ; Neurons/physiology ; Neural Networks, Computer ; Nerve Net/physiology ; }, abstract = {Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.}, }
@article {pmid39355672, year = {2024}, author = {Smith, K and Pilger, A and Amorim, MLM and Mircic, S and Reining, Z and Ristow, N and Miller, D and Leonhardt, A and Donovan, JC and Meier, M and Marzullo, TC and Serbe-Kamp, E and Steiner, AP and Gage, GJ}, title = {Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.}, journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience}, volume = {22}, number = {3}, pages = {A197-A206}, pmid = {39355672}, issn = {1544-2896}, abstract = {Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.}, }
@article {pmid39355516, year = {2024}, author = {von Groll, VG and Leeuwis, N and Rimbert, S and Roc, A and Pillette, L and Lotte, F and Alimardani, M}, title = {Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {11}, number = {3}, pages = {87-97}, pmid = {39355516}, issn = {2326-263X}, abstract = {The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.}, }
@article {pmid39354145, year = {2024}, author = {Lian, YN and Cao, XW and Wu, C and Pei, CY and Liu, L and Zhang, C and Li, XY}, title = {Deconstruction the feedforward inhibition changes in the layer III of anterior cingulate cortex after peripheral nerve injury.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {1237}, pmid = {39354145}, issn = {2399-3642}, mesh = {Animals ; *Gyrus Cinguli/physiopathology ; *Peripheral Nerve Injuries/physiopathology ; Mice ; Male ; Mice, Inbred C57BL ; Neural Inhibition ; Neurons/physiology ; Peroneal Nerve/injuries/physiopathology ; Thalamus/physiopathology ; }, abstract = {The anterior cingulate cortex (ACC) is one of the critical brain areas for processing noxious information. Previous studies showed that peripheral nerve injury induced broad changes in the ACC, contributing to pain hypersensitivity. The neurons in layer 3 (L3) of the ACC receive the inputs from the mediodorsal thalamus (MD) and form the feedforward inhibition (FFI) microcircuits. The effects of peripheral nerve injury on the MD-driven FFI in L3 of ACC are unknown. In our study, we record the enhanced excitatory synaptic transmissions from the MD to L3 of the ACC in mice with common peroneal nerve ligation, affecting FFI. Chemogenetically activating the MD-to-ACC projections induces pain sensitivity and place aversion in naive mice. Furthermore, chemogenetically inactivating MD-to-ACC projections decreases pain sensitivity and promotes place preference in nerve-injured mice. Our results indicate that the peripheral nerve injury changes the MD-to-ACC projections, contributing to pain hypersensitivity and aversion.}, }
@article {pmid39352734, year = {2024}, author = {Gao, Y and Cai, YC and Liu, DY and Yu, J and Wang, J and Li, M and Xu, B and Wang, T and Chen, G and Northoff, G and Bai, R and Song, XM}, title = {GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {39352734}, issn = {2050-084X}, support = {2021ZD0200401//STI 2030 - Major Projects/ ; 2022ZD0206000//STI 2030 - Major Projects/ ; 61876222//The National Natural Science Foundation of China/ ; 82222032//The National Natural Science Foundation of China/ ; U1909205//The National Natural Science Foundation of China/ ; 785907//Horizon 2020 Framework Programme/ ; 20YJC880095//Humanities and Social Sciences Ministry of Education/ ; 18YJA190001//Humanities and Social Sciences Ministry of Education/ ; 2022C03096//The Key R&amp;D Program of Zhejiang/ ; 2022ZJJH02-06//The Key R&amp;D Program of Zhejiang/ ; 32000761//The National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Frontal Lobe/physiology/diagnostic imaging ; Male ; *gamma-Aminobutyric Acid/metabolism ; *Intelligence/physiology ; Female ; Young Adult ; *Magnetic Resonance Imaging ; Adult ; Magnetic Resonance Spectroscopy/methods ; Space Perception/physiology ; }, abstract = {The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.}, }
@article {pmid39351695, year = {2024}, author = {Wang, PS and Yang, XX and Wei, Q and Lv, YT and Wu, ZY and Li, HF}, title = {Clinical characterization and founder effect analysis in Chinese amyotrophic lateral sclerosis patients with SOD1 common variants.}, journal = {Annals of medicine}, volume = {56}, number = {1}, pages = {2407522}, pmid = {39351695}, issn = {1365-2060}, mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Age of Onset ; *Amyotrophic Lateral Sclerosis/genetics ; China/epidemiology ; East Asian People ; Exome Sequencing ; *Founder Effect ; Genetic Association Studies ; Haplotypes ; Mutation ; Phenotype ; *Superoxide Dismutase-1/genetics ; }, abstract = {OBJECTIVE: In the Asian population, SOD1 variants are the most common cause of amyotrophic lateral sclerosis (ALS). To date, more than 200 variants have been reported in SOD1. This study aimed to summarize the genotype-phenotype correlation and determine whether the patients carrying common variants derive from a common ancestor.<br><br>METHODS: A total of 103 sporadic ALS (SALS) and 11 familial ALS (FALS) probands were included and variants were screened by whole exome sequencing. Functional analyses were performed on fibroblasts derived from patients with SOD1 p.V48A and control. Haplotype analysis was performed in the probands with p.H47R or p.V48A and their familial members.<br><br>RESULTS: A total of 25 SOD1 variants were identified in 44 probands, in which p.H47R, p.V48A and p.C112Y variants were the most common variants. 94.3% and 60% of patients with p.H47R or p.V48A had lower limb onset with predominant lower motor neurons (LMNs) involvement. Patients with p.H47R had a slow progression and prolonged survival time, while patients with p.V48A exhibited a duration of 2-5&#xa0;years. Patients with p.C112Y variant showed remarkable phenotypic variation in age at onset and disease course. SOD1[V48A] fibroblasts showed mutant SOD1 aggregate formation, enhanced intracellular reactive oxygen species level, and decreased mitochondrial membrane potential compared to the control fibroblast. Haplotype analysis showed that seven families had two different haplotypes. p.H47R and p.V48A variants did not originate from a common founder.<br><br>CONCLUSIONS: Our study expanded the understanding of the genotype-phenotype correlation of ALS with SOD1 variants and revealed that the common p.H47R or p.V48A variant did not have a founder effect.}, }
@article {pmid39350409, year = {2024}, author = {Pang, M and Yao, H and Bao, K and Xu, R and Xi, R and Peng, R and Zhi, H and Zhang, K and He, R and Du, Y and Su, Y and Liu, X and Ming, D}, title = {Phenolic Glycoside Monomer from Reed Rhizome Inhibits Melanin Production via PI3K-Akt and Ras-Raf-MEK-ERK Pathways.}, journal = {Current medicinal chemistry}, volume = {}, number = {}, pages = {}, doi = {10.2174/0109298673341645240919072455}, pmid = {39350409}, issn = {1875-533X}, abstract = {INTRODUCTION: Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders.<br><br>METHOD: In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-&#x3b2;-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% &#xb1; 14.00% and tyrosinase in vivo by 48.26% &#xb1; 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5.<br><br>RESULTS: Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis.<br><br>CONCLUSION: These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation.}, }
@article {pmid39350194, year = {2024}, author = {Du, YC and Ma, LH and Li, QF and Ma, Y and Dong, Y and Wu, ZY}, title = {Genotype-phenotype correlation and founder effect analysis in southeast Chinese patients with sialidosis type I.}, journal = {Orphanet journal of rare diseases}, volume = {19}, number = {1}, pages = {362}, pmid = {39350194}, issn = {1750-1172}, support = {82230062, 82071260//National Natural Science Foundation of China/ ; }, mesh = {Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; China/epidemiology ; East Asian People ; *Founder Effect ; Genetic Association Studies ; Genotype ; Haplotypes ; *Mucolipidoses/genetics ; Mutation ; Neuraminidase/genetics ; Polymorphism, Single Nucleotide ; }, abstract = {BACKGROUND: Sialidosis type 1 (ST-1) is a rare autosomal recessive disorder caused by mutation in the NEU1 gene. However, limited reports on ST-1 patients in the Chinese mainland are available.<br><br>METHODS: This study reported the genetic and clinical characteristics of 10 ST-1 patients from southeastern China. A haplotype analysis was performed using 21 single nucleotide polymorphism (SNP) markers of 500&#xa0;kb flanking the recurrent c.544&#xa0;A&#x2009;>&#x2009;G in 8 families harboring the mutation. Furthermore, this study summarized and compared previously reported ST-1 patients from Taiwan and mainland China.<br><br>RESULTS: Five mutations within NEU1 were found, including two novel ones c.557&#xa0;A&#x2009;>&#x2009;G and c.799&#xa0;C&#x2009;>&#x2009;T. The c.544&#xa0;A&#x2009;>&#x2009;G mutation was most frequent and identified in 9 patients, 6 patients were homozygous for c.544&#xa0;A&#x2009;>&#x2009;G. Haplotype analysis revealed a shared haplotype surrounding c.544&#xa0;A&#x2009;>&#x2009;G was identified, suggesting a founder effect presenting in southeast Chinese population. Through detailed assessment, 52 ST-1 patients from 45 families from Taiwan and mainland China were included. Homozygous c.544&#xa0;A&#x2009;>&#x2009;G was the most common genotype and found in 42.2% of the families, followed by the c.544&#xa0;A&#x2009;>&#x2009;G/c.239&#xa0;C&#x2009;>&#x2009;T compound genotype, which was observed in 22.2% of the families. ST-1 patients with the homozygous c.544&#xa0;A&#x2009;>&#x2009;G mutation developed the disease at a later age and had a lower incidence of cherry-red spots significantly.<br><br>CONCLUSION: The results contribute to gaps in the clinical and genetic features of ST-1 patients in southeastern mainland China and provide a deeper understanding of this disease to reduce misdiagnosis.}, }
@article {pmid39349841, year = {2024}, author = {Fan, J and Gao, Z}, title = {Promoting glymphatic flow: A non-invasive strategy using 40 Hz light flickering.}, journal = {Purinergic signalling}, volume = {}, number = {}, pages = {}, pmid = {39349841}, issn = {1573-9546}, support = {2070974//National Natural Science Foundation of China/ ; 2070974//National Natural Science Foundation of China/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {The glymphatic system is critical for brain homeostasis by eliminating metabolic waste, whose disturbance contributes to the accumulation of pathogenic proteins in neurodegenerative diseases. Promoting glymphatic clearance is a potential and attractive strategy for several brain disorders, including neurodegenerative diseases. Previous studies have uncovered that 40 Hz flickering augmented glymphatic flow and facilitated sleep (Zhou et al. in Cell Res 34:214-231, 2024) since sleep drives waste clearance via glymphatic flow (Xie et al. in Science 342:373-377, 2013). However, it remains unclear whether 40 Hz light flickering directly increased glymphatic flow or indirectly by promoting sleep. A recent article published in Cell Discovery by Chen et al. (Sun et al. in Cell Discov 10:81, 2024) revealed that 40 Hz light flickering facilitated glymphatic flow, by promoting the polarization of astrocytic aquaporin-4 (AQP4) and vasomotion through upregulated adenosine-A2A receptor (A2AR) signaling, independent of sleep. These findings suggest that 40 Hz light flickering may be used as a non-invasive approach to control the function of the glymphatic-lymphatic system, to help remove metabolic waste in the brain, thereby presenting a potential strategy for neurodegenerative disease treatment.}, }
@article {pmid39349588, year = {2024}, author = {Chen, X and Cao, L and Haendel, BF}, title = {Right visual field advantage in orientation discrimination is influenced by biased suppression.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22687}, pmid = {39349588}, issn = {2045-2322}, support = {201908060012//China Scholarship Council/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China/ ; 32271078//National Natural Science Foundation of China/ ; 677819/ERC_/European Research Council/International ; }, mesh = {Humans ; *Visual Fields/physiology ; Male ; Female ; Adult ; *Reaction Time/physiology ; *Electroencephalography/methods ; Young Adult ; Orientation/physiology ; Orientation, Spatial/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; Visual Perception/physiology ; Functional Laterality/physiology ; Walking/physiology ; Attention/physiology ; }, abstract = {Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.}, }
@article {pmid39349502, year = {2024}, author = {Fan, Y and Tao, Y and Wang, J and Gao, Y and Wei, W and Zheng, C and Zhang, X and Song, XM and Northoff, G}, title = {Irregularity of visual motion perception and negative symptoms in schizophrenia.}, journal = {Schizophrenia (Heidelberg, Germany)}, volume = {10}, number = {1}, pages = {82}, pmid = {39349502}, issn = {2754-6993}, support = {LR23E070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82001410//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.}, }
@article {pmid39347924, year = {2024}, author = {Yan, ZN and Liu, PR and Zhou, H and Zhang, JY and Liu, SX and Xie, Y and Wang, HL and Yu, JB and Zhou, Y and Ni, CM and Huang, L and Ye, ZW}, title = {Brain-computer Interaction in the Smart Era.}, journal = {Current medical science}, volume = {44}, number = {6}, pages = {1123-1131}, pmid = {39347924}, issn = {2523-899X}, mesh = {Humans ; Artificial Intelligence ; *Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Machine Learning ; }, abstract = {The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.}, }
@article {pmid39346532, year = {2024}, author = {Zou, J and Chen, H and Chen, X and Lin, Z and Yang, Q and Tie, C and Wang, H and Niu, L and Guo, Y and Zheng, H}, title = {Noninvasive closed-loop acoustic brain-computer interface for seizure control.}, journal = {Theranostics}, volume = {14}, number = {15}, pages = {5965-5981}, pmid = {39346532}, issn = {1838-7640}, mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Seizures/physiopathology/therapy ; *Electroencephalography/methods ; Rats, Sprague-Dawley ; Vagus Nerve Stimulation/methods ; Disease Models, Animal ; Male ; Hippocampus/physiopathology ; Vagus Nerve/physiology ; Epilepsy/therapy/physiopathology ; Brain/physiopathology/physiology ; }, abstract = {Rationale: The brain-computer interface (BCI) is core tasks in comprehensively understanding the brain, and is one of the most significant challenges in neuroscience. The development of novel non-invasive neuromodulation technique will drive major innovations and breakthroughs in the field of BCI. Methods: We develop a new noninvasive closed-loop acoustic brain-computer interface (aBCI) for decoding the seizure onset based on the electroencephalography and triggering ultrasound stimulation of the vagus nerve to terminate seizures. Firstly, we create the aBCI system and decode the onset of seizure via a multi-level threshold model based on the analysis of wireless-collected electroencephalogram (EEG) signals recorded from above the hippocampus. Then, the different acoustic parameters induced acoustic radiation force were used to stimulate the vagus nerve in a rat model of epilepsy-induced by pentylenetetrazole. Finally, the results of epileptic EEG signal triggering ultrasound stimulation of the vagus nerve to control seizures. In addition, the mechanism of aBCI control seizures were investigated by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In a rat model of epilepsy, the aBCI system selectively actives mechanosensitive neurons in the nodose ganglion while suppressing neuronal excitability in the hippocampus and amygdala, and stops seizures rapidly upon ultrasound stimulation of the vagus nerve. Physical transection or chemical blockade of the vagus nerve pathway abolish the antiepileptic effects of aBCI. In addition, aBCI shows significant antiepileptic effects compared to conventional vagus nerve electrical stimulation in an acute experiment. Conclusions: Closed-loop aBCI provides a novel, safe and effective tool for on-demand stimulation to treat abnormal neuronal discharges, opening the door to next generation non-invasive BCI.}, }
@article {pmid39346088, year = {2024}, author = {Zhu, L and Zhang, Q and Ni, K and Yang, XJ and Jin, K and Wei, W and Preece, DA and Li, BM and Cai, XL}, title = {Assessing Emotion Regulation Difficulties Across Negative and Positive Emotions: Psychometric Properties and Clinical Applications of the Perth Emotion Regulation Competency Inventory in the Chinese Context.}, journal = {Psychology research and behavior management}, volume = {17}, number = {}, pages = {3299-3311}, pmid = {39346088}, issn = {1179-1578}, abstract = {BACKGROUND: Abnormalities of regulating positive and negative emotion have been documented in patients with mental disorders. Valid and reliable psychological instruments for measuring emotion regulation across different valences are needed. The Perth Emotion Regulation Competency Inventory (PERCI) is a 32-item self-report measure recently developed to compressively assess emotion regulation ability across both positive and negative valences.<br><br>PURPOSE: This study aimed to validate the Chinese PERCI in a large non-clinical sample and examine the clinical utility in patients with major depressive disorder (MDD).<br><br>METHODS: The Chinese PERCI was administered to 1090 Chinese participants (mean age = 20.64 years, 773 females). The factor structure, internal consistency, test-retest reliability, convergent validity, concurrent validity, and predictive validity were examined. Moreover, a MDD group (n = 50) and a matched healthy control group (n = 50) were recruited. Group comparisons and the linear discriminant analysis were conducted to assess the clinical relevance of the PERCI.<br><br>RESULTS: Confirmatory factor analysis supported the intended eight-factor structure of the PERCI in the Chinese population. The PERCI showed high internal consistency, test-retest reliability, as well as good convergent and concurrent validity. The MDD group had significantly higher PERCI scores than the healthy control group. Linear discriminant function comprised of the eight factors successfully distinguish patients with MDD from their matched controls.<br><br>CONCLUSION: The Chinese version of the PERCI is a valid and reliable instrument to compressively measure emotion regulation across positive and negative valences in the general Chinese population and patients with depression.}, }
@article {pmid39345641, year = {2024}, author = {Karpowicz, BM and Ye, J and Fan, C and Tostado-Marcos, P and Rizzoglio, F and Washington, C and Scodeler, T and de Lucena, D and Nason-Tomaszewski, SR and Mender, MJ and Ma, X and Arneodo, EM and Hochberg, LR and Chestek, CA and Henderson, JM and Gentner, TQ and Gilja, V and Miller, LE and Rouse, AG and Gaunt, RA and Collinger, JL and Pandarinath, C}, title = {Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.09.15.613126}, pmid = {39345641}, issn = {2692-8205}, support = {R01 DC018446/DC/NIDCD NIH HHS/United States ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices.}, }
@article {pmid39345497, year = {2024}, author = {Gedela, NSS and Salim, S and Radawiec, RD and Richie, J and Chestek, C and Draelos, A and Pelled, G}, title = {Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39345497}, issn = {2692-8205}, support = {R01 NS098231/NS/NINDS NIH HHS/United States ; UF1 NS115817/NS/NINDS NIH HHS/United States ; }, abstract = {The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.}, }
@article {pmid39345413, year = {2024}, author = {Wu, M and Yang, Y and Zhang, J and Efimov, AI and Vazquez-Guardado, A and Li, X and Zhang, K and Wang, Y and Gu, J and Zeng, L and Liu, J and Riahi, M and Yoon, H and Kim, M and Zhang, H and Lee, M and Kang, J and Ting, K and Cheng, S and Zhang, W and Banks, A and Good, CH and Cox, JM and Pinto, L and Huang, Y and Kozorovitskiy, Y and Rogers, JA}, title = {Patterned wireless transcranial optogenetics generates artificial perception.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.09.20.613966}, pmid = {39345413}, issn = {2692-8205}, support = {R01 MH117111/MH/NIMH NIH HHS/United States ; R01 NS107539/NS/NINDS NIH HHS/United States ; T32 MH067564/MH/NIMH NIH HHS/United States ; }, abstract = {Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in the development of next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective, and miniaturized platform with long-term stability is crucial for creating a clinically meaningful interface capable of mediating artificial perceptual feedback. In this study, we demonstrate a miniaturized fully implantable wireless transcranial optogenetic encoder designed to generate artificial perceptions through digitized optogenetic manipulation of large cortical ensembles. This platform enables the spatiotemporal orchestration of large-scale cortical activity for remote perception genesis via real-time wireless communication and control, with optimized device performance achieved by simulation-guided methods addressing light and heat propagation during operation. Cue discrimination during operant learning demonstrates the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination performance reveal principles that adhere to general perceptual rules. These conceptual and technical advancements expand our understanding of artificial neural syntax and its perception by the brain, guiding the evolution of next-generation brain-machine communication.}, }
@article {pmid39345118, year = {2025}, author = {Rustamov, N and Souders, L and Sheehan, L and Carter, A and Leuthardt, EC}, title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.}, journal = {Neurorehabilitation and neural repair}, volume = {39}, number = {1}, pages = {74-86}, pmid = {39345118}, issn = {1552-6844}, support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R21 NS102696/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Male ; Middle Aged ; Female ; *Upper Extremity/physiopathology ; Aged ; *Stroke/physiopathology/complications ; Chronic Disease ; Adult ; Recovery of Function/physiology ; Electroencephalography ; Paresis/rehabilitation/physiopathology/etiology ; Motor Activity/physiology ; Gamma Rhythm/physiology ; Theta Rhythm/physiology ; Prospective Studies ; }, abstract = {BACKGROUND: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.<br><br>OBJECTIVES: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.<br><br>METHODS: Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12&#x2009;weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.<br><br>RESULTS: Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.<br><br>CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.<br><br>TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.}, }
@article {pmid39342695, year = {2024}, author = {Wang, J and Ning, X and Xu, W and Li, Y and Jia, Z and Lin, Y}, title = {Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106742}, doi = {10.1016/j.neunet.2024.106742}, pmid = {39342695}, issn = {1879-2782}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Brain/physiology ; }, abstract = {Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.}, }
@article {pmid39342624, year = {2024}, author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Ahanov, O and Kapageridou, E and Savopoulos, C and Didangelos, T}, title = {Dimensionality and psychometric properties of the Greek version of the Diabetes Impact and Device Satisfaction (DIDS) scale.}, journal = {Psychiatrike = Psychiatriki}, volume = {35}, number = {4}, pages = {314-323}, doi = {10.22365/jpsych.2024.017}, pmid = {39342624}, issn = {1105-2333}, mesh = {Humans ; *Psychometrics/instrumentation/methods ; Female ; Male ; Adult ; *Diabetes Mellitus, Type 1/psychology/drug therapy ; Greece ; Reproducibility of Results ; Middle Aged ; *Patient Satisfaction ; *Quality of Life ; Surveys and Questionnaires ; Insulin Infusion Systems ; Insulin/administration &amp; dosage ; }, abstract = {Type 1 diabetes mellitus (T1D) is a chronic condition with rising prevalence. The only treatment for individuals with T1D to prevent diabetes-related complications is exogenous insulin administration. Diabetes-related technology has significantly contributed to the management of T1D by reducing the burden of living with diabetes and providing greater flexibility in insulin management during daily activities. This study presents the psychometric properties of the Greek translation of the Diabetes Impact and Device Satisfaction (DIDS) Scale, which assesses satisfaction with the use of an insulin delivery device and the impact of diabetes management on individuals with T1D. A sample of 101 adults with T1D, mostly females (71.3%), with a mean age of 38.4 years (± 11.7), completed the translated Greek version of DIDS (DIDS-Gr). Exploratory factor analysis revealed three factors: 'Device Satisfaction', 'Diabetes Management Impact', and (new factor) 'Device Usability'. The internal consistency indices (Cronbach's alpha) for the subscales were 0.86, 0.71, and 0.60, respectively. Furthermore, convergent validity was demonstrated with moderate to high positive correlations between the DIDS-Grand the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and its subscales, while divergent validity was also confirmed with weaker correlations with the depression subscale of the Hospital Anxiety and Depression Scale (HADS). Additionally, test-retest reliability and differential validity were present in our study. Therefore, DIDS-Gr is a valid and reliable measure for assessing the impact of diabetes on individuals with T1D and the satisfaction with the use of an insulin delivery device in Greece.}, }
@article {pmid39341475, year = {2024}, author = {Luo, C and Ding, N}, title = {Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes.}, journal = {NeuroImage}, volume = {300}, number = {}, pages = {120875}, doi = {10.1016/j.neuroimage.2024.120875}, pmid = {39341475}, issn = {1095-9572}, mesh = {Humans ; *Speech Perception/physiology ; Male ; Female ; *Electroencephalography/methods ; Young Adult ; Adult ; Cerebral Cortex/physiology ; Linguistics ; Acoustic Stimulation ; }, abstract = {In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.}, }
@article {pmid39338869, year = {2024}, author = {Miladinović, A and Accardo, A and Jarmolowska, J and Marusic, U and Ajčević, M}, title = {Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338869}, issn = {1424-8220}, support = {V-A Italia-Slovenia 2014-2020//Interreg/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Support Vector Machine ; *Stroke/physiopathology ; Male ; Female ; Algorithms ; Middle Aged ; Stroke Rehabilitation/methods ; Aged ; Discriminant Analysis ; Time Factors ; }, abstract = {Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1-2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.}, }
@article {pmid39338854, year = {2024}, author = {Frosolone, M and Prevete, R and Ognibeni, L and Giugliano, S and Apicella, A and Pezzulo, G and Donnarumma, F}, title = {Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338854}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.}, }
@article {pmid39338748, year = {2024}, author = {Gulyás, D and Jochumsen, M}, title = {Detection of Movement-Related Brain Activity Associated with Hand and Tongue Movements from Single-Trial Around-Ear EEG.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338748}, issn = {1424-8220}, mesh = {Humans ; *Tongue/physiology ; *Electroencephalography/methods ; *Movement/physiology ; *Brain-Computer Interfaces ; *Hand/physiology ; Male ; Adult ; Female ; Brain/physiology ; Support Vector Machine ; Young Adult ; Ear/physiology ; }, abstract = {Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.}, }
@article {pmid39338733, year = {2024}, author = {Huang, J and Chang, Y and Li, W and Tong, J and Du, S}, title = {A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338733}, issn = {1424-8220}, support = {No.81705//Shengzhi Du/ ; Grant No.NCRCOP20230013//Yinghui Chang/ ; No. 2021ZD011//Jigang Tong/ ; No. 62303244//Wenyu Li/ ; No. 22JCQNJC01440//Wenyu Li/ ; 111-2221-E-A49 -126 -MY3//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112-2218-E-A49-027//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-002-013//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2640-E-A49-009//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-A49-028//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112UC2N006//Satellite Communications and AIoT Research Center/The Co-operation Platform of the Industry-Academia Innovation School, National Yang Ming Chiao Tung University (NYCU), Taiwan R.OC./ ; }, mesh = {*Electroencephalography/methods ; *Neural Networks, Computer ; Humans ; *Imagination/physiology ; *Semantics ; *Brain-Computer Interfaces ; *Algorithms ; Perception/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.}, }
@article {pmid39338628, year = {2024}, author = {Mohajelin, F and Sheykhivand, S and Shabani, A and Danishvar, M and Danishvar, S and Lahijan, LZ}, title = {Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338628}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.}, }
@article {pmid39338620, year = {2024}, author = {Razzaq, Z and Brahimi, N and Rehman, HZU and Khan, ZH}, title = {Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {18}, pages = {}, pmid = {39338620}, issn = {1424-8220}, mesh = {*Fuzzy Logic ; *Robotics/methods ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Algorithms ; Brain/physiology ; Neural Networks, Computer ; Machine Learning ; }, abstract = {Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.}, }
@article {pmid39335353, year = {2024}, author = {Matulyte, G and Parciauskaite, V and Bjekic, J and Pipinis, E and Griskova-Bulanova, I}, title = {Gamma-Band Auditory Steady-State Response and Attention: A Systemic Review.}, journal = {Brain sciences}, volume = {14}, number = {9}, pages = {}, pmid = {39335353}, issn = {2076-3425}, abstract = {Auditory steady-state response (ASSR) is the result of the brain's ability to follow and entrain its oscillatory activity to the phase and frequency of periodic auditory stimulation. Gamma-band ASSR has been increasingly investigated with intentions to apply it in neuropsychiatric disorders diagnosis as well as in brain-computer interface technologies. However, it is still debatable whether attention can influence ASSR, as the results of the attention effects of ASSR are equivocal. In our study, we aimed to systemically review all known articles related to the attentional modulation of gamma-band ASSRs. The initial literature search resulted in 1283 papers. After the removal of duplicates and ineligible articles, 49 original studies were included in the final analysis. Most analyzed studies demonstrated ASSR modulation with differing attention levels; however, studies providing mixed or non-significant results were also identified. The high versatility of methodological approaches including the utilized stimulus type and ASSR recording modality, as well as tasks employed to modulate attention, were detected and emphasized as the main causality of result inconsistencies across studies. Also, the impact of training, inter-individual variability, and time of focus was addressed.}, }
@article {pmid39332212, year = {2024}, author = {Deng, Y and Ji, Z and Wang, Y and Zhou, SK}, title = {OS-SSVEP: One-shot SSVEP classification.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106734}, doi = {10.1016/j.neunet.2024.106734}, pmid = {39332212}, issn = {1879-2782}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Neural Networks, Computer ; Discriminant Analysis ; Least-Squares Analysis ; Photic Stimulation/methods ; Adult ; Male ; Calibration ; Algorithms ; }, abstract = {It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject. Specifically, CSDuDoFN uses multi-reference least-squares transformation (MLST) to map data from both the source subjects and the target subject into the domain of sine-cosine templates, thereby reducing cross-subject domain gap and benefiting transfer learning. In addition, CSDuDoFN is fed with both transformed and original data, with an adequate fusion of their features occurring at different network layers. To capitalize on the calibration trial of the target subject, OS-SSVEP utilizes source aliasing matrix estimation (SAME)-based data augmentation to incorporate into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the SSVEP-based brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.}, }
@article {pmid39332118, year = {2024}, author = {Crell, MR and Müller-Putz, GR}, title = {Handwritten character classification from EEG through continuous kinematic decoding.}, journal = {Computers in biology and medicine}, volume = {182}, number = {}, pages = {109132}, doi = {10.1016/j.compbiomed.2024.109132}, pmid = {39332118}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Handwriting ; Biomechanical Phenomena/physiology ; Female ; Male ; Adult ; Signal Processing, Computer-Assisted ; Hand/physiology ; Young Adult ; }, abstract = {The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants, offering new insights into the neural correlates of handwriting. Letters were classified with two methods: the direct classification from low-frequency and broadband electroencephalogram (EEG) and a two-step approach comprising the continuous decoding of hand kinematics and the application of those in subsequent classification. The two-step approach poses a novel application of continuous movement decoding for the classification of letters from EEG. When using low-frequency EEG, results show moderate accuracies of 23.1% for ten letters and 39.0% for a subset of five letters with highest discriminability of the trajectories. The two-step approach yielded significantly higher performances of 26.2% for ten letters and 46.7% for the subset of five letters. Hand kinematics could be reconstructed with a correlation of 0.10 to 0.57 (average chance level: 0.04) between the decoded and original kinematic. The study shows the general feasibility of extracting handwritten letters from non-invasively recorded neural signals and indicates that the proposed two-step approach can improve performances. As an exploratory investigation of the neural mechanisms of handwriting in EEG, we found significant influence of the written letter on the low-frequency components of neural signals. Differences between letters occurred mostly in central and occipital channels. Further, our results suggest movement speed as the most informative kinematic for the decoding of short hand movements.}, }
@article {pmid39331412, year = {2024}, author = {Wang, S and Jiang, Q and Liu, H and Yu, C and Li, P and Pan, G and Xu, K and Xiao, R and Hao, Y and Wang, C and Song, J}, title = {Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {40}, pages = {e2403380121}, pmid = {39331412}, issn = {1091-6490}, support = {2021ZD0200401//STI 2030-Major Projects/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; U20A6001//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; }, mesh = {Animals ; Rats ; *Electrodes, Implanted ; Electrophysiological Phenomena ; Polymers/chemistry ; Cerebral Cortex/physiology ; Neurons/physiology ; Rats, Sprague-Dawley ; Brain/physiology ; }, abstract = {Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.}, }
@article {pmid39331031, year = {2024}, author = {Wang, W and Liu, Y and Wang, G and Cheng, Q and Ming, D}, title = {Oscillatory cortico-cortical connectivity during tactile discrimination between dynamic and static stimulation.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {9}, pages = {}, doi = {10.1093/cercor/bhae277}, pmid = {39331031}, issn = {1460-2199}, support = {2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; *Electroencephalography ; *Touch Perception/physiology ; Adult ; Young Adult ; Cerebral Cortex/physiology ; Discrimination, Psychological/physiology ; Touch/physiology ; Neural Pathways/physiology ; Physical Stimulation ; Brain Mapping ; Evoked Potentials/physiology ; }, abstract = {Fine sensory modalities play an essential role in perceiving the world. However, little is known about how the cortico-cortical distinguishes between dynamic and static tactile signals. This study investigated oscillatory connectivity during a tactile discrimination task of dynamic and static stimulation via electroencephalogram (EEG) recordings and the fast oscillatory networks across widespread cortical regions. While undergoing EEG recordings, the subject felt an electro-tactile presented by a 3-dot array. Each block consisted of 3 forms of stimulation: Spatio-temporal (dynamic), Spatial (static), and Control condition (lack of electrical stimulation). The average event-related potential for the Spatial and Spatio-temporal conditions exhibited statistically significant differences between 25 and 75, 81 and 121, 174 and 204 and 459 and 489 ms after stimulus onset. Based on those times, the sLORETA approach was used to reconstruct the inverse solutions of EEG. Source localization appeared superior parietal at around 25 to 75 ms, in the primary motor cortex at 81 to 121 ms, in the central prefrontal cortex at 174 to 204 and 459 to 489 ms. To better assess spectral brain functional connectivity, we selected frequency ranges with correspondingly significant differences: for static tactile stimulation, these are concentrated in the Theta, Alpha, and Gamma bands, whereas for dynamic stimulation, the relative energy change bands are focused on the Theta and Alpha bands. These nodes' functional connectivity analysis (phase lag index) showed 3 distinct distributed networks. A tactile information discrimination network linked the Occipital lobe, Prefrontal lobe, and Postcentral gyrus. A tactile feedback network linked the Prefrontal lobe, Postcentral gyrus, and Temporal lobe. A dominant motor feedforward loop network linked the Parietal cortex, Prefrontal lobe, Frontal lobe, and Parietal cortex. Processing dynamic and static tactile signals involves discriminating tactile information, motion planning, and cognitive decision processing.}, }
@article {pmid39330094, year = {2024}, author = {Ruan, Z and Li, H}, title = {Two Levels of Integrated Information Theory: From Autonomous Systems to Conscious Life.}, journal = {Entropy (Basel, Switzerland)}, volume = {26}, number = {9}, pages = {}, pmid = {39330094}, issn = {1099-4300}, support = {22&amp;ZD034//the Major Program of National Social Science Fund of China/ ; }, abstract = {Integrated Information Theory (IIT) is one of the most prominent candidates for a theory of consciousness, although it has received much criticism for trying to live up to expectations. Based on the relevance of three issues generalized from the developments of IITs, we have summarized the main ideas of IIT into two levels. At the second level, IIT claims to be strictly anchoring consciousness, but the first level on which it is based is more about autonomous systems or systems that have reached some other critical complexity. In this paper, we argue that the clear gap between the two levels of explanation of IIT has led to these criticisms and that its panpsychist tendency plays a crucial role in this. We suggest that the problems of IIT are far from being "pseudoscience", and by adding more necessary elements, when the first level is combined with the second level, IIT can genuinely move toward an appropriate theory of consciousness that can provide necessary and sufficient interpretations.}, }
@article {pmid39329668, year = {2024}, author = {Tan, X and Wang, D and Xu, M and Chen, J and Wu, S}, title = {Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {9}, pages = {}, pmid = {39329668}, issn = {2306-5354}, support = {12275295//National Natural Science Foundation of China/ ; GZC20230189//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; }, abstract = {Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.}, }
@article {pmid39329659, year = {2024}, author = {Hiwaki, O}, title = {Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {9}, pages = {}, pmid = {39329659}, issn = {2306-5354}, support = {22KK-128//Mazda Motor Corporation (Japan)/ ; }, abstract = {Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.}, }
@article {pmid39329584, year = {2024}, author = {Mounesi Rad, S and Danishvar, S}, title = {Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {9}, pages = {}, pmid = {39329584}, issn = {2313-7673}, abstract = {Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain-computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.}, }
@article {pmid39328074, year = {2024}, author = {Sawyer, A and Cooke, L and Breyman, E and Spohn, S and Edelman, S and Saravanan, K and Putrino, D}, title = {Meeting the Needs of People With Severe Quadriplegia in the 21st Century: The Case for Implanted Brain-Computer Interfaces.}, journal = {Neurorehabilitation and neural repair}, volume = {38}, number = {11-12}, pages = {877-886}, doi = {10.1177/15459683241282783}, pmid = {39328074}, issn = {1552-6844}, mesh = {Humans ; *Quadriplegia/rehabilitation ; *Brain-Computer Interfaces ; *Self-Help Devices ; }, abstract = {BACKGROUND: In recent decades, there has been a widespread adoption of digital devices among the non-disabled population. The pervasive integration of digital devices has revolutionized how the majority of the population manages daily activities. Most of us now depend on digital platforms and services to conduct activities across the domains of communication, finance, healthcare, and work. However, a clear disparity exists for people who live with severe quadriplegia, who largely lack access to tools that would enable them to perform daily tasks digitally and communicate effectively with their environment.<br><br>OBJECTIVES: The purpose of this piece is to (i) highlight the unmet needs of people with severe quadriplegia (including cases for medical necessity and perspectives from the community), (ii) present the current landscape of assistive technology for people with severe quadriplegia, (iii) make the case for implantable BCIs (how they address needs and why they are a good solution relative to other assistive technologies), and (iv) present future directions.<br><br>RESULTS: There are technologies that are currently available to this population, but these technologies are certainly not usable with the same level of ease, efficiency, or autonomy as what has been designed for the non-disabled community. This hinders the ability of people with severe quadriplegia to achieve digital autonomy, perpetuating social isolation and limiting the expression of needs, opinions, and preferences.<br><br>CONCLUSION: Most importantly, the gap in digital equality fundamentally undermines the basic human rights of people with severe quadriplegia.}, }
@article {pmid39326769, year = {2024}, author = {Wu, X and Xie, C and Cheng, F and Li, Z and Li, R and Xu, D and Kim, H and Zhang, J and Liu, H and Liu, M}, title = {Comparative evaluation of interpretation methods in surface-based age prediction for neonates.}, journal = {NeuroImage}, volume = {300}, number = {}, pages = {120861}, doi = {10.1016/j.neuroimage.2024.120861}, pmid = {39326769}, issn = {1095-9572}, mesh = {Humans ; Infant, Newborn ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging ; Female ; Male ; *Deep Learning ; Image Interpretation, Computer-Assisted/methods ; Neuroimaging/methods/standards ; }, abstract = {Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods-regional age prediction and the perturbation-based saliency map approach-for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.}, }
@article {pmid39326569, year = {2025}, author = {Hurley, ET and Twomey-Kozack, J and Doyle, TR and Meyer, LE and Meyer, AM and Lorentz, SG and Bradley, KE and Dickens, JF and Klifto, CS}, title = {Bioinductive Collagen Implant Has Potential to Improve Rotator Cuff Healing: A Systematic Review.}, journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association}, volume = {41}, number = {2}, pages = {333-342.e2}, doi = {10.1016/j.arthro.2024.09.028}, pmid = {39326569}, issn = {1526-3231}, mesh = {Humans ; *Rotator Cuff Injuries/surgery ; *Collagen/therapeutic use ; *Rotator Cuff/surgery ; *Wound Healing/drug effects ; Prostheses and Implants ; Treatment Outcome ; }, abstract = {PURPOSE: To systematically review the literature to evaluate the clinical studies on bioinductive collagen implant (BCI) for the treatment of rotator cuff tears.<br><br>METHODS: A literature search of MEDLINE, Embase, and the Cochrane Library was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Clinical studies reporting BCI for rotator cuff tears were included. Quantitive and qualitative data were evaluated.<br><br>RESULTS: A total of 21 studies were included. In patients with full-thickness tears, 7 of the 8 studies with pre- to postoperative American Shoulder and Elbow Surgeons (ASES) scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 75% to 100% of patients meeting the minimal clinically important difference. In those with partial-thickness tears, 7 of the 8 studies with pre- to postoperative ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 54.4% to 100% of patients meeting the minimal clinically important difference. For studies that quantified percent increases in tendon thickness, the reported increases ranged from 13% to 44% in full-thickness tears and 14% to 60% in partial-thickness tears. Six studies evaluated rotator cuff retears after BCI treatment in the full-thickness cohort, with rates reported ranging from 0% to 9%. Five studies evaluated rotator cuff retears after BCI treatment in the partial-thickness cohort, with rates reported ranging from 0% to 18%. Two of the included studies found that BCI was cost-effective due to the increased tendon healing, with cost savings of $5,338 to $13,061 per healed rotator cuff tendon.<br><br>CONCLUSIONS: The literature on rotator cuff tear augmentation with BCI has shown consistently reported good results. Additionally, there was evidence of low retear rates and consistently improved tendon thickness with BCI, with 2 randomized controlled trials showing improved tendon healing with BCI. However, there appears to be a higher rate of adhesive capsulitis reported.<br><br>LEVEL OF EVIDENCE: Level IV, systematic review of Level I, III, and IV studies.}, }
@article {pmid39326451, year = {2024}, author = {Erbslöh, A and Buron, L and Ur-Rehman, Z and Musall, S and Hrycak, C and Löhler, P and Klaes, C and Seidl, K and Schiele, G}, title = {Technical survey of end-to-end signal processing in BCIs using invasive MEAs.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad8031}, pmid = {39326451}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Signal Processing, Computer-Assisted ; *Microelectrodes ; Algorithms ; Electrodes, Implanted ; Electroencephalography/methods ; }, abstract = {Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries.This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed on-chip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardware inference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.}, }
@article {pmid39326392, year = {2024}, author = {Magee, P and Ienca, M and Farahany, N}, title = {Beyond neural data: Cognitive biometrics and mental privacy.}, journal = {Neuron}, volume = {112}, number = {18}, pages = {3017-3028}, doi = {10.1016/j.neuron.2024.09.004}, pmid = {39326392}, issn = {1097-4199}, mesh = {Humans ; *Privacy ; *Cognition/physiology ; *Brain-Computer Interfaces/ethics ; Wearable Electronic Devices ; Biometry/methods ; Confidentiality/ethics ; }, abstract = {Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy.}, }
@article {pmid39321842, year = {2024}, author = {Dillen, A and Omidi, M and Ghaffari, F and Vanderborght, B and Roelands, B and Romain, O and Nowé, A and De Pauw, K}, title = {A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad7f8d}, pmid = {39321842}, issn = {1741-2552}, abstract = {Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency. Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected. Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.}, }
@article {pmid39321841, year = {2024}, author = {Wen, X and Jia, S and Han, D and Dong, Y and Gao, C and Cao, R and Hao, Y and Guo, Y and Cao, R}, title = {Filter banks guided correlational convolutional neural network for SSVEPs based BCI classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad7f89}, pmid = {39321841}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/classification ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Electroencephalography/methods/classification ; Adult ; Male ; Photic Stimulation/methods ; Female ; }, abstract = {Objective.In the field of steady-state visual evoked potential brain computer interfaces (SSVEP-BCIs) research, convolutional neural networks (CNNs) have gradually been proved to be an effective method. Whereas, majority works apply the frequency domain characteristics in long time window to train the network, thus lead to insufficient performance of those networks in short time window. Furthermore, only the frequency domain information for classification lacks of other task-related information.Approach.To address these issues, we propose a time-frequency domain generalized filter-bank convolutional neural network (FBCNN-G) to improve the SSVEP-BCIs classification performance. The network integrates multiple frequency information of electroencephalogram (EEG) with template and predefined prior of sine-cosine signals to perform feature extraction, which contains correlation analyses in both template and signal aspects. Then the classification is performed at the end of the network. In addition, the method proposes the use of filter banks divided into specific frequency bands as pre-filters in the network to fully consider the fundamental and harmonic frequency characteristics of the signal.Main results.The proposed FBCNN-G model is compared with other methods on the public dataset Benchmark. The results manifest that this model has higher accuracy of character recognition accuracy and information transfer rates in several time windows. Particularly, in the 0.2 s time window, the mean accuracy of the proposed method reaches62.02%±5.12%, indicating its superior performance.Significance.The proposed FBCNN-G model is critical for the exploitation of SSVEP-BCIs character recognition models.}, }
@article {pmid39321840, year = {2024}, author = {Arif, M and Ur Rehman, F and Sekanina, L and Malik, AS}, title = {A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad7f8e}, pmid = {39321840}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain/physiology ; Artifacts ; Brain-Computer Interfaces ; Machine Learning ; }, abstract = {Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.}, }
@article {pmid39321611, year = {2024}, author = {Wang, X and Qi, H}, title = {Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis.}, journal = {Computer methods and programs in biomedicine}, volume = {257}, number = {}, pages = {108425}, doi = {10.1016/j.cmpb.2024.108425}, pmid = {39321611}, issn = {1872-7565}, mesh = {*Evoked Potentials, Somatosensory/physiology ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography ; Imagination/physiology ; }, abstract = {BACKGROUND AND OBJECTIVE: Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.<br><br>METHODS: The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.<br><br>RESULTS: The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001).<br><br>CONCLUSIONS: The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.}, }
@article {pmid39321571, year = {2024}, author = {Cioffi, E and Hutber, A and Molloy, R and Murden, S and Yurkewich, A and Kirton, A and Lin, JP and Gimeno, H and McClelland, VM}, title = {EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {167}, number = {}, pages = {143-166}, pmid = {39321571}, issn = {1872-8952}, support = {U19 AT002023/AT/NCCIH NIH HHS/United States ; }, mesh = {Humans ; *Neurofeedback/methods ; *Electroencephalography/methods ; *Neurological Rehabilitation/methods ; Child ; Adult ; Sensorimotor Cortex/physiopathology ; Cerebral Palsy/rehabilitation/physiopathology ; }, abstract = {OBJECTIVE: Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies.<br><br>METHODS: MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers.<br><br>RESULTS: Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback.<br><br>CONCLUSIONS: There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback.<br><br>SIGNIFICANCE: The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.}, }
@article {pmid39320995, year = {2025}, author = {Huang, H and Chen, J and Xiao, J and Chen, D and Zhang, J and Pan, J and Li, Y}, title = {Real-Time Attention Regulation and Cognitive Monitoring Using a Wearable EEG-Based BCI.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {2}, pages = {716-724}, doi = {10.1109/TBME.2024.3468351}, pmid = {39320995}, issn = {1558-2531}, mesh = {Humans ; *Electroencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; Male ; *Attention/physiology ; Adult ; Female ; *Cognition/physiology ; Young Adult ; *Signal Processing, Computer-Assisted/instrumentation ; Neurofeedback/physiology/methods ; }, abstract = {OBJECTIVE: Attention regulation is an essential ability in daily life that affects learning and work efficiency and is closely related to mental health. The effectiveness of brain-computer interface (BCI) systems in attention regulation has been proven, but most of these systems rely on bulky and expensive equipment and are still in the experimental stage. This study proposes a wearable BCI system for real-time attention regulation and cognitive monitoring.<br><br>METHODS: The BCI system integrates a wearable single-channel electroencephalogram (EEG) headband with wireless data streaming for real-time analysis. Twenty healthy subjects participated in the long-term attention regulation experiment and were evenly divided into an experimental group and a control group based on the presence of real-time neurofeedback. The neurofeedback is represented by output value of attention, which calculated from single-channel EEG data. Before and after the regulation sessions, baseline assessments were conducted for each subject, incorporating multi-channel EEG data analysis and cognitive behavioral evaluations, to verify the effectiveness of system for attention regulation.<br><br>RESULTS: The online experimental results indicate that the average attention level in the experimental group increased from 0.625 to 0.812, while no significant improvement was observed in the control group. Further comparative analysis revealed the reasons for the enhancement of attention regulation ability in terms of both brain network patterns and cognitive performance.<br><br>SIGNIFICANCE: The proposed wearable BCI system is effective at improving attention regulation ability and could be a promising tool for assisting people with attention disorders.}, }
@article {pmid39318665, year = {2024}, author = {Chai, C and Yang, X and Gao, X and Shi, J and Wang, X and Song, H and Chen, YH and Sawan, M}, title = {Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {12}, number = {}, pages = {1452865}, pmid = {39318665}, issn = {2296-4185}, abstract = {Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.}, }
@article {pmid39316474, year = {2025}, author = {Li, X and Yang, Z and Tu, X and Wang, J and Huang, J}, title = {MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {1}, pages = {224-234}, doi = {10.1109/JBHI.2024.3467090}, pmid = {39316474}, issn = {2168-2208}, support = {//Major Program (JD) of Hubei Province/ ; //National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Convolutional Neural Networks ; }, abstract = {Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource demands of deep learning models. In this study, we propose a novel lightweight Multi-Scale Feature Residual Convolutional Neural Network (MFRC-Net). MFRC-Net primarily consists of two blocks: temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks. The former captures dynamic changes in EEG signals across various time scales through multi-scale grouped convolution and backbone temporal convolution skip connections; the latter improves local spatial feature extraction and calibrates feature mapping through the introduction of cross-domain spatial filtering layers. Furthermore, by specifically optimizing the loss function, MFRC-Net effectively reduces sensitivity to outliers. Experiment results on the BCI Competition IV 2a dataset and the SHU dataset demonstrate that, with a parameter size of only 13 K, MFRC-Net achieves accuracy of 85.1% and 69.3%, respectively, surpassing current state-of-the-art models. The integration of temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks in lightweight models significantly boosts performance, as evidenced by ablation studies and visualizations.}, }
@article {pmid39316274, year = {2024}, author = {Dinov, ID}, title = {Neuroinformatics Applications of Data Science and Artificial Intelligence.}, journal = {Neuroinformatics}, volume = {22}, number = {4}, pages = {403-405}, pmid = {39316274}, issn = {1559-0089}, mesh = {Humans ; *Artificial Intelligence/trends ; Brain/physiology/diagnostic imaging ; Brain-Computer Interfaces/trends ; *Data Science/methods/trends ; *Neurosciences/methods/trends ; }, abstract = {Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.}, }
@article {pmid39314938, year = {2024}, author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, F and Rieth, L and Andersen, RA}, title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2246 days.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39314938}, support = {U01 NS098975/NS/NINDS NIH HHS/United States ; UG3 NS107688/NS/NINDS NIH HHS/United States ; }, abstract = {MOTIVATION: The clinical success of brain-machine interfaces depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. Therefore, there is a need to quantify any damage that microelectrodes sustain when they are chronically implanted in the human cortex.<br><br>METHODS: Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from Neuroport arrays chronically implanted in the cortex of three people with tetraplegia for 956-2246 days. We analyzed eleven multi-electrode arrays in total: eight arrays with platinum (Pt) electrode tips and three with sputtered iridium oxide tips (SIROF); one Pt array was left in sterile packaging, serving as a control. The arrays were implanted/explanted across three different clinical sites surgeries (Caltech/UCLA, Caltech/USC and APL/Johns Hopkins) in the anterior intraparietal area, Brodmann's area 5, motor cortex, and somatosensory cortex.Human experts rated the electron micrographs of electrodes with respect to five damage metrics: the loss of metal at the electrode tip, the amount of separation between the silicon shank and tip metal, tissue adherence or bio-material to the electrode, damage to the shank insulation and silicone shaft. These metrics were compared to functional outcomes (recording quality, noise, impedance and stimulation ability).<br><br>RESULTS: Despite higher levels of physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt electrodes (measured by SNR), at the time of explant. Additionally, 1 kHz impedance (measured in vivo prior to explant) significantly correlated with all physical damage metrics, recording, and stimulation performance for SIROF electrodes (but not Pt), suggesting a reliable measurement of in vivo degradation.We observed a new degradation type, primarily occurring on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, tip metalization damage was not significantly higher due to stimulation or amount of charge. Physical damage was centralized to specific regions of an array often with differences between outer and inner electrodes. This is consistent with degradation due to contact with the biologic milieu, influenced by variations in initial manufactured state. From our data, we hypothesize that erosion of the silicon shank often precedes damage to the tip metal, accelerating damage to the electrode / tissue interface.<br><br>CONCLUSIONS: These findings link quantitative measurements, such as impedance, to the physical condition of the microelectrodes and their capacity to record and stimulate. These data could lead to improved manufacturing or novel electrode designs to improve long-term performance of BMIs making them are vitally important as multi-year clinical trials of BMIs are becoming more common.}, }
@article {pmid39314865, year = {2024}, author = {Luo, X}, title = {Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: a randomized controlled study.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1394424}, pmid = {39314865}, issn = {1664-2295}, abstract = {BACKGROUND: Lower limb motor dysfunction is one of the most serious consequences of stroke; however, there is insufficient evidence for optimal rehabilitation strategies. Improving lower limb motor function through effective rehabilitation strategies is a top priority for stroke patients. Neuroplasticity is a key factor in the recovery of motor function. The extent to which neuroplasticity-based rehabilitation therapy using brain-computer interface (BCI) is effective in treating lower limb motor dysfunction in acute ischemic stroke patients has not been extensively investigated.<br><br>OBJECTIVE: This study aimed to assess the impact of BCI rehabilitation on lower limb motor dysfunction in individuals with acute ischemic stroke by evaluating motor function, walking ability, and daily living activities.<br><br>METHODS: This study was conducted in a randomized controlled trial, involving 64 patients with acute ischemic stroke who experienced lower limb motor dysfunction. All patients were divided into two groups, with 32 patients assigned to the control group was given conventional rehabilitation once a day for 70&#x2009;min, 5 times a week for 2&#x2009;weeks, and the experimental group (n&#x2009;=&#x2009;32) was given BCI rehabilitation on top of the conventional rehabilitation for 1&#x2009;h a day, 30&#x2009;min of therapy in the morning and an additional 30&#x2009;min in the afternoon, for a total of 20 sessions over a two-week period. The primary outcome was lower extremity motor function, which was assessed using the lower extremity portion of the Fugl-Meyer Rating Scale (FMA-LE), and the secondary endpoints were the Functional Ambulation Scale (FAC), and the Modified Barthel index (MBI).<br><br>RESULTS: After 20 sessions of treatment, both groups improved in motor function, walking function, and activities of daily living, and the improvements in FMA-LE scores (p&#x2009;<&#x2009;0.001), FAC (p&#x2009;=&#x2009;0.031), and MBI (p&#x2009;<&#x2009;0.001) were more pronounced in the experimental group compared with the control group.<br><br>CONCLUSION: Conventional rehabilitation therapy combined with BCI rehabilitation therapy can improve the lower limb motor function of hemiplegic patients with stroke, enhance the patient's ability to perform activities of daily living, and promote the improvement of walking function, this is an effective rehabilitation policy to promote recovery from lower extremity motor function disorders.}, }
@article {pmid39314275, year = {2024}, author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE}, title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39314275}, issn = {2692-8205}, support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; }, abstract = {OBJECTIVE: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.<br><br>APPROACH: We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.<br><br>MAIN RESULTS: We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters.<br><br>SIGNIFICANCE: This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.}, }
@article {pmid39314155, year = {2025}, author = {Cai, Q and Meng, L and Quan, M and Wang, L and Ren, J and Zheng, C and Yang, J and Ming, D}, title = {Progress of research in the application of ultrasound technology for the treatment of Alzheimer's disease.}, journal = {Neural regeneration research}, volume = {20}, number = {10}, pages = {2823-2837}, pmid = {39314155}, issn = {1673-5374}, abstract = {Alzheimer's disease is a common neurodegenerative disorder defined by decreased reasoning abilities, memory loss, and cognitive deterioration. The presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer's disease. The use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent years. As a safe and feasible therapeutic and drug-delivery method, ultrasound has shown promise in improving cognitive deficits. This article provides a summary of the application of ultrasound technology for treating Alzheimer's disease over the past 5 years, including standalone ultrasound treatment, ultrasound combined with microbubbles or drug therapy, and magnetic resonance imaging-guided focused ultrasound therapy. Emphasis is placed on the benefits of introducing these treatment methods and their potential mechanisms. We found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-β plaque deposition. We believe that ultrasound is an effective therapy for Alzheimer's disease, and this review provides a theoretical basis for future ultrasound treatment methods.}, }
@article {pmid39314138, year = {2025}, author = {Lv, Y and Li, H}, title = {Blood diagnostic and prognostic biomarkers in amyotrophic lateral sclerosis.}, journal = {Neural regeneration research}, volume = {20}, number = {9}, pages = {2556-2570}, pmid = {39314138}, issn = {1673-5374}, abstract = {Amyotrophic lateral sclerosis is a devastating neurodegenerative disease for which the current treatment approaches remain severely limited. The principal pathological alterations of the disease include the selective degeneration of motor neurons in the brain, brainstem, and spinal cord, as well as abnormal protein deposition in the cytoplasm of neurons and glial cells. The biological markers under extensive scrutiny are predominantly located in the cerebrospinal fluid, blood, and even urine. Among these biomarkers, neurofilament proteins and glial fibrillary acidic protein most accurately reflect the pathologic changes in the central nervous system, while creatinine and creatine kinase mainly indicate pathological alterations in the peripheral nerves and muscles. Neurofilament light chain levels serve as an indicator of neuronal axonal injury that remain stable throughout disease progression and are a promising diagnostic and prognostic biomarker with high specificity and sensitivity. However, there are challenges in using neurofilament light chain to differentiate amyotrophic lateral sclerosis from other central nervous system diseases with axonal injury. Glial fibrillary acidic protein predominantly reflects the degree of neuronal demyelination and is linked to non-motor symptoms of amyotrophic lateral sclerosis such as cognitive impairment, oxygen saturation, and the glomerular filtration rate. TAR DNA-binding protein 43, a pathological protein associated with amyotrophic lateral sclerosis, is emerging as a promising biomarker, particularly with advancements in exosome-related research. Evidence is currently lacking for the value of creatinine and creatine kinase as diagnostic markers; however, they show potential in predicting disease prognosis. Despite the vigorous progress made in the identification of amyotrophic lateral sclerosis biomarkers in recent years, the quest for definitive diagnostic and prognostic biomarkers remains a formidable challenge. This review summarizes the latest research achievements concerning blood biomarkers in amyotrophic lateral sclerosis that can provide a more direct basis for the differential diagnosis and prognostic assessment of the disease beyond a reliance on clinical manifestations and electromyography findings.}, }
@article {pmid39313602, year = {2025}, author = {Yin, X and Yang, C and Dong, H and Liang, J and Lin, M}, title = {Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {63}, number = {2}, pages = {355-363}, pmid = {39313602}, issn = {1741-0444}, support = {2024A1515012810//Basic and Applied Basic Research Foundation of Guangdong Province/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Female ; Young Adult ; Time Factors ; }, abstract = {The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.}, }
@article {pmid39312420, year = {2024}, author = {Wang, J and Kim, SJ and Wu, W and Lee, J and Hinton, H and Gertner, RS and Jung, HS and Park, H and Ham, D}, title = {A Cyto-silicon Hybrid System with On-chip Closed-loop Modulation.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2024.3466549}, pmid = {39312420}, issn = {1940-9990}, abstract = {We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 μs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.}, }
@article {pmid39312418, year = {2024}, author = {Lee, G and Jang, J and Song, K and Kim, TW}, title = {A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2024.3465533}, pmid = {39312418}, issn = {1940-9990}, abstract = {This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.}, }
@article {pmid39310800, year = {2024}, author = {Vanderheiden, G and Marte, C and Bahram, S}, title = {Rethinking Our Approach to Accessibility in the Era of Rapidly Emerging Technologies.}, journal = {Human aspects of IT for the aged population : 10th International Conference, ITAP 2024, held as part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings. Part I. ITAP (Conference) ...}, volume = {14725}, number = {}, pages = {306-323}, pmid = {39310800}, support = {90REGE0008/ACL/ACL HHS/United States ; }, abstract = {Accessibility has always played catch-up to the detriment of people with disabilities - and this appears to be exacerbated by the rapid advancements in technology. A key question becomes, can we better predict where technology will be in 10 or 20 years and develop a plan to be better positioned to make these new technologies accessible when they make it to market? To attempt to address this question, a "Future of Interface Workshop" was convened in February 2023, chaired by Vinton Cerf and Gregg Vanderheiden that brought together leading researchers in artificial intelligence, brain-computer interfaces, computer vision, and VR/AR/XR, and disability to both a) identify barriers these new technologies might present and how to address them, and b) how these new technologies might be tapped to address current un- or under-addressed problems and populations. This paper provides an overview of the results of the workshop as well as the current version of the R&amp;D Agenda work that was initiated at the conference. It will also present an alternate approach to accessibility that is being proposed based on the new emerging technologies.}, }
@article {pmid39309467, year = {2024}, author = {Stacy, NI and Smith, R and Sullivan, KE and Nelson, SE and Nolan, EC and De Voe, RS and Witherington, BE and Perrault, JR}, title = {Health assessment of nesting loggerhead sea turtles (Caretta caretta) in one of their largest rookeries (central eastern Florida coast, USA).}, journal = {Conservation physiology}, volume = {12}, number = {1}, pages = {coae064}, pmid = {39309467}, issn = {2051-1434}, abstract = {Reproduction is a physiologically demanding process for sea turtles. Health indicators, including morphometric indices and blood analytes, provide insight into overall health, physiology and organ function for breeding sea turtles as a way to assess population-level effects. The Archie Carr National Wildlife Refuge (ACNWR) on Florida's central eastern coast is critical nesting habitat for loggerhead sea turtles (Caretta caretta), but health variables from this location have not been documented. Objectives of the study were to (1) assess morphometrics and blood analyte data (including haematology, plasma biochemistry, protein electrophoresis, β-hydroxybutyrate, trace nutrients, vitamins and fatty acid profiles) from loggerheads nesting on or near the beaches of the ACNWR, (2) investigate correlations of body condition index (BCI) with blood analytes and (3) analyse temporal trends in morphometric and blood analyte data throughout the nesting season. Morphometric and/or blood analyte data are reported for 57 nesting loggerheads encountered between 2016 and 2019. Plasma copper and iron positively correlated with BCI. Mass tended to decline across nesting season, whereas BCI did not. Many blood analytes significantly increased or decreased across nesting season, reflecting the catabolic state and haemodynamic variations of nesting turtles. Twenty-three of 34 fatty acids declined across nesting season, which demonstrates the physiological demands of nesting turtles for vitellogenesis and reproductive activities, thus suggesting potential utility of fatty acids for the assessment of foraging status and phases of reproduction. The findings herein are relevant for future spatiotemporal and interspecies comparisons, investigating stressor effects and understanding the physiological demands in nesting sea turtles. This information provides comparative data for individual animals in rescue or managed care settings and for assessment of conservation strategies.}, }
@article {pmid39308547, year = {2024}, author = {Villa, J and Cury, J and Kessler, L and Tan, X and Richter, CP}, title = {Enhancing biocompatibility of the brain-machine interface: A review.}, journal = {Bioactive materials}, volume = {42}, number = {}, pages = {531-549}, pmid = {39308547}, issn = {2452-199X}, support = {R01 DC018666/DC/NIDCD NIH HHS/United States ; }, abstract = {In vivo implantation of microelectrodes opens the door to studying neural circuits and restoring damaged neural pathways through direct electrical stimulation and recording. Although some neuroprostheses have achieved clinical success, electrode material properties, inflammatory response, and glial scar formation at the electrode-tissue interfaces affect performance and sustainability. Those challenges can be addressed by improving some of the materials' mechanical, physical, chemical, and electrical properties. This paper reviews materials and designs of current microelectrodes and discusses perspectives to advance neuroprosthetics performance.}, }
@article {pmid39305732, year = {2024}, author = {Jirakittayakorn, N and Wongsawat, Y and Mitrirattanakul, S}, title = {An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.}, journal = {Computers in biology and medicine}, volume = {182}, number = {}, pages = {109138}, doi = {10.1016/j.compbiomed.2024.109138}, pmid = {39305732}, issn = {1879-0534}, mesh = {Humans ; *Deep Learning ; *Polysomnography ; *Sleep Stages/physiology ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Electromyography/methods ; }, abstract = {Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by 'the-last-dense' layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.}, }
@article {pmid39297701, year = {2024}, author = {Wu, L and Liu, J and Geng, Y and Fang, J and Gao, X and Lai, J and Yao, M and Lu, S and Yin, W and Fu, P and Chen, W and Hu, S}, title = {Single-cell transcriptomic atlas reveals immune and metabolism perturbation of depression in the pathogenesis of breast cancer.}, journal = {Cancer communications (London, England)}, volume = {44}, number = {11}, pages = {1311-1315}, pmid = {39297701}, issn = {2523-3548}, support = {82172770//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; }, }
@article {pmid39303815, year = {2025}, author = {Xie, X and Li, W and Xiong, Z and Xu, J and Liao, T and Sun, L and Xu, H and Zhang, M and Zhou, J and Xiong, W and Fu, Z and Li, Z and Han, Q and Cui, D and Anthony, DC}, title = {Metformin reprograms tryptophan metabolism via gut microbiome-derived bile acid metabolites to ameliorate depression-Like behaviors in mice.}, journal = {Brain, behavior, and immunity}, volume = {123}, number = {}, pages = {442-455}, doi = {10.1016/j.bbi.2024.09.014}, pmid = {39303815}, issn = {1090-2139}, mesh = {Animals ; *Gastrointestinal Microbiome/drug effects ; *Metformin/pharmacology ; Mice ; *Bile Acids and Salts/metabolism ; *Depression/metabolism/drug therapy ; Male ; *Tryptophan/metabolism ; *Serotonin/metabolism ; *Brain/metabolism/drug effects ; Mice, Inbred C57BL ; Behavior, Animal/drug effects ; Stress, Psychological/metabolism ; Akkermansia/metabolism/drug effects ; Antidepressive Agents/pharmacology ; Disease Models, Animal ; Fecal Microbiota Transplantation/methods ; Dysbiosis/metabolism ; }, abstract = {As an adjunct therapy, metformin enhances the efficacy of conventional antidepressant medications. However, its mode of action remains unclear. Here, metformin was found to ameliorate depression-like behaviors in mice exposed to chronic restraint stress (CRS) by normalizing the dysbiotic gut microbiome. Fecal transplants from metformin-treated mice ameliorated depressive behaviors in stressed mice. Microbiome profiling revealed that Akkermansia muciniphila (A. muciniphila), in particular, was markedly increased in the gut by metformin and that oral administration of this species alone was sufficient to reverse CRS-induced depressive behaviors and normalize aberrant stress-induced 5-hydroxytryptamine (5-HT) metabolism in the brain and gut. Untargeted metabolomic profiling further identified the bile acid metabolites taurocholate and deoxycholic acid as direct A. muciniphila-derived molecules that are, individually, sufficient to rescue the CRS-induced impaired 5-HT metabolism and depression-like behaviors. Thus, we report metformin reprograms 5-HT metabolism via microbiome-brain interactions to mitigate depressive syndromes, providing novel insights into gut microbiota-derived bile acids as potential therapeutic candidates for depressive mood disorders from bench to bedside.}, }
@article {pmid39301184, year = {2024}, author = {Fried-Oken, M and Kinsella, M and Stevens, I and Klein, E}, title = {What stakeholders with neurodegenerative conditions value about speech and accuracy in development of BCI systems for communication.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {11}, number = {1-2}, pages = {21-32}, pmid = {39301184}, issn = {2326-263X}, support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; }, abstract = {This research examined values of individuals with neurodegenerative conditions about features of speed and accuracy as they consider potential use of augmentative and alternative communication brain-computer interface systems (AAC-BCI). Sixty-six individuals with neurodegenerative disease responded to prompts about six hypothetical ethical vignettes. Data were analyzed with qualitative content analysis. The following themes emerged. (1) Disease progression may contribute to the trade-off between speed and accuracy with AAC-BCI systems. (2) Individual experiences with technology use inform their views about the speed-accuracy trade-off. (3) There is a range of views about how slow or inaccurate communication may impact relationships, the integrity of a message, and quality of life. (4) Design solutions are proposed to address trade-offs in AAC-BCI systems. With the rapid development of AAC-BCI systems, user-centered design must integrate values of potential end-users illustrating that context, partner, message, and environment impact the prioritization of speed or accuracy in any communication exchange.}, }
@article {pmid39300485, year = {2024}, author = {Meng, L and Shi, Y and Zhao, H and Wang, D and Zhu, X and Ming, D}, title = {The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {166}, pmid = {39300485}, issn = {1743-0003}, support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; TJYXZDXK-060B//Tianjin Key Medical Discipline (Specialty) Construction Project/ ; 23JCYBJC00690//Tianjin Natural Science Foundation/ ; }, mesh = {Humans ; *Parkinson Disease/rehabilitation/diagnosis/physiopathology ; Male ; Female ; Middle Aged ; Aged ; *Gait/physiology ; Gait Disorders, Neurologic/rehabilitation/etiology/physiopathology/diagnosis ; }, abstract = {BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.<br><br>METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.<br><br>RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933).<br><br>CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.}, }
@article {pmid39296916, year = {2024}, author = {Van Damme, S and Mumford, L and Johnson, A and Chau, T}, title = {Case report: Novel use of clinical brain-computer interfaces in recreation programming for an autistic adolescent with co-occurring attention deficit hyperactivity disorder.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1434792}, pmid = {39296916}, issn = {1662-5161}, abstract = {BACKGROUND: In recent years, several autistic children and youth have shown interest in Holland Bloorview Kids Rehabilitation Hospital's clinical brain computer interface (BCI) program. Existing literature about BCI use among autistic individuals has focused solely on cognitive skill development and remediation of challenging behaviors. To date, the benefits of recreational BCI programming with autistic children and youth have not been documented.<br><br>PURPOSE: This case report summarizes the experiences of an autistic male adolescent with co-occurring attention deficit hyperactivity disorder using a BCI for recreation and considers possible benefits with this novel user population.<br><br>METHODS: A single retrospective chart review was completed with parental guardian's consent.<br><br>FINDINGS: The participant demonstrated enjoyment in BCI sessions and requested continued opportunities to engage in BCI programming. This enjoyment correlated with improved Canadian Occupational Performance Measure (COPM) scores in BCI programming, outperforming scores from other recreational programs. Additionally, clinicians observed changes in social communication efforts and self-advocacy in this first autistic participant.<br><br>CONCLUSION: The use of brain computer interfaces in recreational programming provides a novel opportunity for engagement for autistic children and youth that may also support skill development.}, }
@article {pmid39296025, year = {2024}, author = {Sung, DJ and Kim, KT and Jeong, JH and Kim, L and Lee, SJ and Kim, H and Kim, SJ}, title = {Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification.}, journal = {Heliyon}, volume = {10}, number = {17}, pages = {e37343}, pmid = {39296025}, issn = {2405-8440}, abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.}, }
@article {pmid39294338, year = {2024}, author = {Fan, L and Liu, J and Hu, W and Chen, Z and Lan, J and Zhang, T and Zhang, Y and Wu, X and Zhong, Z and Zhang, D and Zhang, J and Qin, R and Chen, H and Zong, Y and Zhang, J and Chen, B and Jiang, J and Cheng, J and Zhou, J and Gao, Z and Liu, Z and Chai, Y and Fan, J and Wu, P and Chen, Y and Zhu, Y and Wang, K and Yuan, Y and Huang, P and Zhang, Y and Feng, H and Song, K and Zeng, X and Zhu, W and Hu, X and Yin, W and Chen, W and Wang, J}, title = {Author Correction: Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.}, journal = {Cell research}, volume = {34}, number = {11}, pages = {815}, doi = {10.1038/s41422-024-01003-5}, pmid = {39294338}, issn = {1748-7838}, }
@article {pmid39294121, year = {2024}, author = {Song, H and Yang, P and Zhang, X and Tao, R and Zuo, L and Liu, W and Fu, J and Kong, Z and Tang, R and Wu, S and Pang, L and Zhang, X}, title = {Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {381}, pmid = {39294121}, issn = {2158-3188}, mesh = {Humans ; Male ; *Alcoholism/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging ; Adult ; *Frontal Lobe/physiopathology/diagnostic imaging ; Connectome ; Corpus Striatum/diagnostic imaging/physiopathology ; Case-Control Studies ; Middle Aged ; Neural Pathways/physiopathology/diagnostic imaging ; Young Adult ; }, abstract = {Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R[2] = 0.38; Michigan alcoholism screening test, MAST, R[2] = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R[2] = 0.17; MAST, R[2] = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.}, }
@article {pmid39293197, year = {2024}, author = {Zagaria, A and Ballesio, A}, title = {Insomnia symptoms as long-term predictors of anxiety symptoms in middle-aged and older adults from the English Longitudinal Study of Ageing (ELSA), and the role of systemic inflammation.}, journal = {Sleep medicine}, volume = {124}, number = {}, pages = {120-126}, doi = {10.1016/j.sleep.2024.09.020}, pmid = {39293197}, issn = {1878-5506}, mesh = {Humans ; *Sleep Initiation and Maintenance Disorders/epidemiology/complications/blood ; Female ; Male ; Middle Aged ; Longitudinal Studies ; *Inflammation/blood ; *Anxiety ; *C-Reactive Protein/analysis ; Aged ; England/epidemiology ; Aging/psychology/physiology ; Risk Factors ; }, abstract = {Insomnia, i.e., difficulties in sleep onset and sleep maintenance, may increase the risk of anxiety symptoms, although long-term follow-up studies are rarely reported. Here, we examined whether insomnia symptoms may predict anxiety symptoms in a 9-year follow-up, and whether inflammation may play a mediating role. Data from 1355 participants (63.44 ± 7.47 years, 55.1 % females) from the English Longitudinal Study of Ageing (ELSA) were analysed. Insomnia symptoms were assessed in 2012/13. High-sensitivity C-reactive protein (hs-CRP), a marker of systemic inflammation, was measured in 2016/17. Anxiety symptoms were assessed in 2020/21. After adjusting for confounders and baseline levels, structural equation modelling (SEM) revealed that insomnia symptoms significantly predicted anxiety symptoms (β = 0.357, p < .001) but not hs-CRP (β = -0.016, p = .634). Similarly, hs-CRP was not related to anxiety symptoms (β = -0.024, p = .453). The hs-CRP mediation hypothesis was therefore rejected (β = 0.0004; 95 % BCI -0.001 to 0.005), and multi-group SEM showed that sex did not moderate these paths. However, baseline diagnoses of anxiety disorders prospectively predicted higher hs-CRP (B = 0.083, p = .030). Results of the current study suggest that individuals with baseline anxiety disorders may be at higher risk of developing low-grade chronic inflammation. Several alternative psychophysiological mechanisms linking insomnia and anxiety symptoms should be explored, including autonomic and cortical pre-sleep arousal, cortisol reactivity, and pro-inflammatory cytokines. Finally, insomnia symptoms may be a treatment target to lower the risk of anxiety symptoms in elderly.}, }
@article {pmid39292591, year = {2025}, author = {Wang, H and Han, H and Gan, JQ and Wang, H}, title = {Lightweight Source-Free Domain Adaptation Based on Adaptive Euclidean Alignment for Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {2}, pages = {909-922}, doi = {10.1109/JBHI.2024.3463737}, pmid = {39292591}, issn = {2168-2208}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Adult ; }, abstract = {For privacy protection of subjects in electroencephalogram (EEG)-based brain-computer interfaces (BCIs), using source-free domain adaptation (SFDA) for cross-subject recognition has proven to be highly effective. However, updating and storing a model trained on source subjects for each new subject can be inconvenient. This paper extends Euclidean alignment (EA) to propose adaptive Euclidean alignment (AEA), which learns a projection matrix to align the distribution of the target subject with the source subjects, thus eliminating domain drift issues and improving model classification performance of subject-independent BCIs. Combining the proposed AEA with various existing SFDA methods, such as SHOT, GSFDA, and NRC, this paper presents three new methods: AEA-SHOT, AEA-GSFDA, and AEA-NRC. In our experimental studies, these AEA-based SFDA methods were applied to four well-known deep learning models (i.e., EEGNet, Shallow ConvNet, Deep ConvNet, and MSFBCNN) on two motor imagery (MI) datasets, one event-related potential (ERP) dataset and one steady-state visual evoked potentials (SSVEP) dataset. The advanced cross-subject EEG classification performance demonstrates the efficacy of our proposed methods. For example, AEA-SHOT achieved the best average accuracy of 81.4% on the PhysioNet dataset.}, }
@article {pmid39292590, year = {2025}, author = {Geirnaert, S and Yao, Y and Francart, T and Bertrand, A}, title = {Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {2}, pages = {970-983}, doi = {10.1109/JBHI.2024.3462991}, pmid = {39292590}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; Young Adult ; }, abstract = {Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant (non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group-wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.}, }
@article {pmid39290180, year = {2024}, author = {}, title = {Neuroprostheses move forward, in multiple languages.}, journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry}, volume = {30}, number = {5}, pages = {517}, doi = {10.1177/10738584241276369}, pmid = {39290180}, issn = {1089-4098}, mesh = {Humans ; *Language ; Brain-Computer Interfaces ; Neural Prostheses ; Brain/physiology ; }, }
@article {pmid39289533, year = {2024}, author = {Wexler, A and Feinsinger, A}, title = {Ethical challenges in translating brain-computer interfaces.}, journal = {Nature human behaviour}, volume = {8}, number = {10}, pages = {1831-1833}, pmid = {39289533}, issn = {2397-3374}, support = {RF1MH121373//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; }, }
@article {pmid39288794, year = {2025}, author = {Sawyer, A and Chetty, N and McMullen, DP and Dean, H and Eisler, J and Fried-Oken, M and Hochberg, LR and Gibbons, C and Waite, KE and Oxley, T and Fry, A and Weber, D and Putrino, D}, title = {Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad7bec}, pmid = {39288794}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Consensus ; Outcome Assessment, Health Care/methods ; }, abstract = {Objective: The 10th International brain computer interface (BCI) Society Meeting, 'Balancing Innovation and Translation', was held from the 6th to 9th of June 2023 in Brussels, Belgium. This report provides a summary of the workshop 'Building Consensus on Clinical Outcome Assessments (COAs) for BCI Devices'. This workshop was intended to give participants an overview of the current state of BCI, future opportunities, and how different countries and regions provide regulatory oversight to support the BCI community to develop safe and effective devices for patients.Approach: Five presentations and a panel discussion including representatives from regulators, industry, and clinical research stakeholders focused on how various stakeholders and the BCI community might best work together to ensure studies provide data that is useful for evaluating safety and effectiveness, including reaching consensus on COAs that represent clinically meaningful benefits and support regulatory and payor requirements. This report focuses on the regulatory and reimbursement requirements for medical devices and how to best measure safety and effectiveness and summarizes the presentations from five experts and the discussion between the panel and the audience.Main results: Consensus was reached on the following items specifically related to BCI: (i) the importance of and need for a new generation of COAs, (ii) the challenges facing the development of appropriate clinical outcome assessments, and (iii) that improvements in COAs should demonstrate obvious and clinically meaningful benefit(s). There was discussion on: (i) clinical trial design for BCIs and (ii) considerations for payor reimbursement and other funding.Significance: Whilst the importance of building community consensus on COAs was apparent, further collaboration will be required to reach consensus on which specific current and/or novel COAs could be used for the BCI field to evolve from research to market.}, }
@article {pmid39288164, year = {2024}, author = {Hamidi Shishavan, H and Roy, R and Golzari, K and Singla, A and Zalozhin, D and Lohan, D and Farooq, M and Dede, EM and Kim, I}, title = {Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features.}, journal = {PloS one}, volume = {19}, number = {9}, pages = {e0308506}, pmid = {39288164}, issn = {1932-6203}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Adult ; Male ; *Photic Stimulation ; Female ; Electroencephalography/methods ; Young Adult ; Automobile Driving ; Signal-To-Noise Ratio ; }, abstract = {Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.}, }
@article {pmid39286921, year = {2024}, author = {Cai, M and Hong, J}, title = {Joint multi-feature extraction and transfer learning in motor imagery brain computer interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2024.2404541}, pmid = {39286921}, issn = {1476-8259}, abstract = {Motor imagery brain computer interface (BCI) systems are considered one of the most crucial paradigms and have received extensive attention from researchers worldwide. However, the non-stationary from subject-to-subject transfer is a substantial challenge for robust BCI operations. To address this issue, this paper proposes a novel approach that integrates joint multi-feature extraction, specifically combining common spatial patterns (CSP) and wavelet packet transforms (WPT), along with transfer learning (TL) in motor imagery BCI systems. This approach leverages the time-frequency characteristics of WPT and the spatial characteristics of CSP while utilizing transfer learning to facilitate EEG identification for target subjects based on knowledge acquired from non-target subjects. Using dataset IVa from BCI Competition III, our proposed approach achieves an impressive average classification accuracy of 93.4%, outperforming five kinds of state-of-the-art approaches. Furthermore, it offers the advantage of enabling the design of various auxiliary problems to learn different aspects of the target problem from unlabeled data through transfer learning, thereby facilitating the implementation of innovative ideas within our proposed approach. Simultaneously, it demonstrates that integrating CSP and WPT while transferring knowledge from other subjects is highly effective in enhancing the average classification accuracy of EEG signals and it provides a novel solution to address subject-to-subject transfer challenges in motor imagery BCI systems.}, }
@article {pmid39286440, year = {2024}, author = {Kew, SYN and Mok, SY and Goh, CH}, title = {Machine learning and brain-computer interface approaches in prognosis and individualized care strategies for individuals with amyotrophic lateral sclerosis: A systematic review.}, journal = {MethodsX}, volume = {13}, number = {}, pages = {102765}, pmid = {39286440}, issn = {2215-0161}, abstract = {Amyotrophic lateral sclerosis (ALS) characterized by progressive degeneration of motor neurons is a debilitating disease, posing substantial challenges in both prognosis and daily life assistance. However, with the advancement of machine learning (ML) which is renowned for tackling many real-world settings, it can offer unprecedented opportunities in prognostic studies and facilitate individuals with ALS in motor-imagery tasks. ML models, such as random forests (RF), have emerged as the most common and effective algorithms for predicting disease progression and survival time in ALS. The findings revealed that RF models had an excellent predictive performance for ALS, with a testing R2 of 0.524 and minimal treatment effects of 0.0717 for patient survival time. Despite significant limitations in sample size, with a maximum of 18 participants, which may not adequately reflect the population diversity being studied, ML approaches have been effectively applied to ALS datasets, and numerous prognostic models have been tested using neuroimaging data, longitudinal datasets, and core clinical variables. In many literatures, the constraints of ML models are seldom explicitly enunciated. Therefore, the main objective of this research is to provide a review of the most significant studies on the usage of ML models for analyzing ALS. This review covers a variation of ML algorithms involved in applications in ALS prognosis besides, leveraging ML to improve the efficacy of brain-computer interfaces (BCIs) for ALS individuals in later stages with restricted voluntary muscular control. The key future advances in individualized care and ALS prognosis may include the advancement of more personalized care aids that enable real-time input and ongoing validation of ML in diverse healthcare contexts.}, }
@article {pmid39285529, year = {2024}, author = {Wang, N and Huang, J and Fang, Y and Du, H and Chen, Y and Zhao, S}, title = {Molecular biomarkers of blunt cardiac injury: recent advances and future perspectives.}, journal = {Expert review of molecular diagnostics}, volume = {24}, number = {11}, pages = {1023-1031}, doi = {10.1080/14737159.2024.2405919}, pmid = {39285529}, issn = {1744-8352}, mesh = {Humans ; *Biomarkers/analysis ; *Heart Injuries/diagnosis ; Natriuretic Peptide, Brain/blood ; Peptide Fragments/blood ; Troponin/blood ; Wounds, Nonpenetrating/diagnosis ; }, abstract = {INTRODUCTION: Blunt cardiac injury (BCI), associated with high morbidity and mortality, involves multiple injuries. With no widely accepted gold standard diagnostic test and molecular biomarkers still in debate and far from application in clinical practice, exploring specific molecular biomarkers of BCI is of great significance. The clarification of molecular biomarkers can improve the diagnosis of BCI, leading to more precise care for victims in various situations.<br><br>AREAS COVERED: Using the search term 'Biomarker AND Blunt cardiac injury,' we carefully reviewed related papers from June 2004 to June 2024 in PubMed and CNKI. After reviewing, we included 20 papers, summarizing the biomarkers reported in previous studies, and then reviewed molecular biomarkers such as troponins, Nterminal proBtype natriuretic peptide (NT proBNP), hearttype fatty acid binding protein (hFABP), and lactate for BCI diagnosis. Finally, valuable views on future research directions for diagnostic biomarkers of BCI were presented.<br><br>EXPERT OPINION: Several advanced technologies have been introduced into clinical medicine, which have ultimately changed the research on cardiac diseases in recent years. Some biomarkers have been identified and utilized for BCI diagnosis. Herein, we summarize the latest relevant information as a reference for clinical practice and future studies.}, }
@article {pmid39284710, year = {2024}, author = {Duran, F and Medina, MS and Ibargüengoytía, NR and Boretto, JM}, title = {Effects of blood extraction and ecophysiological experiments on stress in adult males of Liolaemus attenboroughi.}, journal = {Biology open}, volume = {13}, number = {10}, pages = {}, pmid = {39284710}, issn = {2046-6390}, support = {PICT2020-03395//Fondo para la Investigacion Cientifica y Tecnologica/ ; UNComahue 04/B234//Universidad Nacional del Comahue/ ; PICT2020-03395//Fondo para la Investigaci&#xf3;n Cient&#xed;fica y Tecnol&#xf3;gica/ ; //INIBIOMA: Instituto de Investigaciones en Biodiversidad y Medioambiente/ ; }, mesh = {Animals ; Male ; *Stress, Physiological ; *Lizards/physiology/blood/parasitology ; Corticosterone/blood ; }, abstract = {Stress during laboratory experiments can affect the outcomes of ecophysiological studies. The serum corticosterone concentration (CORT), the leukocyte profile, heterophil/lymphocyte ratio (H/L), and the presence of blood endoparasites were analyzed as a proxy of stress and immunological state in adult males of the lizard Liolaemus attenboroughi, endemic to Patagonia, Argentina. The results of the ecophysiological variables (preferred temperature, running speed, locomotor endurance, and body condition index, BCI) were analyzed in relation to stress indicators obtained from blood samples taken at three different times: at capture, and on the third and seventh days in the laboratory. Males at capture showed a high percentage of lymphocytes and heterophils and a low of basophils, monocytes, and eosinophils. Haemogregorina-type endoparasites have been recorded in the genus Liolaemus for the first time. The proportion of infected males remained stable during captivity; however, these males showed higher CORT levels, increased percentages of basophils, and decreased percentages of lymphocytes. There was a significant increment in CORT and H/L, and a decrease in BCI during laboratory experiments, compared with baseline values at capture. The performance was not related to the CORT or the repeated blood sampling. The BCI decreased, possibly due to energy reserve mobilization caused by acute stress. This study shows that blood extraction and ecophysiological experiments over 7 days have a minor effect on the stress indicators used.}, }
@article {pmid39283802, year = {2024}, author = {Wang, L and Li, M and Xu, D and Yang, Y}, title = {Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3636-3646}, doi = {10.1109/TNSRE.2024.3461339}, pmid = {39283802}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Algorithms ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Deep Learning ; Male ; Adult ; Female ; Cerebral Cortex/physiology ; Young Adult ; }, abstract = {Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance.}, }
@article {pmid39283065, year = {2024}, author = {Jin, L and Hu, J and Han, G and Li, Y and Zhu, J and Zhu, Y and He, X and Xu, D and Zheng, L and Bai, R and Wang, L}, title = {Glymphatic System Impairment in the Advanced Stage of Moyamoya Disease.}, journal = {Journal of neuroscience research}, volume = {102}, number = {9}, pages = {e25381}, doi = {10.1002/jnr.25381}, pmid = {39283065}, issn = {1097-4547}, support = {2022C03133//Key Research and Development Program of Zhejiang Province/ ; 81870910//National Natural Science Foundation of China/ ; 82171271//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Moyamoya Disease/diagnostic imaging/surgery/pathology/physiopathology ; Female ; Male ; *Glymphatic System/diagnostic imaging/pathology ; Adult ; Middle Aged ; *Diffusion Tensor Imaging ; *Cerebrovascular Circulation/physiology ; Young Adult ; Angiography, Digital Subtraction ; Brain/diagnostic imaging/pathology ; }, abstract = {Assessing the glymphatic system activity using diffusion tensor imaging analysis along with the perivascular space (DTI-ALPS) may be helpful to understand the pathophysiology of moyamoya disease (MMD). 63 adult patients with MMD and 20 healthy controls (HCs) were included for T1-weighted images, T2-FLAIR, pseudocontinuous arterial spin labeling, and DTI. 60 patients had digital subtraction angiography more than 6 months after combined revascularization. The Suzuki stage, postoperative Matsushima grade, periventricular anastomoses (PA), enlarged perivascular spaces (EPVS), deep and subcortical white matter hyperintensities (DSWMH), DTI-ALPS, cerebral blood flow (CBF), and cognitive scales of MMD patients were assessed. MMD patients were divided into early and advanced stage based on the Suzuki stage. We detected lower DTI-ALPS in patients with advanced stage relative to HCs (p = 0.046) and patients with early stage (p = 0.004), hemorrhagic MMD compared with ischemic MMD (p = 0.048), and PA Grade 2 compared with Grade 0 (p = 0.010). DTI-ALPS was correlated with the EPVS in basal ganglia (r = -0.686, p < 0.001), Suzuki stage (r = -0.465, p < 0.001), DSWMH (r = -0.423, p = 0.001), and global CBF (r = 0.300, p = 0.017) and cognitive scores (r = 0.343, p = 0.018). The DTI-ALPS of patients with good postoperative collateral formation was higher compared to those with poor postoperative collateral formation (p = 0.038). In conclusion, the glymphatic system was impaired in advanced MMD patients and may affected cognitive function and postoperative neoangiogenesis.}, }
@article {pmid39279838, year = {2024}, author = {Vermani, A and Dowling, M and Jeon, H and Jordan, I and Nassar, J and Bernaerts, Y and Zhao, Y and Vaerenbergh, SV and Park, IM}, title = {Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {39279838}, issn = {2331-8422}, support = {RF1 DA056404/DA/NIDA NIH HHS/United States ; }, abstract = {Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders.}, }
@article {pmid39279046, year = {2024}, author = {Chen, Y and Tong, S and Xu, Y and Xu, Y and Wu, Z and Zhu, X and Wang, X and Li, C and Lin, C and Li, X and Zhang, C and Wang, Y and Shao, X and Fang, J and Wu, Y}, title = {Involvement of basolateral amygdala-rostral anterior cingulate cortex in mechanical allodynia and anxiety-like behaviors and potential mechanisms of electroacupuncture.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {9}, pages = {e70035}, pmid = {39279046}, issn = {1755-5949}, support = {LQ24H270003//Natural Science Foundation of Zhejiang Province/ ; LY19H270007//Natural Science Foundation of Zhejiang Province/ ; LY23H270009//Natural Science Foundation of Zhejiang Province/ ; 82074541//National Natural Science Foundation of China/ ; 202210344020//College Students' Innovative Entrepreneurial Training Plan Program/ ; 2021YKJ08//Zhejiang Chinese Medical University/ ; 2021YKJ09//Zhejiang Chinese Medical University/ ; }, mesh = {Animals ; *Electroacupuncture/methods ; *Gyrus Cinguli ; *Hyperalgesia/therapy ; *Anxiety/therapy/psychology ; Male ; Mice ; *Basolateral Nuclear Complex/metabolism ; Mice, Inbred C57BL ; Neuralgia/therapy/psychology ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Neural Pathways ; }, abstract = {AIMS: Chronic pain is highly associated with anxiety. Electroacupuncture (EA) is effective in relieving pain and anxiety. Currently, little is known about the neural mechanisms underlying the comorbidity of chronic pain and anxiety and the EA mechanism. This study investigated a potential neural circuit underlying the comorbid and EA mechanisms.<br><br>METHODS: Spared nerve injury (SNI) surgery established the chronic neuropathic pain mouse model. The neural circuit was activated or inhibited using the chemogenetic method to explore the relationship between the neural circuit and mechanical allodynia and anxiety-like behaviors. EA combined with the chemogenetic method was used to explore whether the effects of EA were related to this neural circuit.<br><br>RESULTS: EA attenuated mechanical allodynia and anxiety-like behaviors in SNI mice, which may be associated with the activity of CaMKII neurons in the basolateral amygdala (BLA). Inhibition of BLA[CaMKII]-rACC induced mechanical allodynia and anxiety-like behaviors in sham mice. Activation of the BLA[CaMKII]-rACC alleviated neuropathic pain and anxiety-like behaviors in SNI mice. The analgesic and anxiolytic effects of 2&#x2009;Hz EA were antagonized by the inhibition of the BLA[CaMKII]-rACC.<br><br>CONCLUSION: BLA[CaMKII]-rACC mediates mechanical allodynia and anxiety-like behaviors. The analgesic and anxiolytic effects of 2&#x2009;Hz EA may be associated with the BLA[CaMKII]-rACC.}, }
@article {pmid39277877, year = {2024}, author = {Chen, H}, title = {Translational Bioethics in China: Brain-Computer Interface Research as a Case Study.}, journal = {Ethics &amp; human research}, volume = {46}, number = {5}, pages = {37-42}, doi = {10.1002/eahr.500224}, pmid = {39277877}, issn = {2578-2363}, mesh = {China ; Humans ; *Brain-Computer Interfaces/ethics ; *Translational Research, Biomedical/ethics ; *Bioethics ; Ethics, Research ; }, abstract = {The research and development of emerging technologies has potential long-term and societal impacts that pose governance challenges. This essay summarizes the development of research ethics in China over the past few decades, as well as the measures taken by the Chinese government to build its ethical governance system of science and technology after the occurrence of the CRISPR-babies incident. The essay then elaborates on the current problems of this system through the case study of ethical governance of brain-computer interface research, and explores how the transition from research ethics to translational bioethics, which encourages interdisciplinary collaboration and focuses on societal implications, may respond to the challenges of ethical governance of science and technology.}, }
@article {pmid39275712, year = {2024}, author = {Mohamed, AK and Aswat, M and Aharonson, V}, title = {Low-Cost Dynamometer for Measuring and Regulating Wrist Extension and Flexion Motor Tasks in Electroencephalography Experiments.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {17}, pages = {}, pmid = {39275712}, issn = {1424-8220}, support = {001-404 - 8241101- 5121105//University of the Witwatersrand/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Wrist/physiology ; Male ; Adult ; Female ; Movement/physiology ; Brain-Computer Interfaces ; Young Adult ; Muscle Strength Dynamometer ; Range of Motion, Articular/physiology ; }, abstract = {A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.}, }
@article {pmid39275636, year = {2024}, author = {Drigas, A and Sideraki, A}, title = {Brain Neuroplasticity Leveraging Virtual Reality and Brain-Computer Interface Technologies.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {17}, pages = {}, pmid = {39275636}, issn = {1424-8220}, mesh = {Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; *Neuronal Plasticity/physiology ; *Virtual Reality ; }, abstract = {This study explores neuroplasticity through the use of virtual reality (VR) and brain-computer interfaces (BCIs). Neuroplasticity is the brain's ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement.}, }
@article {pmid39274138, year = {2024}, author = {Chang, H and Sun, Y and Lu, S and Lan, Y}, title = {Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.}, journal = {Polymers}, volume = {16}, number = {17}, pages = {}, pmid = {39274138}, issn = {2073-4360}, abstract = {Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.}, }
@article {pmid39273744, year = {2024}, author = {Banyai, AD and Brișan, C}, title = {Robotics in Physical Rehabilitation: Systematic Review.}, journal = {Healthcare (Basel, Switzerland)}, volume = {12}, number = {17}, pages = {}, pmid = {39273744}, issn = {2227-9032}, abstract = {As the global prevalence of motor disabilities continues to rise, there is a pressing need for advanced solutions in physical rehabilitation. This systematic review examines the progress and challenges of implementing robotic technologies in the motor rehabilitation of patients with physical disabilities. The integration of robotic technologies such as exoskeletons, assistive training devices, and brain-computer interface systems holds significant promise for enhancing functional recovery and patient autonomy. The review synthesizes findings from the most important studies, focusing on the clinical effectiveness of robotic interventions in comparison to traditional rehabilitation methods. The analysis reveals that robotic therapies can significantly improve motor function, strength, co-ordination, and dexterity. Robotic systems also support neuroplasticity, enabling patients to relearn lost motor skills through precise, controlled, and repetitive exercises. However, the adoption of these technologies is hindered by high costs, the need for specialized training, and limited accessibility. Key insights from the review highlight the necessity of personalizing robotic therapies to meet individual patient needs, alongside addressing technical, economic, social, and cultural barriers. The review also underscores the importance of continued research to optimize these technologies and develop effective implementation strategies. By overcoming these challenges, robotic technologies can revolutionize motor rehabilitation, improving quality of life and social integration for individuals with motor disabilities.}, }
@article {pmid39273223, year = {2024}, author = {Cho, C and Lee, S}, title = {The Effects of Blood Flow Restriction Aerobic Exercise on Body Composition, Muscle Strength, Blood Biomarkers, and Cardiovascular Function: A Narrative Review.}, journal = {International journal of molecular sciences}, volume = {25}, number = {17}, pages = {}, pmid = {39273223}, issn = {1422-0067}, support = {NRF-2022R1A2C1003657//National Research Foundation of Korea (NRF)/ ; Research Grant in 2021//Incheon National University/ ; }, mesh = {Humans ; *Body Composition ; *Exercise/physiology ; *Biomarkers/blood ; *Muscle Strength/physiology ; Regional Blood Flow ; Resistance Training/methods ; }, abstract = {Blood flow restriction exercise has emerged as a promising alternative, particularly for elderly individuals and those unable to participate in high-intensity exercise. However, existing research has predominantly focused on blood flow restriction resistance exercise. There remains a notable gap in understanding the comprehensive effects of blood flow restriction aerobic exercise (BFRAE) on body composition, lipid profiles, glycemic metabolism, and cardiovascular function. This review aims to explore the physiological effects induced by chronic BFRAE. Chronic BFRAE has been shown to decrease fat mass, increase muscle mass, and enhance muscular strength, potentially benefiting lipid profiles, glycemic metabolism, and overall function. Thus, the BFRAE offers additional benefits beyond traditional aerobic exercise effects. Notably, the BFRAE approach may be particularly suitable for individuals with low fitness levels, those prone to injury, the elderly, obese individuals, and those with metabolic disorders.}, }
@article {pmid39271023, year = {2024}, author = {Zhang, Z and Li, M and Wei, R and Liao, W and Wang, F and Xu, G}, title = {Research on shared control of robots based on hybrid brain-computer interface.}, journal = {Journal of neuroscience methods}, volume = {412}, number = {}, pages = {110280}, doi = {10.1016/j.jneumeth.2024.110280}, pmid = {39271023}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; *Electroencephalography/methods ; Male ; *Electromyography/methods ; Young Adult ; Brain/physiology ; Adult ; Female ; }, abstract = {BACKGROUND: With the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain-computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control.<br><br>NEW METHODS: This paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion.<br><br>RESULTS: Eight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50&#x202f;%, improves the success rate of traversing obstacles by 25.00&#x202f;%, and the navigation trajectory is more consistent with expectations.<br><br>CONCLUSIONS: The HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.}, }
@article {pmid39270764, year = {2024}, author = {Chang, W and Zhao, X and Wang, L and Zhou, X}, title = {Causal role of frontocentral beta oscillation in comprehending linguistic communicative functions.}, journal = {NeuroImage}, volume = {300}, number = {}, pages = {120853}, doi = {10.1016/j.neuroimage.2024.120853}, pmid = {39270764}, issn = {1095-9572}, mesh = {Humans ; Female ; Male ; *Beta Rhythm/physiology ; Young Adult ; Adult ; *Comprehension/physiology ; Communication ; Electroencephalography ; Frontal Lobe/physiology ; Linguistics ; }, abstract = {Linguistic communication is often considered as an action serving the function of conveying the speaker's goal to the addressee. Although neuroimaging studies have suggested a role of the motor system in comprehending communicative functions, the underlying mechanism is yet to be specified. Here, by two EEG experiments and a tACS experiment, we demonstrate that the frontocentral beta oscillation, which represents action states, plays a crucial part in linguistic communication understanding. Participants read scripts involving two interlocutors and rated the interlocutors' attitudes. Each script included a critical sentence said by the speaker expressing a context-dependent function of either promise, request, or reply to the addressee's query. These functions were behaviorally discriminated, with higher addressee's will rating for the promise than for the reply and higher speaker's will rating for the request than for the reply. EEG multivariate analyses showed that different communicative functions were represented by different patterns of the frontocentral beta activity but not by patterns of alpha activity. Further tACS results showed that, relative to alpha tACS and sham stimulation, beta tACS improved the predictability of communicative functions of request or reply, as measured by the speaker's will rating. These results convergently suggest a causal role of the frontocentral beta activities in comprehending linguistic communications.}, }
@article {pmid39269692, year = {2024}, author = {Wang, L and Wang, J and Su, H and Zhang, X and Zhang, L and Kang, X}, title = {A zero precision loss framework for EEG channel selection: enhancing efficiency and maintaining interpretability.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-16}, doi = {10.1080/10255842.2024.2401918}, pmid = {39269692}, issn = {1476-8259}, abstract = {The brain-computer interface (BCI) systems based on motor imagery typically rely on a large number of electrode channels to acquire information. The rational selection of electroencephalography (EEG) channel combinations is crucial for optimizing computational efficiency and enhancing practical applicability. However, evaluating all potential channel combinations individually is impractical. This study aims to explore a strategy for quickly achieving a balance between maximizing channel reduction and minimizing precision loss. To this end, we developed a spatio-temporal attention perception network named STAPNet. Based on the channel contributions adaptively generated by its subnetwork, we propose an extended step bi-directional search strategy that includes variable ratio channel selection (VRCS) and strided greedy channel selection (SGCS), designed to enhance global search capabilities and accelerate the optimization process. Experimental results show that on the High Gamma and BCI Competition IV 2a public datasets, the framework respectively achieved average maximum accuracies of 91.47% and 84.17%. Under conditions of zero precision loss, the average number of channels was reduced by a maximum of 87.5%. Additionally, to investigate the impact of neural information loss due to channel reduction on the interpretation of complex brain functions, we employed a heatmap visualization algorithm to verify the universal importance and complete symmetry of the selected optimal channel combination across multiple datasets. This is consistent with the brain's cooperative mechanism when processing tasks involving both the left and right hands.}, }
@article {pmid39268765, year = {2024}, author = {Barco-Castillo, C and Sotelo Perilla, M and Rangel Amaya, J and Castaño, JC}, title = {A new nomogram for the evaluation of detrusor underactivity and bladder outlet obstruction in nonneurogenic female patients with lower urinary tract symptoms who undergo urodynamic studies.}, journal = {Neurourology and urodynamics}, volume = {43}, number = {8}, pages = {2076-2083}, doi = {10.1002/nau.25553}, pmid = {39268765}, issn = {1520-6777}, mesh = {Humans ; Female ; *Urinary Bladder Neck Obstruction/physiopathology/diagnosis ; *Nomograms ; *Urodynamics ; *Lower Urinary Tract Symptoms/physiopathology/diagnosis ; Middle Aged ; *Urinary Bladder, Underactive/physiopathology/diagnosis ; Adult ; Aged ; Urinary Bladder/physiopathology ; }, abstract = {INTRODUCTION: Micturition physiology differs in men and women. However, the results in standard urodynamic studies in women with lower urinary tract symptoms (LUTS) were extrapolated from studies in men. Nowadays, the only validated nomogram for females is Solomon-Greenwell's. However, it only evaluated bladder outlet obstruction (BOO) without considering detrusor underactivity (DU). This study aims to create a nomogram that includes an evaluation of DU and BOO in nonneurogenic women and validate it against videourodynamic studies along with other nomograms.<br><br>MATERIALS AND METHODS: For the first analysis (creation cohort), we included 183 women with LUTS who underwent videourodynamic study between 2022 and 2023. Exclusion criteria were females with neurologic diseases, renal transplantation, and trouble performing the flow-pressure study. Baseline characteristics of the patients, urodynamic parameters, and classifications on different nomograms and indexes were evaluated. A logistic regression found Qmax and PdetQmax as predictors for DU and BOO. The Barco-Castillo nomogram was created by clustering analysis and adjusted by the results of the logistic regression. A second (test) cohort was evaluated from 2023 to 2024, including 142 patients for the validation of the nomogram. A p&#x2009;<&#x2009;0.05 was considered significant.<br><br>RESULTS: All urodynamic parameters were compared between both cohorts, with no significant differences. The median age of the creation cohort was 50 years old (interquartile range [IQR] 39-63). All patients had LUTS and a previous standard urodynamic study without a clear diagnosis. The cluster analysis had a p&#x2009;<&#x2009;0.05 for two groups of BOO (yes/no) and two of DU (yes/no). We created the graph based on the logistic regression results and adjusted it according to the data. The median age of the test cohort was 44 years old (IQR 33.75-59) and had the same indication for the videourodynamic study. The receiver operating characteristic&#xa0;(ROC) curve for BOO showed an accuracy of 85.4% for Barco-Castillo nomogram, 68.5% for Blaivas-Groutz, 58.1% for Solomon-Greenwell, 57.1% for BOOI, and 50% for LinPURR. For DU, accuracy was 80.5% for PIP-1, 80.2% for Barco-Castillo, 76.6% for BCI, and 70.1% for LinPURR.<br><br>CONCLUSIONS: When evaluating women's urodynamic studies, it is important to focus on female physiology and discourage the use of parameters previously standardized in men. We encourage using the new Barco-Castillo nomogram to determine BOO and DU in women as the currently easiest and more accurate tool.}, }
@article {pmid39268310, year = {2024}, author = {Bakrbaldawi, AAA and Al-Sheikh, U and Jiang, H and Zhu, J}, title = {Multiple Glioblastomas Ablation by Laser Interstitial Thermal Therapy (LITT): A Rare Case.}, journal = {Cureus}, volume = {16}, number = {8}, pages = {e66726}, pmid = {39268310}, issn = {2168-8184}, abstract = {Multiple glioblastomas (GBMs) are aggressive, malignant, and sporadic brain tumors. We present the case of a 58-year-old patient with two GBMs in the right frontal lobe and associated edema. The patient presented with sudden left limb weakness accompanied by abnormal gait for five consecutive days. Magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT), a minimally invasive technique that disperses thermal energy was used to cauterize the deep-seated brain lesions. Following two sessions of MRg-LITT, the patient showed full remission from symptoms. However, the disruption of the blood-brain barrier (BBB) induced vasogenic edema surrounding the necrotic GBMs. Post-operative nine-month MRI images revealed severe vasogenic edema and compression on the ventricles, shifting the midline toward the left side. Therefore the patient underwent an emergency craniectomy and continues to live with close follow-ups. Here, we established that LITT procedures were effective in cauterizing GBMs with no recurrence.}, }
@article {pmid39266806, year = {2024}, author = {Pragya,  and Bisht, S and Parashar, P}, title = {Nanotechnology-driven Microemulsion Based Intranasal Delivery to Neurotechnology-driven Neuralink: Strategies to Improve Management of Neurodegenerative Disorders.}, journal = {AAPS PharmSciTech}, volume = {25}, number = {7}, pages = {215}, pmid = {39266806}, issn = {1530-9932}, mesh = {*Administration, Intranasal/methods ; Humans ; *Neurodegenerative Diseases/drug therapy/metabolism ; *Emulsions ; *Drug Delivery Systems/methods ; *Nanotechnology/methods ; Animals ; Blood-Brain Barrier/metabolism ; Nanoparticles/chemistry/administration &amp; dosage ; }, abstract = {Neurodegenerative disorder refers to malfunctioning of neurons their degradation leading to death of neurons. Among various neurodegenerative disorders APHD (Alzheimer's, Parkinson's, and Huntington's Disease) are particularly concerning due to their progressive and debilitating nature. The therapeutic agent used for treatment and management of APHD often show unsatisfactory clinical outcome owing to poor solubility and limited permeability across blood brain barrier (BBB). The nose-to brain delivery can overcome this BBB challenge as it can transport drug directly to brain though olfactory pathways bypassing BBB. Additionally, the nanotechnology has emerged as a cutting-edge methodology to address this issue and specifically mucoadhesive micro/nanoemulsion can improve the overall performance of the drug when administered intranasally. Beyond the therapy neurotechnology has emerged as are revolutionary AI-driven BCI (Brain computer interface) aimed to restore independence in patients with function loss due to neuron degeneration/death. A promising BCI Neuralink has been recently explored for clinical trials and results revealed that a quadriplegia bearing person with implanted Neuralink chip was able to perform few normal functions of daily routine such as playing online games, text messaging, reading, and learning foreign languages online through accessing the particular websites. This review will discuss the fundamental concepts of neurodegeneration, application of micro/nanoemulsion through intranasal route and integration of neurotechnology for the management and treatment of APHD.}, }
@article {pmid39265614, year = {2024}, author = {Kapralov, N and Jamshidi Idaji, M and Stephani, T and Studenova, A and Vidaurre, C and Ros, T and Villringer, A and Nikulin, V}, title = {Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad7a24}, pmid = {39265614}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Signal-To-Noise Ratio ; Male ; Female ; Longitudinal Studies ; *Electroencephalography/methods ; Adult ; Sensorimotor Cortex/physiology ; Brain Waves/physiology ; Young Adult ; Reproducibility of Results ; }, abstract = {Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.}, }
@article {pmid39265503, year = {2025}, author = {King, BJ and Read, GJM and Salmon, PM}, title = {Prospectively identifying risks and controls for advanced brain-computer interfaces: A Networked Hazard Analysis and Risk Management System (Net-HARMS) approach.}, journal = {Applied ergonomics}, volume = {122}, number = {}, pages = {104382}, doi = {10.1016/j.apergo.2024.104382}, pmid = {39265503}, issn = {1872-9126}, mesh = {Humans ; *Brain-Computer Interfaces ; Risk Assessment/methods ; *Risk Management/methods ; Prospective Studies ; }, abstract = {The introduction of advanced digital technologies continues to increase system complexity and introduce risks, which must be proactively identified and managed to support system resilience. Brain-computer interfaces (BCIs) are one such technology; however, the risks arising from broad societal use of the technology have yet to be identified and controlled. This study applied a structured systems thinking-based risk assessment method to prospectively identify risks and risk controls for a hypothetical future BCI system lifecycle. The application of the Networked Hazard Analysis and Risk Management System (Net-HARMS) method identified over 800 risks throughout the BCI system lifecycle, from BCI development and regulation through to BCI use, maintenance, and decommissioning. High-criticality risk themes include the implantation and degradation of unsafe BCIs, unsolicited brain stimulation, incorrect signals being sent to safety-critical technologies, and insufficiently supported BCI users. Over 600 risk controls were identified that could be implemented to support system safety and performance resilience. Overall, many highly-impactful BCI system safety and performance risks may arise throughout the BCI system lifecycle and will require collaborative efforts from a wide range of BCI stakeholders to adequately control. Whilst some of the identified controls are practical, work is required to develop a more systematic set of controls to best support the design of a resilient sociotechnical BCI system.}, }
@article {pmid39265209, year = {2024}, author = {Walsh, BE and Schlauch, RC}, title = {Differential impact of emotional and social loneliness on daily alcohol consumption in individuals with alcohol use disorder.}, journal = {Drug and alcohol dependence}, volume = {264}, number = {}, pages = {112433}, doi = {10.1016/j.drugalcdep.2024.112433}, pmid = {39265209}, issn = {1879-0046}, mesh = {Humans ; *Loneliness/psychology ; Male ; Female ; *Alcohol Drinking/psychology/epidemiology ; *Alcoholism/psychology ; Adult ; Middle Aged ; Emotions ; Prospective Studies ; }, abstract = {BACKGROUND: Loneliness is a predisposing and maintaining factor of alcohol use behavior. Several studies have linked loneliness to daily drinking and elevated alcohol use disorder (AUD) risk; however, operationalizations of both loneliness and drinking have varied greatly.<br><br>METHODS: The current study adopted a multidimensional framework of loneliness (i.e., emotional and social subtypes) to examine daily prospective relations between loneliness and drinking among non-treatment seeking individuals with AUD. Participants (N= 60) reported on current loneliness and drinking twice daily for 14-days. Scores on emotional and social loneliness were disaggregated into within- and between-person predictors, and a multilevel hurdle model proxy was fitted with drinking likelihood (logistic) and quantity (zero truncated negative binomial) specified as separate outcomes.<br><br>RESULTS: Emotional loneliness (within-person) was associated with increased drinking likelihood (OR=1.05, 95&#x2009;% BCI [1.01, 1.10]) and quantity (IRR=1.05, 95&#x2009;% BCI [1.02, 1.09]), while social loneliness (within-person) was associated with decreases in both drinking likelihood (OR=.94, 95&#x2009;% BCI [.89,.99]) and quantity (IRR=.96, 95&#x2009;% BCI [.93,.99]). Between-person loneliness scores were unrelated to both outcomes.<br><br>CONCLUSIONS: These discrepant findings by loneliness subtype may be ascribed to differences in subjective manifestations, in that emotional loneliness is a more severe form of loneliness that overlaps significantly with other negative affective states and promotes a coping response, while social loneliness may be readily alleviated by adaptive behavioral strategies for some, and social withdrawal for others. These findings offer insight into the nuances of loneliness-drinking relations and their clinical implications.}, }
@article {pmid39264785, year = {2024}, author = {Cho, H and Benjaber, M and Alexis Gkogkidis, C and Buchheit, M and Ruiz-Rodriguez, JF and Grannan, BL and Weaver, KE and Ko, AL and Cramer, SC and Ojemann, JG and Denison, T and Herron, JA}, title = {Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3625-3635}, pmid = {39264785}, issn = {1558-0210}, support = {K12 NS129164/NS/NINDS NIH HHS/United States ; U24 NS113637/NS/NINDS NIH HHS/United States ; UH3 NS121565/NS/NINDS NIH HHS/United States ; MC_UU_0003/3//Medical Research Council UK/ ; }, mesh = {Humans ; *Algorithms ; *Software ; Computer Systems ; Brain/physiology ; Male ; Adult ; Equipment Design ; Female ; Electrodes, Implanted ; Brain-Computer Interfaces ; Electroencephalography ; }, abstract = {With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.}, }
@article {pmid39264571, year = {2025}, author = {Guo, X and Chen, L and Lai, J and Hu, S}, title = {Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder.}, journal = {Neuroscience bulletin}, volume = {41}, number = {1}, pages = {181-185}, pmid = {39264571}, issn = {1995-8218}, }
@article {pmid39264055, year = {2024}, author = {Ma, L and Li, Y and Xue, J and Xu, L and Li, X and Chang, X}, title = {Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.}, journal = {Inquiry : a journal of medical care organization, provision and financing}, volume = {61}, number = {}, pages = {469580241275324}, pmid = {39264055}, issn = {1945-7243}, mesh = {*Medicine, Chinese Traditional/standards ; Humans ; *Tertiary Care Centers ; *Quality Indicators, Health Care ; China ; Quality of Health Care ; }, abstract = {Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.}, }
@article {pmid39263239, year = {2024}, author = {Roman-Gonzalez, A}, title = {Editorial: Multimodal perceiving technologies in neuroscience and vision applications.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1477572}, doi = {10.3389/fnins.2024.1477572}, pmid = {39263239}, issn = {1662-4548}, }
@article {pmid39262538, year = {2024}, author = {Yukawa, Y and Higashi, T and Minakuchi, M and Naito, E and Murata, T}, title = {Vibration-Induced Illusory Movement Task Can Induce Functional Recovery in Patients With Subacute Stroke.}, journal = {Cureus}, volume = {16}, number = {8}, pages = {e66667}, pmid = {39262538}, issn = {2168-8184}, abstract = {In recent years, mental practice (MP), which involves repetitive motor imagery (MI), has been applied in rehabilitation to actively enhance exercise performance. MP is a method that involves repetitive MI, consciously evoking the intentions and content of the exercise without actual exercise. Combining actual exercise with MP promotes the development of exercise skills. However, it is possible that the MI recall ability differs greatly between individuals, affecting the therapeutic effect. In contrast, the vibration-induced illusory movement (VIM) task acts as a method to induce a motor illusion by somatosensory stimuli without actual motor. VIM, actual movement, and MI are thought to share a common neural basis in the brain. Therefore, it was hypothesized that the VIM task would complement the differences in MI recall in individual patients with hemiplegic stroke and may be a new treatment to enhance MI recall. Accordingly, in this study, we investigated the therapeutic effects of the VIM task in patients with hemiplegic stroke. In Study I, the therapeutic effect of the VIM task in 14 patients with post-stroke hemiplegia was evaluated by motor function assessment. In Study II, treatment effects were investigated by examining the ability of the same group of patients to recall MI and by neurophysiological examination of the electroencephalogram (EEG) during MI recall in four patients who consented to the study. Motor function and MI were assessed four times: before the intervention, after occupational therapy, after the VIM task (which used the motor illusion induced by tendon vibration), and one month after acceptance of therapy. Compared with occupational therapy, the VIM task showed a statistically significant improvement in upper limb function and MI ability. In addition, we found an increase in event-related desynchronization intensity during MI in the affected hemisphere only after the VIM task. It is possible that the VIM task facilitates motor function and MI. VIM task implementation of MI recall variability between individuals, which is a problem in mental practice, possible to increase the effectiveness of the brain-machine interface.}, }
@article {pmid39261006, year = {2024}, author = {Chen, C and Song, S}, title = {Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {43}, pages = {}, pmid = {39261006}, issn = {1529-2401}, mesh = {Animals ; Mice ; Female ; Male ; *Inferior Colliculi/physiology/cytology ; *Neurons/physiology ; *Sound Localization/physiology ; Acoustic Stimulation/methods ; Mice, Inbred C57BL ; Auditory Perception/physiology ; Models, Neurological ; }, abstract = {Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.}, }
@article {pmid39260393, year = {2024}, author = {Johnston, R and Boulay, C and Miller, K and Sachs, A}, title = {Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {6}, pages = {}, doi = {10.1088/2057-1976/ad795e}, pmid = {39260393}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; *Cognition ; *Memory, Short-Term ; *Brain Mapping/methods ; Male ; Adult ; Brain/physiology ; Female ; Electrodes, Implanted ; }, abstract = {Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates.Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions.Results.From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention.Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.}, }
@article {pmid39260008, year = {2024}, author = {Zhou, S and Yang, B and Yuan, M and Jiang, R and Yan, R and Pan, G and Tang, H}, title = {Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106677}, doi = {10.1016/j.neunet.2024.106677}, pmid = {39260008}, issn = {1879-2782}, abstract = {Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.}, }
@article {pmid39259621, year = {2025}, author = {Chen, D and Song, Z and Du, Y and Chen, S and Zhang, X and Li, Y and Huang, Q}, title = {Aperiodic Component Analysis in Quantification of Steady-State Visually Evoked Potentials.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {2}, pages = {468-479}, doi = {10.1109/TBME.2024.3458060}, pmid = {39259621}, issn = {1558-2531}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; Young Adult ; Algorithms ; }, abstract = {OBJECTIVE: In this study, we aimed to investigate whether and how the aperiodic component in electroencephalograms affects different quantitative processes of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces.<br><br>METHODS: We applied the Fitting Oscillations &amp; One-Over-F method to parameterize power spectra as a combination of periodic oscillations and an aperiodic component. Electroencephalographic responses and system performance were measured and compared using four prevailing methods: power spectral density analysis, canonical correlation analysis, filter bank canonical correlation analysis and the state-of-the-art method, task discriminant component analysis.<br><br>RESULTS: We found that controlling for the aperiodic component prominently downgraded the performance of brain-computer interfaces measured by canonical correlation analysis (94.9% to 82.8%), filter bank canonical correlation analysis (94.1% to 87.6%), and task discriminant component analysis (96.5% to 70.3%). However, it had almost no effect on that measured by power spectral density analysis (80.4% to 78.7%). This was accompanied by a differential aperiodic impact between power spectral density analysis and the other three methods on the differentiation of the target and non-target stimuli.<br><br>CONCLUSION: The aperiodic component distinctly impacts the quantification of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces.<br><br>SIGNIFICANCE: Our work underscores the significance of taking into account the dynamic nature of aperiodic activities in research related to the quantification of steady-state visually evoked potentials.}, }
@article {pmid39257699, year = {2024}, author = {Rueda-Castro, V and Azofeifa, JD and Chacon, J and Caratozzolo, P}, title = {Bridging minds and machines in Industry 5.0: neurobiological approach.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1427512}, pmid = {39257699}, issn = {1662-5161}, abstract = {INTRODUCTION: In transitioning from Industry 4.0 to the forthcoming Industry 5.0, this research explores the fusion of the humanistic view and technological developments to redefine Continuing Engineering Education (CEE). Industry 5.0 introduces concepts like biomanufacturing and human-centricity, embodying the integration of sustainability and resiliency principles in CEE, thereby shaping the upskilling and reskilling initiatives for the future workforce. The interaction of sophisticated concepts such as Human-Machine Interface and Brain-Computer Interface (BCI) forms a conceptual bridge toward the approaching Fifth Industrial Revolution, allowing one to understand human beings and the impact of their biological development across diverse and changing workplace settings.<br><br>METHODS: Our research is based on recent studies into Knowledge, Skills, and Abilities taxonomies, linking these elements with dynamic labor market profiles. This work intends to integrate a biometric perspective to conceptualize and describe how cognitive abilities could be represented by linking a Neuropsychological test and a biometric assessment. We administered the brief Neuropsychological Battery in Spanish (Neuropsi Breve). At the same time, 15 engineering students used the Emotiv insight device that allowed the EEG recollection to measure performance metrics such as attention, stress, engagement, and excitement.<br><br>RESULTS: The findings of this research illustrate a methodology that allowed the first approach to the cognitive abilities of engineering students to be from neurobiological and behavioral perspectives. Additionally, two profiles were extracted from the results. The first illustrates the Neuropsi test areas, its most common mistakes, and its performance ratings regarding the students' sample. The second profile shows the interaction between the EEG and Neuropsi test, showing engineering students' cognitive and emotional states based on biometric levels.<br><br>DISCUSSIONS: The study demonstrates the potential of integrating neurobiological assessment into engineering education, highlighting a significant advancement in addressing the skills requirements of Industry 5.0. The results suggest that obtaining a comprehensive understanding of students' cognitive abilities is possible, and educational interventions can be adapted by combining neuropsychological approaches with EEG data collection. In the future, it is essential to refine these evaluation methods further and explore their applicability in different engineering disciplines. Additionally, it is necessary to investigate the long-term impact of these methods on workforce preparation and performance.}, }
@article {pmid39256727, year = {2024}, author = {Tong, D and Wu, F and Chen, X and Du, Z and Zhou, J and Zhang, J and Yang, Y and Du, A and Ma, G}, title = {The mrp-3 gene is involved in haem efflux and detoxification in a blood-feeding nematode.}, journal = {BMC biology}, volume = {22}, number = {1}, pages = {199}, pmid = {39256727}, issn = {1741-7007}, mesh = {Animals ; *Haemonchus/genetics/metabolism ; *Heme/metabolism ; Inactivation, Metabolic/genetics ; Helminth Proteins/genetics/metabolism ; RNA Interference ; Multidrug Resistance-Associated Proteins/genetics/metabolism ; }, abstract = {BACKGROUND: Haem is essential but toxic for metazoan organisms. Auxotrophic nematodes can acquire sufficient haem from the environment or their hosts in the meanwhile eliminate or detoxify excessive haem through tightly controlled machinery. In previous work, we reported a role of the unique transporter protein HRG-1 in the haem acquisition and homeostasis of parasitic nematodes. However, little is known about the haem efflux and detoxification via ABC transporters, particularly the multiple drug resistance proteins (MRPs).<br><br>RESULTS: Here, we further elucidate that a member of the mrp family (mrp-3) is involved in haem efflux and detoxification in a blood-feeding model gastrointestinal parasite, Haemonchus contortus. This gene is haem-responsive and dominantly expressed in the intestine and inner membrane of the hypodermis of this parasite. RNA interference of mrp-3 resulted in a disturbance of genes (e.g. hrg-1, hrg-2 and gst-1) that are known to be involved in haem homeostasis and an increased formation of haemozoin in the treated larvae and lethality in vitro, particularly when exposed to exogenous haem. Notably, the nuclear hormone receptor NHR-14 appears to be associated the regulation of mrp-3 expression for haem homeostasis and detoxification. Gene knockdown of nhr-14 and/or mrp-3 increases the sensitivity of treated larvae to exogenous haem and consequently a high death rate (>&#x2009;80%).<br><br>CONCLUSIONS: These findings demonstrate that MRP-3 and the associated molecules are essential for haematophagous nematodes, suggesting novel intervention targets for these pathogens in humans and animals.}, }
@article {pmid39255823, year = {2024}, author = {Zhao, S and Cao, Y and Yang, W and Yu, J and Xu, C and Dai, W and Li, S and Pan, G and Luo, B}, title = {DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad7904}, pmid = {39255823}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Consciousness Disorders/diagnosis/physiopathology ; Female ; Male ; Adult ; Middle Aged ; Aged ; Young Adult ; Deep Learning ; Adolescent ; }, abstract = {Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.}, }
@article {pmid39255189, year = {2024}, author = {Jia, T and Meng, L and Li, S and Liu, J and Wu, D}, title = {Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3442-3451}, doi = {10.1109/TNSRE.2024.3457504}, pmid = {39255189}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Imagination/physiology ; *Algorithms ; *Deep Learning ; *Machine Learning ; Computer Security ; Privacy ; }, abstract = {Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.}, }
@article {pmid39255188, year = {2024}, author = {Hu, L and Gao, W and Lu, Z and Shan, C and Ma, H and Zhang, W and Li, Y}, title = {Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3543-3553}, doi = {10.1109/TNSRE.2024.3457502}, pmid = {39255188}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; *Neural Networks, Computer ; Male ; Female ; Adult ; *Algorithms ; Young Adult ; Calibration ; Machine Learning ; }, abstract = {The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross- subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. Results: The proposed method significantly boosts the average accuracy of wearable P300 BCI to 73.23±7.62 % without calibration and 78.75±6.37 % with fine-tuning. The results demonstrate the feasibility and excellent performance of our dataset and framework. A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.}, }
@article {pmid39255187, year = {2024}, author = {Tan, X and Lian, Q and Zhu, J and Zhang, J and Wang, Y and Qi, Y}, title = {Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3432-3441}, doi = {10.1109/TNSRE.2024.3457313}, pmid = {39255187}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Algorithms ; Male ; Female ; Young Adult ; Speech/physiology ; Adult ; Phonetics ; Cluster Analysis ; Language ; }, abstract = {OBJECTIVE: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification.<br><br>METHODS: We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation.<br><br>RESULTS: We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days.<br><br>CONCLUSION: Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable.<br><br>SIGNIFICANCE: Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.}, }
@article {pmid39255091, year = {2024}, author = {Mei, L and Ingolfsson, TM and Cioflan, C and Kartsch, V and Cossettini, A and Wang, X and Benini, L}, title = {An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2024.3457522}, pmid = {39255091}, issn = {1940-9990}, abstract = {Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes. The workflow is deployed on a wearable, parallel ultra-low power BMI platform (BioGAP). Our results based on two in-house datasets, Dataset A and Dataset B, show that the CL workflow improves average accuracy by up to 30.36% and 10.17%, respectively. Furthermore, when implementing the continual learning on a Parallel Ultra-Low Power (PULP) microcontroller (GAP9), it achieves an energy consumption as low as 0.45mJ per inference and an adaptation time of only 21.5 ms, yielding around 25 h of battery life with a small 100 mAh, 3.7 V battery on BioGAP. Our setup, coupled with the compact CNN model and on-device CL capabilities, meets users' needs for improved privacy, reduced latency, and enhanced inter-session performance, offering good promise for smart embedded real-world BMIs.}, }
@article {pmid39255081, year = {2025}, author = {Ding, W and Liu, A and Xie, C and Wang, K and Chen, X}, title = {Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {2}, pages = {503-514}, doi = {10.1109/TBME.2024.3458389}, pmid = {39255081}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Adult ; Male ; *Signal Processing, Computer-Assisted ; Female ; Algorithms ; Young Adult ; *Deep Learning ; }, abstract = {OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data.<br><br>METHODS: Concretely, RCC reconstructs training samples using probabilistically mixed eigenvector matrices derived from covariance matrices across source domains. This process manipulates the channel correlation of training samples, implicitly creating novel synthesized domains. By increasing the diversity of source domains, RCC aims to enhance the domain generalizability of the pre-trained model. The effectiveness of RCC is validated through subject-independent and subject-adaptive classification experiments.<br><br>RESULTS: The results of subject-independent classification demonstrate that RCC significantly improves the classification performance of the pre-trained model on unseen target subjects. Moreover, when compared to the fine-tuning process using the RCC-absent pre-trained model, the fine-tuning process using the RCC-enhanced pre-trained model yields significantly improved performance in the subject-adaptive classification.<br><br>CONCLUSION: RCC proves to enhance the performance of transfer learning by optimizing the utilization of the source domain data.<br><br>SIGNIFICANCE: The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.}, }
@article {pmid39253749, year = {2024}, author = {Donegan, T and Sanchez-Vives, MV}, title = {Perception and control of a virtual body in immersive virtual reality for rehabilitation.}, journal = {Current opinion in neurology}, volume = {37}, number = {6}, pages = {638-644}, pmid = {39253749}, issn = {1473-6551}, mesh = {Humans ; *Virtual Reality ; Neurological Rehabilitation/methods ; Brain-Computer Interfaces ; Musculoskeletal Diseases/rehabilitation ; }, abstract = {PURPOSE OF REVIEW: This review explores recent advances in using immersive virtual reality to improve bodily perception and motor control in rehabilitation across musculoskeletal and neurological conditions, examining how virtual reality's unique capabilities can address the challenges of traditional approaches. The potential in this area of the emerging metaverse and the integration of artificial intelligence in virtual reality are discussed.<br><br>RECENT FINDINGS: In musculoskeletal rehabilitation, virtual reality shows promise in enhancing motivation, adherence, improving range of motion, and reducing kinesiophobia, particularly postsurgery. For neurological conditions like stroke and spinal cord injury, virtual reality's ability to manipulate bodily perceptions offers significant therapeutic potential, with reported improvements in upper limb function and gait performance. Balance and gait rehabilitation, especially in older adults, have also seen positive outcomes. The integration of virtual reality with brain-computer interfaces presents exciting possibilities for severe speech and motor impairments.<br><br>SUMMARY: Current research is limited by small sample sizes, short intervention durations, and variability in virtual reality systems. Future studies should focus on larger, long-term trials to confirm findings and explore underlying mechanisms. As virtual reality technology advances, its integration into rehabilitation programs could revolutionize treatment approaches, personalizing treatments, facilitating home training, and potentially improving patient outcomes across a wide variety of conditions.}, }
@article {pmid39253705, year = {2024}, author = {AlQahtani, NJ and Al-Naib, I and Althobaiti, M}, title = {Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {12}, number = {}, pages = {1454262}, pmid = {39253705}, issn = {2296-4185}, abstract = {The global rise in lower limb amputation cases necessitates advancements in prosthetic limb technology to enhance the quality of life for affected patients. This review paper explores recent advancements in the integration of EEG and fNIRS modalities for smart lower prosthetic limbs for rehabilitation applications. The paper synthesizes current research progress, focusing on the synergy between brain-computer interfaces and neuroimaging technologies to enhance the functionality and user experience of lower limb prosthetics. The review discusses the potential of EEG and fNIRS in decoding neural signals, enabling more intuitive and responsive control of prosthetic devices. Additionally, the paper highlights the challenges, innovations, and prospects associated with the incorporation of these neurotechnologies in the field of rehabilitation. The insights provided in this review contribute to a deeper understanding of the evolving landscape of smart lower prosthetic limbs and pave the way for more effective and user-friendly solutions in the realm of neurorehabilitation.}, }
@article {pmid39253519, year = {2024}, author = {Lyu, C and Li, Z and Xu, C and Wong, KKL and Luginbuhl, DJ and McLaughlin, CN and Xie, Q and Li, T and Li, H and Luo, L}, title = {Dimensionality reduction simplifies synaptic partner matching in an olfactory circuit.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39253519}, issn = {2692-8205}, support = {R01 DC005982/DC/NIDCD NIH HHS/United States ; }, abstract = {The distribution of postsynaptic partners in three-dimensional (3D) space presents complex choices for a navigating axon. Here, we discovered a dimensionality reduction principle in establishing the 3D glomerular map in the fly antennal lobe. Olfactory receptor neuron (ORN) axons first contact partner projection neuron (PN) dendrites at the 2D spherical surface of the antennal lobe during development, regardless of whether the adult glomeruli are at the surface or interior of the antennal lobe. Along the antennal lobe surface, axons of each ORN type take a specific 1D arc-shaped trajectory that precisely intersects with their partner PN dendrites. Altering axon trajectories compromises synaptic partner matching. Thus, a 3D search problem is reduced to 1D, which simplifies synaptic partner matching and may generalize to the wiring process of more complex brains.}, }
@article {pmid39253067, year = {2024}, author = {Xu, Y and Jie, L and Jian, W and Yi, W and Yin, H and Peng, Y}, title = {Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1447662}, pmid = {39253067}, issn = {1662-5161}, abstract = {For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.}, }
@article {pmid39252162, year = {2024}, author = {Yeh, KT and Ho, VW and Chen, TY and Tu, JC and Lin, HY and Chan, KC}, title = {Long-term follow-up of Bonebridge BCI 601 implantation in microtia patients with aural atresia: Acoustic and subjective benefits.}, journal = {Journal of the Chinese Medical Association : JCMA}, volume = {87}, number = {12}, pages = {1090-1097}, doi = {10.1097/JCMA.0000000000001162}, pmid = {39252162}, issn = {1728-7731}, mesh = {Humans ; Female ; Male ; *Congenital Microtia/surgery ; Adult ; Adolescent ; Child ; Retrospective Studies ; Middle Aged ; Young Adult ; *Ear/abnormalities/surgery ; Follow-Up Studies ; Bone Conduction ; Hearing Aids ; Congenital Abnormalities/surgery ; }, abstract = {BACKGROUND: This study evaluated the long-term acoustic and subjective outcomes of Bonebridge bone conduction implant (BCI) 601 implantation in Taiwanese patients with microtia and aural atresia (AA).<br><br>METHODS: A total of 41 microtia patients (28 males and 13 females; 26 with bilateral AA and 15 with unilateral AA) who received Bonebridge BCI 601 implantation between December 2014 and March 2021 at Chang Gung Memorial Hospital, Linkou, Taiwan, were included in this retrospective study. Acoustic outcomes assessed included functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were assessed. Subjective outcomes were assessed using the Chinese versions of four questionnaires: the Abbreviated Profile of Hearing Aid Benefit (APHAB); the Speech, Spatial and Qualities of Hearing Scale; the International Outcome Inventory for Hearing Aids; and the Satisfaction with Amplification in Daily Living.<br><br>RESULTS: The mean age at the time of implantation was 18.9 years (range, 6.3-54.9), and the mean follow-up duration was 6.3 years (range, 2.8-9.1). The mean unaided air conduction pure tone average (PTA4) was 65.3&#x2009;&#xb1;&#x2009;8.8 decibels (dB) hearing level (HL) and the mean aided sound field PTA4 was 31.1&#x2009;&#xb1;&#x2009;9.1 dB HL, resulting in a FHG of 34.2&#x2009;&#xb1;&#x2009;11.7 dB HL (p < 0.05). After Bonebridge implantation, improvements (p < 0.05) in the mean SRT in quiet (from 58.3&#x2009;&#xb1;&#x2009;7.4 dB HL to 29.4&#x2009;&#xb1;&#x2009;7.0 dB HL), SRT in noise (from -1.4&#x2009;&#xb1;&#x2009;7.3 dB signal-to-noise ratio (SNR) to -9.6&#x2009;&#xb1;&#x2009;5.4 dB SNR), WRS in quiet (from 46.4&#x2009;&#xb1;&#x2009;26.9% to 93.8&#x2009;&#xb1;&#x2009;3.1%), and WRS in noise (from 46.7&#x2009;&#xb1;&#x2009;21.8% to 72.7&#x2009;&#xb1;&#x2009;19.3%) were found. Additionally, the bilateral AA group exhibited greater SRT and WRS improvements compared to the unilateral AA group (p < 0.05). All mean subscale scores in the four questionnaires showed improvement after Bonebridge implantation, except for the mean aversiveness to sounds subscale score in the APHAB questionnaire.<br><br>CONCLUSION: Bonebridge BCI 601 implantation provided long-term acoustic and subjective benefits for patients with microtia and AA, particularly those with bilateral AA.}, }
@article {pmid39250958, year = {2024}, author = {Kang, T and Chen, Y and Wallraven, C}, title = {I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad788e}, pmid = {39250958}, issn = {1741-2552}, mesh = {*Artifacts ; Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Male ; Adult ; Algorithms ; Female ; Principal Component Analysis/methods ; Imagination/physiology ; Young Adult ; }, abstract = {Objective.In this paper, we conduct a detailed investigation on the effect of independent component (IC)-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets.Approach.We apply a pipeline matrix of two popular different independent component (IC) decomposition methods (Infomax and Adaptive Mixture Independent Component Analysis (AMICA)) with three different component rejection strategies (none, ICLabel, and multiple artifact rejection algorithm [MARA]) on three different EEG datasets (motor imagery, long-term memory formation, and visual memory). We cross-validate processed data from each pipeline with three architectures commonly used for EEG classification (two convolutional neural networks and one long short-term memory-based model. We compare decoding performances on within-participant and within-dataset levels.Main Results.Our results show that the benefit from using IC-based noise rejection for decoding analyses is at best minor, as component-rejected data did not show consistently better performance than data without rejections-especially given the significant computational resources required for independent component analysis (ICA) computations.Significance.With ever-growing emphasis on transparency and reproducibility, as well as the obvious benefits arising from streamlined processing of large-scale datasets, there has been an increased interest in automated methods for pre-processing EEG data. One prominent part of such pre-processing pipelines consists of identifying and potentially removing artifacts arising from extraneous sources. This is typically done via IC-based correction for which numerous methods have been proposed, differing not only in the decomposition of the raw data into ICs, but also in how they reject the computed ICs. While the benefits of these methods are well established in univariate statistical analyses, it is unclear whether they help in multivariate scenarios, and specifically in neural network-based decoding studies. As computational costs for pre-processing large-scale datasets are considerable, it is important to consider whether the trade-off between model performance and available resources is worth the effort.}, }
@article {pmid39250956, year = {2024}, author = {Yin, J and Liu, A and Wang, L and Qian, R and Chen, X}, title = {Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad788d}, pmid = {39250956}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Artifacts ; Humans ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Deep Learning ; }, abstract = {Objective.Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance.Approach.We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships.Main results.The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU.Significance.The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.}, }
@article {pmid39250394, year = {2024}, author = {Cheng, S and Liu, Y and Gao, Y and Dong, Z}, title = {"As if it were my own hand": inducing the rubber hand illusion through virtual reality for motor imagery enhancement.}, journal = {IEEE transactions on visualization and computer graphics}, volume = {30}, number = {11}, pages = {7086-7096}, doi = {10.1109/TVCG.2024.3456147}, pmid = {39250394}, issn = {1941-0506}, mesh = {Humans ; *Virtual Reality ; *Hand/physiology ; *Brain-Computer Interfaces ; *Illusions/physiology ; *Electroencephalography/methods ; Male ; Female ; *Imagination/physiology ; Young Adult ; Adult ; Computer Graphics ; Movement/physiology ; }, abstract = {Brain-computer interfaces (BCI) are widely used in the field of disability assistance and rehabilitation, and virtual reality (VR) is increasingly used for visual guidance of BCI-MI (motor imagery). Therefore, how to improve the quality of electroencephalogram (EEG) signals for MI in VR has emerged as a critical issue. People can perform MI more easily when they visualize the hand used for visual guidance as their own, and the Rubber Hand Illusion (RHI) can increase people's ownership of the prosthetic hand. We proposed to induce RHI in VR to enhance participants' MI ability and designed five methods of inducing RHI, namely active movement, haptic stimulation, passive movement, active movement mixed with haptic stimulation, and passive movement mixed with haptic stimulation, respectively. We constructed a first-person training scenario to train participants' MI ability through the five induction methods. The experimental results showed that through the training, the participants' feeling of ownership of the virtual hand in VR was enhanced, and the MI ability was improved. Among them, the method of mixing active movement and tactile stimulation proved to have a good effect on enhancing MI. Finally, we developed a BCI system in VR utilizing the above training method, and the performance of the participants improved after the training. This also suggests that our proposed method is promising for future application in BCI rehabilitation systems.}, }
@article {pmid39250352, year = {2024}, author = {Sultana, M and Perdikis, S}, title = {Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3422-3431}, doi = {10.1109/TNSRE.2024.3456591}, pmid = {39250352}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Algorithms ; Reproducibility of Results ; Imagination/physiology ; Machine Learning ; Adult ; }, abstract = {Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system. Our approach exploits the capability of FL to increase the reliability of decision-making by fusing heterogeneous information channels while maintaining transparency and simplicity. We show that our method leads to significant improvement in classification accuracy, feature stability and class bias when applied to large motor imagery or attempt datasets including end-users with motor disabilities. We postulate that combining data-driven methods with knowledge derived from neuroscience literature through FL can enhance the performance, explainability, and learnability of BCIs.}, }
@article {pmid39249732, year = {2024}, author = {Luo, Y and Wu, W and Gao, Z}, title = {Unlocking the Mysteries of the Subcommissural Organ: A Patron Saint of Neuronal Development.}, journal = {Neuroscience bulletin}, volume = {40}, number = {12}, pages = {2012-2014}, pmid = {39249732}, issn = {1995-8218}, }
@article {pmid39248654, year = {2024}, author = {Choudhari, V and Han, C and Bickel, S and Mehta, AD and Schevon, C and McKhann, GM and Mesgarani, N}, title = {Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {41}, pages = {e2401379}, pmid = {39248654}, issn = {2198-3844}, support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; //The Marie-Josee and Henry R. Kravis Foundation/ ; //National Institute of Health's National Institute on Deafness and Other Communication Disorders/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Speech Perception/physiology ; Male ; Hearing Aids ; Female ; Hearing Loss/physiopathology ; Middle Aged ; Brain-Computer Interfaces ; Speech Intelligibility/physiology ; Adult ; Attention/physiology ; }, abstract = {Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain-controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, a realistic AAD task that involves multiple talkers taking turns as they continuously move in space in background noise is presented. Invasive electroencephalography (iEEG) data are collected from three neurosurgical patients as they focused on one of the two moving conversations. An enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model is presented. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve AAD accuracy. Subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of this approach in real-world scenarios and marks a significant step toward developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.}, }
@article {pmid39248624, year = {2024}, author = {Chen, C and Song, Y and Chen, D and Zhu, J and Ning, H and Xiao, R}, title = {Design and application of pneumatic rehabilitation glove system based on brain-computer interface.}, journal = {The Review of scientific instruments}, volume = {95}, number = {9}, pages = {}, doi = {10.1063/5.0225972}, pmid = {39248624}, issn = {1089-7623}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation ; *Stroke Rehabilitation/instrumentation/methods ; Equipment Design ; }, abstract = {Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.}, }
@article {pmid39247844, year = {2024}, author = {Bunterngchit, C and Wang, J and Hou, ZG}, title = {Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.}, journal = {IEEE journal of translational engineering in health and medicine}, volume = {12}, number = {}, pages = {600-612}, pmid = {39247844}, issn = {2168-2372}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; *Deep Learning ; Signal Processing, Computer-Assisted ; Brain/physiology/diagnostic imaging ; Adult ; Male ; Female ; }, abstract = {The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.}, }
@article {pmid39245135, year = {2024}, author = {Feng, J and Gao, S and Hu, Y and Sun, G and Sheng, W}, title = {Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study.}, journal = {World neurosurgery}, volume = {192}, number = {}, pages = {170-187.e1}, doi = {10.1016/j.wneu.2024.08.163}, pmid = {39245135}, issn = {1878-8769}, mesh = {*Brain-Computer Interfaces/trends ; *Spinal Cord Injuries/rehabilitation ; Humans ; *Bibliometrics ; }, abstract = {BACKGROUND: Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI.<br><br>METHODS: We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field.<br><br>RESULTS: The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field.<br><br>CONCLUSIONS: In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.}, }
@article {pmid39244219, year = {2024}, author = {Nyawanda, BO and Khagayi, S and Obor, D and Odhiambo, SB and Beloconi, A and Otieno, NA and Bigogo, G and Kariuki, S and Munga, S and Vounatsou, P}, title = {The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019.}, journal = {BMJ global health}, volume = {9}, number = {9}, pages = {}, pmid = {39244219}, issn = {2059-7908}, support = {U01 GH000048/GH/CGH CDC HHS/United States ; U01 GH002133/GH/CGH CDC HHS/United States ; }, mesh = {Humans ; Kenya/epidemiology ; *Malaria/mortality ; *Bayes Theorem ; *Climate ; Infant ; Child, Preschool ; }, abstract = {BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.<br><br>METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.<br><br>RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.<br><br>CONCLUSION: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.}, }
@article {pmid39243826, year = {2024}, author = {Shen, B and Yao, Q and Li, W and Dong, S and Zhang, H and Zhao, Y and Pan, Y and Jiang, X and Li, D and Chen, Y and Yan, J and Zhang, W and Zhu, Q and Zhang, D and Zhang, L and Wu, Y}, title = {Dynamic cerebellar and sensorimotor network compensation in tremor-dominated Parkinson's disease.}, journal = {Neurobiology of disease}, volume = {201}, number = {}, pages = {106659}, doi = {10.1016/j.nbd.2024.106659}, pmid = {39243826}, issn = {1095-953X}, mesh = {Humans ; *Parkinson Disease/physiopathology/diagnostic imaging/complications ; Male ; Female ; *Tremor/physiopathology/diagnostic imaging ; *Cerebellum/diagnostic imaging/physiopathology ; Middle Aged ; Aged ; *Magnetic Resonance Imaging ; Sensorimotor Cortex/physiopathology/diagnostic imaging ; Nerve Net/diagnostic imaging/physiopathology ; Neural Pathways/physiopathology ; }, abstract = {AIM: Parkinson's disease (PD) tremor is associated with dysfunction in the basal ganglia (BG), cerebellum (CB), and sensorimotor networks (SMN). We investigated tremor-related static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC) in PD patients.<br><br>METHODS: We analyzed the resting-state functional MRI data of 21 tremor-dominant Parkinson's disease (TDPD) patients and 29 healthy controls. We compared DFNC and SFNC between the three networks and assessed their associations with tremor severity.<br><br>RESULTS: TDPD patients exhibited increased SFNC between the SMN and BG networks. In addition, they spent more mean dwell time (MDT) in state 2, characterized by sparse connections, and less MDT in state 4, indicating stronger connections. Furthermore, enhanced DFNC between the CB and SMN was observed in state 2. Notably, the MDT of state 2 was positively associated with tremor scores.<br><br>CONCLUSION: The enhanced dynamic connectivity between the CB and SMN in TDPD patients suggests a potential compensatory mechanism. However, the tendency to remain in a state of sparse connectivity may contribute to the severity of tremor symptoms.}, }
@article {pmid39242446, year = {2024}, author = {Imath, M and Ragavendran, C}, title = {Letter to editor: "Neuralink's brain implant: a vision for enhanced human-machine integration".}, journal = {Neurosurgical review}, volume = {47}, number = {1}, pages = {566}, doi = {10.1007/s10143-024-02806-1}, pmid = {39242446}, issn = {1437-2320}, mesh = {Humans ; *Brain-Computer Interfaces ; Brain/surgery ; }, }
@article {pmid39241437, year = {2024}, author = {Choo, S and Park, H and Jung, JY and Flores, K and Nam, CS}, title = {Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106665}, doi = {10.1016/j.neunet.2024.106665}, pmid = {39241437}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; Machine Learning ; Deep Learning ; Brain/physiology ; }, abstract = {In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.}, }
@article {pmid39240852, year = {2024}, author = {Yamashiro, K and Matsumoto, N and Ikegaya, Y}, title = {Diffusion model-based image generation from rat brain activity.}, journal = {PloS one}, volume = {19}, number = {9}, pages = {e0309709}, pmid = {39240852}, issn = {1932-6203}, mesh = {Animals ; Rats ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Neural Networks, Computer ; Models, Neurological ; }, abstract = {Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens new possibilities for creative expression and experimentation. Notably, our results show that the generated images successfully reflect the dynamic and stochastic nature of the underlying neural activity, providing a unique procedure for visualization of brain function.}, }
@article {pmid39240342, year = {2024}, author = {Yang, D and Sun, Q and Li, W and Wang, Y and Qian, H and Li, D}, title = {Efficiency of HoLEP in patients with detrusor underactivity and renal dysfunction secondary to BPO.}, journal = {World journal of urology}, volume = {42}, number = {1}, pages = {509}, pmid = {39240342}, issn = {1433-8726}, support = {20214Y012//General Project of Shanghai Changning District Health Commission/ ; 202040140//Surface Project of Shanghai Health Commission/ ; }, mesh = {Humans ; Male ; *Lasers, Solid-State/therapeutic use ; Aged ; *Prostatic Hyperplasia/complications/surgery ; *Urinary Bladder, Underactive/physiopathology ; Prospective Studies ; Middle Aged ; *Urinary Bladder Neck Obstruction/surgery/etiology ; Treatment Outcome ; Laser Therapy/methods ; Prostatectomy/methods/adverse effects ; Kidney Diseases/surgery/complications ; }, abstract = {PURPOSE: The purpose of this study was to assess the bladder and renal functional outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic obstruction (BPO) complicated by detrusor underactivity (DU) and secondary renal dysfunction.<br><br>METHODS: Thirty-one patients were included in this prospective study. Eligible patients had urinary retention, a bladder outlet obstruction index (BOOI) greater than 40, a bladder contractility index (BCI) less than 100, abnormal renal function at the initial diagnosis (serum creatinine&#x2009;>&#x2009;132 &#xb5;mol/L) and a renal pelvis anteroposterior diameter (PRAPD)&#x2009;>&#x2009;1.5&#xa0;cm bilaterally. All patients underwent HoLEP in a routine manner and were evaluated preoperatively and at 1, 3 and 6 months after surgery. The baseline characteristics of the patients, perioperative data, postoperative outcomes and complications were assessed.<br><br>RESULTS: Significant improvement was observed in the international prostate symptom score (IPSS), quality of life (QoL) score, maximal urinary flow rate (Qmax), post-void residual volume (PVR), Scr and RPAPD at the 6-month follow-up. Bladder wall thickness (BWT) exhibited a decreasing trend but did not significantly differ from the preoperative values. No grade 3 or higher adverse events occurred, and grade 3 and lower complications were treated conservatively. Three patients required reinsertion of indwelling catheters, and they were able to void spontaneously after two weeks of catheterisation training and medication treatment.<br><br>CONCLUSION: HoLEP is an effective treatment for men with BPO accompanied by DU and consequent renal function impairment. Patients are able to regain spontaneous voiding. Both bladder and renal functions were preserved and improved.}, }
@article {pmid39237192, year = {2024}, author = {Pons, JL and Reys, V and Grand, F and Moreau, V and Gracy, J and Exner, TE and Labesse, G}, title = {@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.}, journal = {Journal of molecular biology}, volume = {436}, number = {17}, pages = {168704}, doi = {10.1016/j.jmb.2024.168704}, pmid = {39237192}, issn = {1089-8638}, mesh = {Ligands ; *Molecular Docking Simulation ; *Proteins/chemistry/metabolism ; *Protein Conformation ; Binding Sites ; Protein Binding ; Software ; Drug Design ; Models, Molecular ; }, abstract = {Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.}, }
@article {pmid39237038, year = {2024}, author = {Chen, S and Wang, Y and Lin, X and Sun, X and Li, W and Ma, W}, title = {Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.}, journal = {Journal of neuroscience methods}, volume = {411}, number = {}, pages = {110276}, doi = {10.1016/j.jneumeth.2024.110276}, pmid = {39237038}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition.<br><br>This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data.<br><br>NEW METHOD: To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize.<br><br>RESULTS: The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models.<br><br>CONCLUSIONS: The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.}, }
@article {pmid39236139, year = {2025}, author = {Yang, X and Zhu, Z and Jiang, G and Wu, D and He, A and Wang, J}, title = {DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-Temporal Graph Convolutional Networks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {4}, pages = {2471-2483}, doi = {10.1109/JBHI.2024.3449083}, pmid = {39236139}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Brain-Computer Interfaces ; Deep Learning ; Adult ; }, abstract = {Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electroencephalogram (EEG) recently. The complexity of EEG data poses a challenge when it comes to accurately classifying emotions by integrating time, frequency, and spatial domain features. To address this challenge, this paper proposes a fusion model called DC-ASTGCN, which combines the strengths of deep convolutional neural network (DCNN) and adaptive spatio-temporal graphic convolutional neural network (ASTGCN) to comprehensively analyze and understand EEG signals. The DCNN focuses on extracting frequency-domain and local spatial features from EEG signals to identify brain region activity patterns, while the ASTGCN, with its spatio-temporal attention mechanism and adaptive brain topology layer, reveals the functional connectivity features between brain regions in different emotional states. This integration significantly enhances the model's ability to understand and recognize emotional states. Extensive experiments conducted on the DEAP and SEED datasets demonstrate that the DC-ASTGCN model outperforms existing state-of-the-art methods in terms of emotion recognition accuracy.}, }
@article {pmid39236133, year = {2024}, author = {Rong, F and Yang, B and Guan, C}, title = {Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3399-3409}, doi = {10.1109/TNSRE.2024.3454088}, pmid = {39236133}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Machine Learning ; *Algorithms ; Healthy Volunteers ; Movement/physiology ; Upper Extremity/physiology ; Stroke Rehabilitation/methods ; }, abstract = {The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.}, }
@article {pmid39234682, year = {2025}, author = {Longo, UG and Marino, M and de Sire, A and Ruiz-Iban, MA and D'Hooghe, P}, title = {The bioinductive collagen implant yields positive histological, clinical and MRI outcomes in the management of rotator cuff tears: A systematic review.}, journal = {Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA}, volume = {33}, number = {3}, pages = {1070-1090}, pmid = {39234682}, issn = {1433-7347}, mesh = {*Rotator Cuff Injuries/surgery/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; *Collagen/therapeutic use ; Animals ; Rotator Cuff/surgery/pathology/diagnostic imaging ; *Prostheses and Implants ; Treatment Outcome ; }, abstract = {PURPOSE: The aim of this study is to report and discuss the outcomes of clinical, histological and animal studies exploring the application of bio-inductive collagen implants (BCIs) to partial and full-thickness rotator cuff tears (PT- and FT-RCTs) in addition to reporting on cost-related factors.<br><br>METHODS: Review of literature was performed using the PRISMA guidelines. A systematic electronic literature search was conducted using the CENTRAL, CINAHL, Cochrane Library, EBSCOhost, EMBASE and Google Scholar bibliographic databases. Microsoft Excel was used to create tables onto which extracted data were recorded. Tables were organized based on the research statement formulated using the PICO approach. No statistical analysis was performed.<br><br>RESULTS: Nine studies evaluated clinical and MRI outcomes of BCI augmentation for FT-RCTs, seven evaluated similar outcomes when applied to PT-RCTs, two additional studies were case reports and three studies assessed application to FT- and PT-RCTs without stratification of results, one of which also reported on histological data. Two studies reported on histological data alone, and finally, two reported on healthcare costs. BCI augmentation, alone and combined with rotator cuff repair (RCR), displays generally good histological, postoperative clinical and MRI outcomes for PT- and FT-RCT treatment. Recent economic analyses seem to be in favour of the use of this procedure, when selected and applied for appropriate patient populations.<br><br>CONCLUSION: Several studies have shown promising results of BCI application to PT- and FT-RCTs, both concomitantly and independently from RCR. Investigations report promising histological characteristics, improved clinical outcomes, increased tendon thickness, reduced defect size and lower re-tear rates.<br><br>LEVEL OF EVIDENCE: Level IV.}, }
@article {pmid39234592, year = {2024}, author = {Tajmirriahi, M and Rabbani, H}, title = {A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.}, journal = {Journal of medical signals and sensors}, volume = {14}, number = {}, pages = {19}, pmid = {39234592}, issn = {2228-7477}, abstract = {Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.}, }
@article {pmid39234406, year = {2024}, author = {Lakshminarayanan, K and Madathil, D and Murari, BM and Shah, R}, title = {Editorial: Recent advancements in brain-computer interfaces-based limb rehabilitation.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1466450}, doi = {10.3389/fnhum.2024.1466450}, pmid = {39234406}, issn = {1662-5161}, }
@article {pmid39234182, year = {2024}, author = {Si, Y and Wang, Z and Xu, G and Wang, Z and Xu, T and Zhou, T and Hu, H}, title = {Group-member selection for RSVP-based collaborative brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1402154}, pmid = {39234182}, issn = {1662-4548}, abstract = {OBJECTIVE: The brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost. However, the key factors that enhance the collaboration capabilities of multiple users and how to further use these factors to optimize group mode remain unclear.<br><br>APPROACH: This study proposed a group-member selection strategy to optimize the group mode and improve the system performance for RSVP-based cBCI. In contrast to the conventional grouping of collaborators at random, the group-member selection strategy enabled pairing each user with a better collaborator and allowed tasks to be done with fewer collaborators. Initially, we introduced the maximum individual capability and maximum collaborative capability (MIMC) to select optimal pairs, improving the system classification performance. The sequential forward floating selection (SFFS) combined with MIMC then selected a sub-group, aiming to reduce the hardware and labor expenses in the cBCI system. Moreover, the hierarchical discriminant component analysis (HDCA) was used as a classifier for within-session conditions, and the Euclidean space data alignment (EA) was used to overcome the problem of inter-trial variability for cross-session analysis.<br><br>MAIN RESULTS: In this paper, we verified the effectiveness of the proposed group-member selection strategy on a public RSVP-based cBCI dataset. For the two-user matching task, the proposed MIMC had a significantly higher AUC and TPR and lower FPR than the common random grouping mode and the potential group-member selection method. Moreover, the SFFS with MIMC enabled a trade-off between maintaining performance and reducing the number of system users.<br><br>SIGNIFICANCE: The results showed that our proposed MIMC effectively optimized the group mode, enhanced the classification performance in the two-user matching task, and could reduce the redundant information by selecting the sub-group in the RSVP-based multi-user cBCI systems.}, }
@article {pmid39233125, year = {2024}, author = {Mao, T and Guo, B and Quan, P and Deng, Y and Chai, Y and Xu, J and Jiang, C and Zhang, Q and Lu, Y and Goel, N and Basner, M and Dinges, DF and Rao, H}, title = {Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation.}, journal = {NeuroImage}, volume = {299}, number = {}, pages = {120833}, doi = {10.1016/j.neuroimage.2024.120833}, pmid = {39233125}, issn = {1095-9572}, mesh = {Humans ; Male ; Female ; *Magnetic Resonance Imaging ; *Hypothalamus/diagnostic imaging/physiology ; Adult ; Young Adult ; *Individuality ; Circadian Rhythm/physiology ; Sleepiness ; Neural Pathways/physiology/diagnostic imaging ; Corpus Striatum/diagnostic imaging/physiology ; Wakefulness/physiology ; Sleep/physiology ; }, abstract = {While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.}, }
@article {pmid39232389, year = {2024}, author = {Li, N and Chen, S and Wu, Z and Dong, J and Wang, J and Lei, Y and Mo, J and Wei, W and Li, T}, title = {Secular trends in the prevalence of schizophrenia among different age, period and cohort groups between 1990 and 2019.}, journal = {Asian journal of psychiatry}, volume = {101}, number = {}, pages = {104192}, doi = {10.1016/j.ajp.2024.104192}, pmid = {39232389}, issn = {1876-2026}, mesh = {Humans ; *Schizophrenia/epidemiology ; Adult ; Middle Aged ; Prevalence ; Male ; Female ; Young Adult ; Adolescent ; Aged ; Cohort Studies ; Age Factors ; Global Health/statistics &amp; numerical data ; Global Burden of Disease/trends ; }, abstract = {BACKGROUND: Schizophrenia remains a major public health challenge, and designing efforts to manage it requires understanding its prevalence over time at different geographic scales and population groups.<br><br>METHODS: Drawing on data from the Global Burden of Disease study 2019, annual percentage change of schizophrenia was assessed across different age, period and cohort groups at different geographic scales from 1990 to 2019. We examined associations of prevalence with the sociodemographic index.<br><br>RESULTS: Global prevalence of schizophrenia in 2019 was 23.60 million (95&#x202f;% uncertainty interval: 20.23-27.15), with China, India, the USA and Indonesia accounting for 50.72&#x202f;% of it. Global prevalence increased slightly from 1990 to 2019, with an annual percentage change of 0.03&#x202f;% (95&#x202f;% confidence interval 0.01-0.05). Regions with intermediate sociodemographic index accounted for greater proportion of prevalence increasing than regions with high index. Prevalence decreased among those born after 1979 in regions with intermediate sociodemographic index, whereas it consistently improved among all birth cohorts in regions with low index. Regardless of sociodemographic index, prevalence was highest among individuals 30-59 years old than younger or older groups.<br><br>CONCLUSIONS: Prevalence of schizophrenia has shown small increases globally over the last three decades. The burden of disease is heavier in relatively less affluent regions, and it disproportionately affects individuals 30-59 years in all regions. Meanwhile, for regions with lower sociodemographic indices, the recent increasing burden among birth cohorts is more pronounced. These findings may help guide futural design of measures to manage or prevent schizophrenia in communities at higher risk.}, }
@article {pmid39232069, year = {2024}, author = {Shang, L and Si, H and Wang, H and Pan, T and Liu, H and Li, Y and Qiu, J and Xu, M}, title = {Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {20641}, pmid = {39232069}, issn = {2045-2322}, support = {NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No. 202101042005//the first batch of industry-university-research cooperative collaborative education projects of the Ministry of Education in 2021/ ; No.ZDGB2021024//the Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project/ ; }, mesh = {Humans ; *Fatigue/diagnosis ; *Algorithms ; *Neural Networks, Computer ; Aircraft ; Pilots ; Face ; Machine Learning ; Accidents, Aviation ; }, abstract = {Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.}, }
@article {pmid39231981, year = {2024}, author = {Wang, Q and Sun, RY and Hu, JX and Sun, YH and Li, CY and Huang, H and Wang, H and Li, XM}, title = {Hypothalamic-hindbrain circuit for consumption-induced fear regulation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {7728}, pmid = {39231981}, issn = {2041-1723}, support = {82090031//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82090030//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900723//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001186//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019TQ0278//China Postdoctoral Science Foundation/ ; }, mesh = {Animals ; *Fear/physiology ; Male ; Mice ; Hypothalamus/physiology/metabolism ; Relaxin/metabolism ; Neurons/physiology/metabolism ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Eating/physiology ; Conditioning, Classical/physiology ; Hypothalamic Area, Lateral/physiology/metabolism ; }, abstract = {To ensure survival, animals must sometimes suppress fear responses triggered by potential threats during feeding. However, the mechanisms underlying this process remain poorly understood. In the current study, we demonstrated that when fear-conditioned stimuli (CS) were presented during food consumption, a neural projection from lateral hypothalamic (LH) GAD2 neurons to nucleus incertus (NI) relaxin-3 (RLN3)-expressing neurons was activated, leading to a reduction in CS-induced freezing behavior in male mice. LH[GAD2] neurons established excitatory connections with the NI. The activity of this neural circuit, including NI[RLN3] neurons, attenuated CS-induced freezing responses during food consumption. Additionally, the lateral mammillary nucleus (LM), which received NI[RLN3] projections, along with RLN3 signaling in the LM, mediated the decrease in freezing behavior. Collectively, this study identified an LH[GAD2]-NI[RLN3]-LM circuit involved in modulating fear responses during feeding, thereby enhancing our understanding of how animals coordinate nutrient intake with threat avoidance.}, }
@article {pmid39231469, year = {2024}, author = {Baberwal, SS and Magre, LA and Gunawardhana, KRSD and Parkinson, M and Ward, T and Coyle, S}, title = {Motor imagery with cues in virtual reality, audio and screen.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad775e}, pmid = {39231469}, issn = {1741-2552}, abstract = {Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.}, }
@article {pmid39231466, year = {2024}, author = {Yang, L and Sun, Q and Van Hulle, MM}, title = {Binocularly incongruent, multifrequency-coded SSVEP in VR: feasibility and characteristics.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad775f}, pmid = {39231466}, issn = {1741-2552}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Vision, Binocular/physiology ; Male ; Female ; Adult ; *Photic Stimulation/methods ; Young Adult ; *Virtual Reality ; *Electroencephalography/methods ; *Feasibility Studies ; Brain-Computer Interfaces ; }, abstract = {Objective.Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing but their implementation in virtual reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).Approach.This paper reports on a systematical investigation of incongruent dual-, triple-, and quadruple-frequency-coded SSVEP for use in VR, several of which are entirely novel, and compares their performance with that of congruent dual-frequency-coded SSVEP.Main results.We were able to confirm the presence of a summation effect when comparing monocular- and binocular single-frequency congruent stimulation, and a suppression effect when comparing monocular- and binocular dual-frequency incongruent stimulation, as both tap into the binocular vision capabilities which, when hampered, could signal amblyopia.Significance.In sum, our findings not only evidence the potential of VR-based binocularly incongruent SSVEP but also underscore the importance of paradigm choice and decoder design to optimize system performance and user comfort.}, }
@article {pmid39231465, year = {2024}, author = {Pulferer, HS and Kostoglou, K and Müller-Putz, GR}, title = {Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad7762}, pmid = {39231465}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; *Electroencephalography/methods ; Young Adult ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.}, }
@article {pmid39229920, year = {2024}, author = {Ding, L and Guo, H and Zhang, J and Zheng, M and Zhang, W and Wang, L and Du, Q and Zhou, C and Xu, Y and Wu, H and He, Q and Yang, B}, title = {Zosuquidar Promotes Antitumor Immunity by Inducing Autophagic Degradation of PD-L1.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {41}, pages = {e2400340}, pmid = {39229920}, issn = {2198-3844}, support = {82330114//National Natural Science Foundation of China/ ; 82273949//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *B7-H1 Antigen/metabolism/genetics/immunology ; Mice ; *Autophagy ; Humans ; Disease Models, Animal ; Cell Line, Tumor ; ATP Binding Cassette Transporter, Subfamily B/metabolism/genetics ; }, abstract = {The intracellular distribution and transportation process are essential for maintaining PD-L1 (programmed death-ligand 1) expression, and intervening in this cellular process may provide promising therapeutic strategies. Here, through a cell-based high content screening, it is found that the ABCB1 (ATP binding cassette subfamily B member 1) modulator zosuquidar dramatically suppresses PD-L1 expression by triggering its autophagic degradation. Mechanistically, ABCB1 interacts with PD-L1 and impairs COP II-mediated PD-L1 transport from ER (endoplasmic reticulum) to Golgi apparatus. The treatment of zosuquidar enhances ABCB1-PD-L1 interaction and leads the ER retention of PD-L1, which is subsequently degraded in the SQSTM1-dependent selective autophagy pathway. In CT26 mouse model and a humanized xenograft mouse model, zosuquidar significantly suppresses tumor growth and accompanies by increased infiltration of cytotoxic T cells. In summary, this study indicates that ABCB1 serves as a negative regulator of PD-L1, and zosuquidar may act as a potential immunotherapy agent by triggering PD-L1 degradation in the early secretory pathway.}, }
@article {pmid39229190, year = {2024}, author = {Vargas-Irwin, CE and Hosman, T and Gusman, JT and Pun, TK and Simeral, JD and Singer-Clark, T and Kapitonava, A and Nicolas, C and Shah, NP and Avansino, D and Kamdar, F and Williams, Z and Henderson, JM and Hochberg, LR}, title = {Gesture encoding in human left precentral gyrus neuronal ensembles.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.08.23.608325}, pmid = {39229190}, issn = {2692-8205}, abstract = {Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.}, }
@article {pmid39229047, year = {2024}, author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Hochberg, LR and Brandman, DM and Stavisky, SD}, title = {An instantaneous voice synthesis neuroprosthesis.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39229047}, issn = {2692-8205}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to speak due to neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech such as prosody, intonation and immediately hearing one's own voice. Here, we demonstrate a "brain-to-voice" neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real-time to change intonation, emphasize words, and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.}, }
@article {pmid39227389, year = {2024}, author = {Chang, H and Sun, Y and Lu, S and Lin, D}, title = {A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer interface to improve the effect of node displacement.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {20420}, pmid = {39227389}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; Humans ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.}, }
@article {pmid39226926, year = {2024}, author = {Shen, J and Garrad, M and Zhang, Q and Wong, VCH and Pirrera, A and Groh, RMJ}, title = {A rapid-response soft end effector inspired by the hummingbird beak.}, journal = {Journal of the Royal Society, Interface}, volume = {21}, number = {218}, pages = {20240148}, pmid = {39226926}, issn = {1742-5662}, support = {//Royal Academy of Engineering/ ; //Leverhulme Trust/ ; }, mesh = {Animals ; *Beak/physiology/anatomy &amp; histology ; *Birds/physiology ; Robotics ; Models, Biological ; Biomechanical Phenomena ; }, abstract = {Biology is a wellspring of inspiration in engineering design. This paper delves into the application of elastic instabilities-commonly used in biological systems to facilitate swift movement-as a power-amplification mechanism for soft robots. Specifically, inspired by the nonlinear mechanics of the hummingbird beak-and shedding further light on it-we design, build and test a novel, rapid-response, soft end effector. The hummingbird beak embodies the capacity for swift movement, achieving closure in less than [Formula: see text]. Previous work demonstrated that rapid movement is achieved through snap-through deformations, induced by muscular actuation of the beak's root. Using nonlinear finite element simulations coupled with continuation algorithms, we unveil a representative portion of the equilibrium manifold of the beak-inspired structure. The exploration involves the application of a sequence of rotations as exerted by the hummingbird muscles. Specific emphasis is placed on pinpointing and tailoring the position along the manifold of the saddle-node bifurcation at which the onset of elastic instability triggers dynamic snap-through. We show the critical importance of the intermediate rotation input in the sequence, as it results in the accumulation of elastic energy that is then explosively released as kinetic energy upon snap-through. Informed by our numerical studies, we conduct experimental testing on a prototype end effector fabricated using a compliant material (thermoplastic polyurethane). The experimental results support the trends observed in the numerical simulations and demonstrate the effectiveness of the bio-inspired design. Specifically, we measure the energy transferred by the soft end effector to a pendulum, varying the input levels in the sequence of prescribed rotations. Additionally, we demonstrate a potential robotic application in scenarios demanding explosive action. From a mechanics perspective, our work sheds light on how pre-stress fields can enable swift movement in soft robotic systems with the potential to facilitate high input-to-output energy efficiency.}, }
@article {pmid39226850, year = {2024}, author = {Li, S and Daly, I and Guan, C and Cichocki, A and Jin, J}, title = {Inter-participant transfer learning with attention based domain adversarial training for P300 detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106655}, doi = {10.1016/j.neunet.2024.106655}, pmid = {39226850}, issn = {1879-2782}, mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Attention/physiology ; Deep Learning ; }, abstract = {A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.}, }
@article {pmid39226701, year = {2024}, author = {Ji, Y and Silva, RF and Adali, T and Wen, X and Zhu, Q and Jiang, R and Zhang, D and Qi, S and Calhoun, VD}, title = {Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.}, journal = {NeuroImage. Clinical}, volume = {43}, number = {}, pages = {103663}, pmid = {39226701}, issn = {2213-1582}, mesh = {Humans ; Male ; Female ; Adult ; *Schizophrenia/diagnosis ; *Magnetic Resonance Imaging/methods ; Autism Spectrum Disorder/diagnosis ; Brain/physiopathology/diagnostic imaging ; Young Adult ; Mental Disorders/diagnosis ; Adolescent ; Diagnosis, Computer-Assisted/methods ; }, abstract = {Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %±2.22 % and 84.8 %±2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.}, }
@article {pmid39226201, year = {2024}, author = {Liu, J and Wang, R and Yang, Y and Zong, Y and Leng, Y and Zheng, W and Ge, S}, title = {Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {11}, pages = {6581-6593}, doi = {10.1109/JBHI.2024.3454158}, pmid = {39226201}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods/classification ; *Signal Processing, Computer-Assisted ; *Algorithms ; Adult ; Male ; }, abstract = {Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.}, }
@article {pmid39225378, year = {2025}, author = {Zhang, H and Zheng, Z and Chen, X and Xu, L and Guo, C and Wang, J and Cui, Y and Yang, F}, title = {RADICAL: a rationally designed ion channel activated by ligand for chemogenetics.}, journal = {Protein &amp; cell}, volume = {16}, number = {2}, pages = {136-142}, pmid = {39225378}, issn = {1674-8018}, support = {32122040//National Natural Science Foundation of China/ ; LR20C050002//Zhejiang Provincial Natural Science Foundation of China/ ; }, }
@article {pmid39225144, year = {2024}, author = {Quan, P and Mao, T and Zhang, X and Wang, R and Lei, H and Wang, J and Liu, W and Dinges, DF and Jiang, C and Rao, H}, title = {Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation.}, journal = {Human brain mapping}, volume = {45}, number = {13}, pages = {e70013}, pmid = {39225144}, issn = {1097-0193}, support = {2023WTSCX034//Guangdong Scientific Research Platform and Projects for the Higher-educational Institution/ ; 32200889//National Natural Science Foundation of China/ ; 71942003//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; 2021KFKT012//Shanghai International Studies University Research Projects/ ; GD23XXL07//Guangdong Philosophy and Social Science Planning/ ; CTRC UL1RR024134/NH/NIH HHS/United States ; P30-NS045839/NH/NIH HHS/United States ; R01-HL102119/NH/NIH HHS/United States ; R01-MH107571/NH/NIH HHS/United States ; R21-AG051981/NH/NIH HHS/United States ; }, mesh = {Humans ; *Sleep Deprivation/diagnostic imaging/physiopathology/pathology ; *Locus Coeruleus/diagnostic imaging/pathology ; Male ; Female ; *Diffusion Tensor Imaging ; Adult ; Young Adult ; *Psychomotor Performance/physiology ; Arousal/physiology ; Anisotropy ; Neuropsychological Tests ; }, abstract = {Insufficient sleep compromises cognitive performance, diminishes vigilance, and disrupts daily functioning in hundreds of millions of people worldwide. Despite extensive research revealing significant variability in vigilance vulnerability to sleep deprivation, the underlying mechanisms of these individual differences remain elusive. Locus coeruleus (LC) plays a crucial role in the regulation of sleep-wake cycles and has emerged as a potential marker for vigilance vulnerability to sleep deprivation. In this study, we investigate whether LC microstructural integrity, assessed by fractional anisotropy (FA) through diffusion tensor imaging (DTI) at baseline before sleep deprivation, can predict impaired psychomotor vigilance test (PVT) performance during sleep deprivation in a cohort of 60 healthy individuals subjected to a rigorously controlled in-laboratory sleep study. The findings indicate that individuals with high LC FA experience less vigilance impairment from sleep deprivation compared with those with low LC FA. LC FA accounts for 10.8% of the variance in sleep-deprived PVT lapses. Importantly, the relationship between LC FA and impaired PVT performance during sleep deprivation is anatomically specific, suggesting that LC microstructural integrity may serve as a biomarker for vigilance vulnerability to sleep loss.}, }
@article {pmid39222461, year = {2024}, author = {Wang, Z and Liu, Y and Huang, S and Qiu, S and Zhang, Y and Huang, H and An, X and Ming, D}, title = {EEG Characteristic Comparison of Motor Imagery Between Supernumerary and Inherent Limb: Sixth-Finger MI Enhances the ERD Pattern and Classification Performance.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {12}, pages = {7078-7089}, doi = {10.1109/JBHI.2024.3452701}, pmid = {39222461}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Imagination/physiology ; *Fingers/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; Robotics ; Brain-Computer Interfaces ; Movement/physiology ; }, abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.}, }
@article {pmid39220036, year = {2024}, author = {Kim, E and Kim, Y}, title = {Exploring the potential of spiking neural networks in biomedical applications: advantages, limitations, and future perspectives.}, journal = {Biomedical engineering letters}, volume = {14}, number = {5}, pages = {967-980}, pmid = {39220036}, issn = {2093-985X}, abstract = {In this paper, a comprehensive exploration is undertaken to elucidate the utilization of Spiking Neural Networks (SNNs) within the biomedical domain. The investigation delves into the experimentally validated advantages of SNNs in comparison to alternative models like LSTM, while also critically examining the inherent limitations of SNN classifiers or algorithms. SNNs exhibit distinctive advantages that render them particularly apt for targeted applications within the biomedical field. Over time, SNNs have undergone extensive scrutiny in realms such as neuromorphic processing, Brain-Computer Interfaces (BCIs), and Disease Diagnosis. Notably, SNNs demonstrate a remarkable affinity for the processing and analysis of biomedical signals, including but not limited to electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) data. This paper initiates its exploration by introducing some of the biomedical applications of EMG, such as the classification of hand gestures and motion decoding. Subsequently, the focus extends to the applications of SNNs in the analysis of EEG and ECG signals. Moreover, the paper delves into the diverse applications of SNNs in specific anatomical regions, such as the eyes and noses. In the final sections, the paper culminates with a comprehensive analysis of the field, offering insights into the advantages, disadvantages, challenges, and opportunities introduced by various SNN models in the realm of healthcare and biomedical domains. This holistic examination provides a nuanced perspective on the potential transformative impact of SNN across a spectrum of applications within the biomedical landscape.}, }
@article {pmid39218593, year = {2024}, author = {Guo, M and Yang, B and Geng, Y and Jie, R and Zhang, Y and Zheng, Y}, title = {[Visual object detection system based on augmented reality and steady-state visual evoked potential].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {684-691}, pmid = {39218593}, issn = {1001-5515}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Augmented Reality ; *Electroencephalography ; Photic Stimulation ; User-Computer Interface ; Algorithms ; }, abstract = {This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.}, }
@article {pmid39218592, year = {2024}, author = {Zhang, Y and Liu, D and Gao, F}, title = {[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {673-683}, pmid = {39218592}, issn = {1001-5515}, mesh = {*Spectroscopy, Near-Infrared/methods ; Humans ; *Brain-Computer Interfaces ; Deep Learning ; Algorithms ; Brain/physiology/diagnostic imaging ; Neural Networks, Computer ; }, abstract = {In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.}, }
@article {pmid39218591, year = {2024}, author = {Xie, P and Men, Y and Zhen, J and Shao, X and Zhao, J and Chen, X}, title = {[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {664-672}, pmid = {39218591}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Humans ; *Electroencephalography ; *Robotics/instrumentation ; *Algorithms ; Discriminant Analysis ; }, abstract = {Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.}, }
@article {pmid39218590, year = {2024}, author = {Shao, X and Zhang, Y and Zhang, D and Men, Y and Wang, Z and Chen, X and Xie, P}, title = {[Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {656-663}, pmid = {39218590}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; *Hand/physiology ; *Virtual Reality ; *Stroke Rehabilitation/methods/instrumentation ; *Electroencephalography ; Feedback, Sensory ; User-Computer Interface ; Motor Cortex/physiology ; }, abstract = {Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.}, }
@article {pmid39218589, year = {2024}, author = {Wang, Y and Li, Y and Cui, H and Li, M and Chen, X}, title = {[A review of functional electrical stimulation based on brain-computer interface].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {650-655}, pmid = {39218589}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces ; Humans ; Electric Stimulation/methods ; Stroke Rehabilitation/methods ; Spinal Cord Injuries/rehabilitation ; Electric Stimulation Therapy/methods ; }, abstract = {Individuals with motor dysfunction caused by damage to the central nervous system are unable to transmit voluntary movement commands to their muscles, resulting in a reduced ability to control their limbs. However, traditional rehabilitation methods have problems such as long treatment cycles and high labor costs. Functional electrical stimulation (FES) based on brain-computer interface (BCI) connects the patient's intentions with muscle contraction, and helps to promote the reconstruction of nerve function by recognizing nerve signals and stimulating the moving muscle group with electrical impulses to produce muscle convulsions or limb movements. It is an effective treatment for sequelae of neurological diseases such as stroke and spinal cord injury. This article reviewed the current research status of BCI-based FES from three aspects: BCI paradigms, FES parameters and rehabilitation efficacy, and looked forward to the future development trend of this technology, in order to improve the understanding of BCI-based FES.}, }
@article {pmid39218588, year = {2024}, author = {Chen, Y and Zhang, Z and Wang, F and Ding, P and Zhao, L and Fu, Y}, title = {[An emerging discipline: brain-computer interfaces medicine].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {4}, pages = {641-649}, pmid = {39218588}, issn = {1001-5515}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography ; Brain/physiology ; }, abstract = {With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.}, }
@article {pmid39218255, year = {2024}, author = {Guellil, MS and Kies, F and Hussein, EK and Shabaz, M and Hampson, RE}, title = {Pushing the boundaries of brain-computer interfacing (BCI) and neuron-electronics.}, journal = {Journal of neuroscience methods}, volume = {411}, number = {}, pages = {110274}, doi = {10.1016/j.jneumeth.2024.110274}, pmid = {39218255}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Neurons/physiology ; Brain/physiology ; }, }
@article {pmid39218135, year = {2025}, author = {Chu, T and Si, X and Xie, H and Ma, H and Shi, Y and Yao, W and Xing, D and Zhao, F and Dong, F and Gai, Q and Che, K and Guo, Y and Chen, D and Ming, D and Mao, N}, title = {Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles.}, journal = {Biological psychiatry}, volume = {97}, number = {3}, pages = {290-301}, doi = {10.1016/j.biopsych.2024.08.022}, pmid = {39218135}, issn = {1873-2402}, mesh = {Humans ; *Depressive Disorder, Major/genetics/physiopathology ; Female ; Male ; Adult ; *Neurotransmitter Agents/metabolism ; *Brain/physiopathology/diagnostic imaging ; Magnetic Resonance Imaging ; Support Vector Machine ; Middle Aged ; Connectome ; Default Mode Network/physiopathology/diagnostic imaging ; Young Adult ; Nerve Net/physiopathology/diagnostic imaging ; Case-Control Studies ; }, abstract = {BACKGROUND: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.<br><br>METHODS: We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.<br><br>RESULTS: We observed increased regional SC-FC coupling in the default mode network (t337&#xa0;= 3.233) and decreased coupling in the frontoparietal network (t337&#xa0;=&#xa0;-3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve&#xa0;= 0.853), support vector machine (area under the receiver operating characteristic curve&#xa0;= 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.<br><br>CONCLUSIONS: This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.}, }
@article {pmid39216077, year = {2024}, author = {Sun, H and Cai, R and Li, R and Li, M and Gao, L and Li, X}, title = {Conjunctive processing of spatial border and locomotion in retrosplenial cortex during spatial navigation.}, journal = {The Journal of physiology}, volume = {602}, number = {19}, pages = {5017-5038}, doi = {10.1113/JP286434}, pmid = {39216077}, issn = {1469-7793}, support = {32071097//MOST | National Natural Science Foundation of China (NSFC)/ ; 31871056//MOST | National Natural Science Foundation of China (NSFC)/ ; 61703365//MOST | National Natural Science Foundation of China (NSFC)/ ; 91732302//MOST | National Natural Science Foundation of China (NSFC)/ ; 81625006//MOST | National Natural Science Foundation of China (NSFC)/ ; 2018YFC1005003//National Key R&amp;D Program of China/ ; 2019XZZX001-01-20//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2018QN81008//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2020YFB1313500//National Key Research and Development Program of the Ministry of Science and Technology of China/ ; //the MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; }, mesh = {Animals ; *Spatial Navigation/physiology ; *Locomotion/physiology ; Mice ; Male ; Neurons/physiology ; Mice, Inbred C57BL ; Gyrus Cinguli/physiology ; Cerebral Cortex/physiology ; Maze Learning/physiology ; }, abstract = {Spatial information and dynamic locomotor behaviours are equally important for achieving locomotor goals during spatial navigation. However, it remains unclear how spatial and locomotor information is integrated during the processing of self-initiated spatial navigation. Anatomically, the retrosplenial cortex (RSC) has reciprocal connections with brain regions related to spatial processing, including the hippocampus and para-hippocampus, and also receives inputs from the secondary motor cortex. In addition, RSC is functionally associated with allocentric and egocentric spatial targets and head-turning. So, RSC may be a critical region for integrating spatial and locomotor information. In this study, we first examined the role of RSC in spatial navigation using the Morris water maze and found that mice with inactivated RSC took a longer time and distance to reach their destination. Then, by imaging neuronal activity in freely behaving mice within two open fields of different sizes, we identified a large proportion of border cells, head-turning cells and locomotor speed cells in the superficial layer of RSC. Interestingly, some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals. Furthermore, these conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigator scenes using the border, turning and positive-speed conjunctive cells. Our study reveals that the RSC is an important conjunctive brain region that processes spatial and locomotor information during spatial navigation. KEY POINTS: Retrosplenial cortex (RSC) is indispensable during spatial navigation, which was displayed by the longer time and distance of mice to reach their destination after the inactivation of RSC in a water maze. The superficial layer of RSC has a larger population of spatial-related border cells, and locomotion-related head orientation and speed cells; however, it has few place cells in two-dimensional spatial arenas. Some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals, and the conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigation scenes. Our study reveals that the RSC is an important conjunctive brain region that processes both spatial and locomotor information during spatial navigation.}, }
@article {pmid39215126, year = {2024}, author = {Zhao, W and Jiang, X and Zhang, B and Xiao, S and Weng, S}, title = {CTNet: a convolutional transformer network for EEG-based motor imagery classification.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {20237}, pmid = {39215126}, issn = {2045-2322}, support = {3502Z202374054//Xiamen Natural Science Foundation of China/ ; CYKYPT02//Big data technology institute of Chengyi College, Jimei University of China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Brain/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.}, }
@article {pmid39215011, year = {2024}, author = {Egger, J and Kostoglou, K and Müller-Putz, GR}, title = {Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {20247}, pmid = {39215011}, issn = {2045-2322}, support = {101070939//HORIZON EUROPE European Innovation Council/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Gestures ; *Hand/physiology ; Adult ; Young Adult ; Movement/physiology ; Motor Cortex/physiology ; Brain-Computer Interfaces ; }, abstract = {Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.}, }
@article {pmid39214374, year = {2024}, author = {Zheng, Y and Yu, X and Wei, L and Chen, Q and Xu, Y and Ni, P and Deng, W and Guo, W and Hu, X and Qi, X and Li, T}, title = {LT-102, an AMPA receptor potentiator, alleviates depression-like behavior and synaptic plasticity impairments in prefrontal cortex induced by sleep deprivation.}, journal = {Journal of affective disorders}, volume = {367}, number = {}, pages = {18-30}, doi = {10.1016/j.jad.2024.08.176}, pmid = {39214374}, issn = {1573-2517}, mesh = {Animals ; Male ; Mice ; Behavior, Animal/drug effects ; Brain-Derived Neurotrophic Factor/metabolism ; *Depression/drug therapy ; Disease Models, Animal ; Disks Large Homolog 4 Protein/metabolism ; Mice, Inbred C57BL ; *Neuronal Plasticity/drug effects/physiology ; Neurons/drug effects ; *Prefrontal Cortex/metabolism/drug effects ; *Receptors, AMPA/metabolism ; Signal Transduction/drug effects ; *Sleep Deprivation/complications/physiopathology ; }, abstract = {BACKGROUND: Sleep loss is closely related to the onset and development of depression, and the mechanisms involved may include impaired synaptic plasticity. Considering the important role of glutamate α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate receptors (AMPARs) in synaptic plasticity as well as depression, we introduce LT-102, a novel AMPARs potentiator, to evaluate the potential of LT-102 in treating sleep deprivation-induced depression-like behaviors.<br><br>METHODS: We conducted a comprehensive behavioral assessment to evaluate the effects of LT-102 on depression-like symptoms in male C57BL/6J mice. This assessment included the open field test to measure general locomotor activity and anxiety-like behavior, the forced swimming test and tail suspension test to assess despair behaviors indicative of depressive states, and the sucrose preference test to quantify anhedonia, a core symptom of depression. Furthermore, to explore the impact of LT-102 on synaptic plasticity, we utilized a combination of Western blot analysis to detect protein expression levels, Golgi-Cox staining to visualize neuronal morphology, and immunofluorescence to examine the localization of synaptic proteins. Additionally, we utilized primary cortical neurons to delineate the signaling pathway modulated by LT-102.<br><br>RESULTS: Treatment with LT-102 significantly reduced depression-like behaviors associated with sleep deprivation. Quantitative Western blot (WB) analysis revealed a significant increase in GluA1 phosphorylation in the prefrontal cortex (PFC), triggering the Ca[2+]/calmodulin-dependent protein kinase II/cAMP response element-binding protein/brain-derived neurotrophic factor (CaMKII/CREB/BDNF) and forkhead box protein P2/postsynaptic density protein 95 (FoxP2/PSD95) signaling pathways. Immunofluorescence imaging confirmed that LT-102 treatment increased spine density and co-labeling of PSD95 and vesicular glutamate transporter 1 (VGLUT1) in the PFC, reversing the reductions typically observed following sleep deprivation. Golgi staining further validated these results, showing a substantial increase in neuronal dendritic spine density in sleep-deprived mice treated with LT-102. Mechanistically, application of LT-102 to primary cortical neurons, resulted in elevated levels of phosphorylated AKT (p-AKT) and phosphorylated glycogen synthase kinase-3 beta (p-GSK3&#x3b2;), key downstream molecules in the BDNF signaling pathway, which in turn upregulated FoxP2 and PSD95 expression.<br><br>LIMITATIONS: In our study, we chose to exclusively use male mice to eliminate potential influences of the estrous cycle on behavior and physiology. As there is no widely accepted positive drug control for sleep deprivation studies, we did not include one in our research.<br><br>CONCLUSION: Our results suggest that LT-102 is a promising therapeutic agent for counteracting depression-like behaviors and synaptic plasticity deficits induced by sleep deprivation, primarily through the activation of CaMKII/CREB/BDNF and AKT/GSK3&#x3b2;/FoxP2/PSD95 signaling pathways.}, }
@article {pmid39213709, year = {2024}, author = {Si, X and Huang, D and Liang, Z and Sun, Y and Huang, H and Liu, Q and Yang, Z and Ming, D}, title = {Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition.}, journal = {Computers in biology and medicine}, volume = {181}, number = {}, pages = {108973}, doi = {10.1016/j.compbiomed.2024.108973}, pmid = {39213709}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; }, abstract = {Emotion recognition is crucial for human-computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://github.com/ThreePoundUniverse/MACTN.}, }
@article {pmid39213268, year = {2024}, author = {Wang, T and Ke, Y and Huang, Y and He, F and Zhong, W and Liu, S and Ming, D}, title = {Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {12}, pages = {7032-7039}, doi = {10.1109/JBHI.2024.3452410}, pmid = {39213268}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Workload/classification ; *Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; Male ; Adult ; Female ; Young Adult ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.}, }
@article {pmid39213025, year = {2024}, author = {Yan, X and Li, Z and Cao, C and Huang, L and Li, Y and Meng, X and Zhang, B and Yu, M and Huang, T and Chen, J and Li, W and Hao, L and Huang, D and Yi, B and Zhang, M and Zha, S and Yang, H and Yao, J and Qian, P and Leung, CK and Fan, H and Jiang, P and Shui, T}, title = {Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies.}, journal = {Journal of medical Internet research}, volume = {26}, number = {}, pages = {e54874}, pmid = {39213025}, issn = {1438-8871}, mesh = {Humans ; Internet ; Pandemics/prevention &amp; control ; *Social Media/statistics &amp; numerical data ; *Mpox, Monkeypox/epidemiology ; *Infodemic ; }, abstract = {BACKGROUND: The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control.<br><br>OBJECTIVE: This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research.<br><br>METHODS: The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic.<br><br>RESULTS: A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization's declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic-related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic.<br><br>CONCLUSIONS: This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future.}, }
@article {pmid39211094, year = {2024}, author = {Mender, MJ and Ward, AL and Cubillos, LH and Kelberman, MM and Costello, JT and Temmar, H and Wallace, DM and Lin, ET and Lam, JLW and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, PG and Chestek, CA}, title = {Functional Electrical Stimulation and Brain-Machine Interfaces for Simultaneous Control of Wrist and Finger Flexion.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.08.11.607263}, pmid = {39211094}, issn = {2692-8205}, abstract = {Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising treatment to restore hand movements to people with cervical spinal cord injury. Recent intracortical BMIs have shown unprecedented successes in decoding user intentions, however the hand movements restored by FES have largely been limited to predetermined grasps. Restoring dexterous hand movements will require continuous control of many biomechanically linked degrees-of-freedom in the hand, such as wrist and finger flexion, that would form the basis of those movements. Here we investigate the ability to restore simultaneous wrist and finger flexion, which would enable grasping with a controlled hand posture and assist in manipulating objects once grasped. We demonstrate that intramuscular FES can enable monkeys with temporarily paralyzed hands to move their fingers and wrist across a functional range of motion, spanning an average 88.6 degrees at the metacarpophalangeal joint flexion and 71.3 degrees of wrist flexion, and intramuscular FES can control both joints simultaneously in a real-time task. Additionally, we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, even achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. Together, this outlines a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury.}, }
@article {pmid39209118, year = {2024}, author = {Byeon, H and Quraishi, A and Khalaf, MI and Mp, S and Khan, IR and Dutta, AK and Dasari, R and Yellu, RR and Reegu, FA and Bhatt, MW}, title = {Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.}, journal = {SLAS technology}, volume = {29}, number = {5}, pages = {100187}, doi = {10.1016/j.slast.2024.100187}, pmid = {39209118}, issn = {2472-6311}, mesh = {*Fuzzy Logic ; *Electroencephalography/methods ; Humans ; *Machine Learning ; *Brain-Computer Interfaces ; Decision Making ; Signal Processing, Computer-Assisted ; }, abstract = {One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.}, }
@article {pmid39209064, year = {2024}, author = {Zhang, E and Shotbolt, M and Chang, CY and Scott-Vandeusen, A and Chen, S and Liang, P and Radu, D and Khizroev, S}, title = {Controlling action potentials with magnetoelectric nanoparticles.}, journal = {Brain stimulation}, volume = {17}, number = {5}, pages = {1005-1017}, doi = {10.1016/j.brs.2024.08.008}, pmid = {39209064}, issn = {1876-4754}, mesh = {Animals ; Rats ; *Action Potentials/physiology ; Rats, Sprague-Dawley ; Nanoparticles ; Magnetic Fields ; Neurons/physiology ; }, abstract = {Non-invasive or minutely invasive and wireless brain stimulation that can target any region of the brain is an open problem in engineering and neuroscience with serious implications for the treatment of numerous neurological diseases. Despite significant recent progress in advancing new methods of neuromodulation, none has successfully replicated the efficacy of traditional wired stimulation and improved on its downsides without introducing new complications. Due to the capability to convert magnetic fields into local electric fields, MagnetoElectric NanoParticle (MENP) neuromodulation is a recently proposed framework based on new materials that can locally sensitize neurons to specific, low-strength alternating current (AC) magnetic fields (50Hz 1.7 kOe field). However, the current research into this neuromodulation concept is at a very early stage, and the theoretically feasible game-changing advantages remain to be proven experimentally. To break this stalemate phase, this study leveraged understanding of the non-linear properties of MENPs and the nanoparticles' field interaction with the cellular microenvironment. Particularly, the applied magnetic field's strength and frequency were tailored to the M - H hysteresis loop of the nanoparticles. Furthermore, rectangular prisms instead of the more traditional "spherical" nanoparticle shapes were used to: (i) maximize the magnetoelectric effect and (ii) improve the nanoparticle-cell-membrane surface interface. Neuromodulation performance was evaluated in a series of exploratory in vitro experiments on 2446 rat hippocampus neurons. Linear mixed effect models were used to ensure the independence of samples by accounting for fixed adjacency effects in synchronized firing. Neural activity was measured over repeated 4-min segments, containing 90 s of baseline measurements, 90 s of stimulation measurements, and 60 s of post stimulation measurements. 87.5 % of stimulation attempts produced statistically significant (P < 0.05) changes in neural activity, with 58.3 % producing large changes (P < 0.01). In negative controls using either zero or 1.7 kOe-strength field without nanoparticles, no experiments produced significant changes in neural activity (P > 0.05 and P > 0.15 respectively). Furthermore, an exploratory analysis of a direct current (DC) magnetic field indicated that the DC field could be used with MENPs to inhibit neuron activity (P < 0.01). These experiments demonstrated the potential for magnetoelectric neuromodulation to offer a near one-to-one functionality match with conventional electrode stimulation without requiring surgical intervention or genetic modification to achieve success, instead relying on physical properties of these nanoparticles as "On/Off" control mechanisms. ONE-SENTENCE SUMMARY: This in vitro neural cell culture study explores how to exploit the non-linear and anisotropic properties of magnetoelectric nanoparticles for wireless neuromodulation, the importance of magnetic field strength and frequency matching for optimization, and demonstrates, for the first time, that magnetoelectric neuromodulation can inhibit neural responses.}, }
@article {pmid39208037, year = {2024}, author = {Liao, C and Zhao, S and Zhang, J}, title = {Motor Imagery Recognition Based on GMM-JCSFE Model.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3348-3357}, doi = {10.1109/TNSRE.2024.3451716}, pmid = {39208037}, issn = {1558-0210}, mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Algorithms ; Normal Distribution ; Brain-Computer Interfaces ; Reproducibility of Results ; Male ; Female ; }, abstract = {Features from EEG microstate models, such as time-domain statistical features and state transition probabilities, are typically manually selected based on experience. However, traditional microstate models assume abrupt transitions between states, and the classification features can vary among individuals due to personal differences. To date, both empirical and theoretical classification results of EEG microstate features have not been entirely satisfactory. Here, we introduce an enhanced feature extraction method that combines Joint label-Common and label-Specific Feature Exploration (JCSFE) with Gaussian Mixture Models (GMM) to explore microstate features. First, GMMs are employed to represent the smooth transitions of EEG spatiotemporal features within microstate models. Second, category-common and category-specific features are identified by applying regularization constraints to linear classifiers. Third, a graph regularizer is used to extract subject-invariant microstate features. Experimental results on publicly available datasets demonstrate that the proposed model effectively encodes microstate features and improves the accuracy of motor imagery recognition across subjects. The primary code is accessible for download from the website: https://github.com/liaoliao3450/GMM-JCSFE.}, }
@article {pmid39207622, year = {2025}, author = {Feng, J and Wang, X and Pan, M and Li, CX and Zhang, Z and Sun, M and Liao, T and Wang, Z and Luo, J and Shi, L and Chen, YJ and Li, HF and Xu, J}, title = {The Medial Prefrontal Cortex-Basolateral Amygdala Circuit Mediates Anxiety in Shank3 InsG3680 Knock-in Mice.}, journal = {Neuroscience bulletin}, volume = {41}, number = {1}, pages = {77-92}, pmid = {39207622}, issn = {1995-8218}, mesh = {Animals ; *Prefrontal Cortex/physiopathology/metabolism ; *Basolateral Nuclear Complex/physiopathology/metabolism ; Mice ; *Anxiety/physiopathology/genetics/metabolism ; *Nerve Tissue Proteins/genetics ; Male ; Gene Knock-In Techniques ; Pyramidal Cells/physiology ; Mice, Transgenic ; Neural Pathways/physiopathology ; Mice, Inbred C57BL ; Microfilament Proteins ; }, abstract = {Anxiety disorder is a major symptom of autism spectrum disorder (ASD) with a comorbidity rate of ~40%. However, the neural mechanisms of the emergence of anxiety in ASD remain unclear. In our study, we found that hyperactivity of basolateral amygdala (BLA) pyramidal neurons (PNs) in Shank3 InsG3680 knock-in (InsG3680[+/+]) mice is involved in the development of anxiety. Electrophysiological results also showed increased excitatory input and decreased inhibitory input in BLA PNs. Chemogenetic inhibition of the excitability of PNs in the BLA rescued the anxiety phenotype of InsG3680[+/+] mice. Further study found that the diminished control of the BLA by medial prefrontal cortex (mPFC) and optogenetic activation of the mPFC-BLA pathway also had a rescue effect, which increased the feedforward inhibition of the BLA. Taken together, our results suggest that hyperactivity of the BLA and alteration of the mPFC-BLA circuitry are involved in anxiety in InsG3680[+/+] mice.}, }
@article {pmid39207066, year = {2024}, author = {Fan, C and Yang, B and Li, X and Gao, S and Zan, P}, title = {EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {8}, pages = {153}, doi = {10.31083/j.jin2308153}, pmid = {39207066}, issn = {0219-6352}, support = {2022YFC3602500//National Key Research and Development Program of China/ ; 2022YFC3602504//National Key Research and Development Program of China/ ; 62376149//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Adult ; Motor Activity/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is vital due to the variability among individuals and temporal states.<br><br>METHODS: This study introduces a novel network architecture, 3D-convolutional neural network-generative adversarial network (3D-CNN-GAN), for decoding both within-session and cross-session motor imagery. Initially, EEG signals were extracted over various time intervals using a sliding window technique, capturing temporal, frequency, and phase features to construct a temporal-frequency-phase feature (TFPF) three-dimensional feature map. Generative adversarial networks (GANs) were then employed to synthesize artificial data, which, when combined with the original datasets, expanded the data capacity and enhanced functional connectivity. Moreover, GANs proved capable of learning and amplifying the brain connectivity patterns present in the existing data, generating more distinctive brain network features. A compact, two-layer 3D-CNN model was subsequently developed to efficiently decode these TFPF features.<br><br>RESULTS: Taking into account session and individual differences in EEG data, tests were conducted on both the public GigaDB dataset and the SHU laboratory dataset. On the GigaDB dataset, our 3D-CNN and 3D-CNN-GAN models achieved two-class within-session motor imagery accuracies of 76.49% and 77.03%, respectively, demonstrating the algorithm's effectiveness and the improvement provided by data augmentation. Furthermore, on the SHU dataset, the 3D-CNN and 3D-CNN-GAN models yielded two-class within-session motor imagery accuracies of 67.64% and 71.63%, and cross-session motor imagery accuracies of 58.06% and 63.04%, respectively.<br><br>CONCLUSIONS: The 3D-CNN-GAN algorithm significantly enhances the generalizability of EEG-based motor imagery brain-computer interfaces (BCIs). Additionally, this research offers valuable insights into the potential applications of motor imagery BCIs.}, }
@article {pmid39205122, year = {2024}, author = {Moraes, CPA and Dos Santos, LH and Fantinato, DG and Neves, A and Adali, T}, title = {Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {16}, pages = {}, pmid = {39205122}, issn = {1424-8220}, support = {2023/00640-1//S&#xe3;o Paulo Research Foundation (FAPESP)/ ; 88887.595656/2020-00//Coordena&#xe7;&#xe3;o de Aperfei&#xe7;oamento de Pessoal de N&#xed;vel Superior (CAPES)/ ; 2316420//US National Science Foundation (NSF)/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Movement/physiology ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.}, }
@article {pmid39204948, year = {2024}, author = {Dillen, A and Omidi, M and Ghaffari, F and Romain, O and Vanderborght, B and Roelands, B and Nowé, A and De Pauw, K}, title = {User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {16}, pages = {}, pmid = {39204948}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Electroencephalography/methods ; Male ; *Eye-Tracking Technology ; *Augmented Reality ; Female ; Adult ; *User-Computer Interface ; Middle Aged ; Young Adult ; Eye Movements/physiology ; }, abstract = {This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.}, }
@article {pmid39204910, year = {2024}, author = {Hu, W and Ji, B and Gao, K}, title = {A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {16}, pages = {}, pmid = {39204910}, issn = {1424-8220}, support = {62106041, 62204204//the National Natural Science Foundation of China/ ; 223202100019//the Fundamental Research Funds for the Central Universities/ ; 2022ZD0208601//Science and Technology Innovation 2030-Major Project/ ; 21YF1451000//Shanghai Sailing Program/ ; }, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; Electrodes ; Deep Learning ; }, abstract = {The precision of electroencephalograms (EEGs) significantly impacts the performance of brain-computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals.}, }
@article {pmid39204903, year = {2024}, author = {Mattei, E and Lozzi, D and Di Matteo, A and Cipriani, A and Manes, C and Placidi, G}, title = {MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {16}, pages = {}, pmid = {39204903}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand/physiology ; *Movement/physiology ; Male ; Adult ; Biomechanical Phenomena/physiology ; Female ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.}, }
@article {pmid39204507, year = {2024}, author = {Boratto, MH and Graeff, CFO and Han, S}, title = {Highly Stable Flexible Organic Electrochemical Transistors with Natural Rubber Latex Additives.}, journal = {Polymers}, volume = {16}, number = {16}, pages = {}, pmid = {39204507}, issn = {2073-4360}, support = {2024-0029//Incheon National University/ ; }, abstract = {Organic electrochemical transistors (OECTs) have attracted considerable interest in the context of wearable and implantable biosensors due to their remarkable signal amplification combined with seamless integration into biological systems. These properties underlie OECTs' potential utility across a range of bioelectronic applications. One of the main challenges to their practical applications is the mechanical limitation of PEDOT:PSS, the most typical conductive polymer used as a channel layer, when the OECTs are applied to implantable and stretchable bioelectronics. In this work, we address this critical issue by employing natural rubber latex (NRL) as an additive in PEDOT:PSS to improve flexibility and stretchability of the OECT channels. Although the inclusion of NRL leads to a decrease in transconductance, mainly due to a reduced carrier mobility from 0.3 to 0.1 cm[2]/V·s, the OECTs maintain satisfactory transconductance, exceeding 5 mS. Furthermore, it is demonstrated that the OECTs exhibit excellent mechanical stability while maintaining their performance even after 100 repetitive bending cycles. This work, therefore, suggests that the NRL/PEDOT:PSS composite film can be deployed for wearable/implantable applications, where high mechanical stability is needed. This finding opens up new avenues for practical use of OECTs in more robust and versatile wearable and implantable biosensors.}, }
@article {pmid39202770, year = {2024}, author = {Balčiauskas, L and Balčiauskienė, L}, title = {Extreme Body Condition Index Values in Small Mammals.}, journal = {Life (Basel, Switzerland)}, volume = {14}, number = {8}, pages = {}, pmid = {39202770}, issn = {2075-1729}, abstract = {The body condition index (BCI) values in small mammals are important in understanding their survival and reproduction. The upper values could be related to the Chitty effect (presence of very heavy individuals), while the minimum ones are little known. In this study, we analyzed extremes of BCI in 12 small mammal species, snap-trapped in Lithuania between 1980 and 2023, with respect to species, animal age, sex, and participation in reproduction. The proportion of small mammals with extreme body condition indices was negligible (1.33% with a BCI < 2 and 0.52% with a BCI > 5) when considering the total number of individuals processed (n = 27,073). When compared to the expected proportions, insectivores and herbivores were overrepresented, while granivores and omnivores were underrepresented among underfit animals. The proportions of granivores and insectivores were higher, while those of omnivores and herbivores were lower than expected in overfit animals. In several species, the proportions of age groups in underfit and overfit individuals differed from that expected. The male-female ratio was not expressed, with the exception of Sorex araneus. The highest proportion of overfit and absence of underfit individuals was found in Micromys minutus. The observation that individuals with the highest body mass are not among those with the highest BCI contributes to the interpretation of the Chitty effect. For the first time in mid-latitudes, we report individuals of very high body mass in three shrew species.}, }
@article {pmid39199779, year = {2024}, author = {Tang, F and Yan, F and Zhong, Y and Li, J and Gong, H and Li, X}, title = {Optogenetic Brain-Computer Interfaces.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {8}, pages = {}, pmid = {39199779}, issn = {2306-5354}, support = {Grant No.821RC531//Hainan Provincial Natural Science Foundation of China for High-level Talents/ ; 2022ZD0205201//STI2030-Major Projects/ ; No. T2122015//National Nature Science Foundation of China/ ; No.2019-I2M-5-014//CAMS Innovation Fund for Medical Sciences/ ; }, abstract = {The brain-computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.}, }
@article {pmid39199740, year = {2024}, author = {Fernandes, JVMR and Alexandria, AR and Marques, JAL and Assis, DF and Motta, PC and Silva, BRDS}, title = {Emotion Detection from EEG Signals Using Machine Deep Learning Models.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {8}, pages = {}, pmid = {39199740}, issn = {2306-5354}, abstract = {Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.}, }
@article {pmid39199739, year = {2024}, author = {Chiou, N and Günal, M and Koyejo, S and Perpetuini, D and Chiarelli, AM and Low, KA and Fabiani, M and Gratton, G}, title = {Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {8}, pages = {}, pmid = {39199739}, issn = {2306-5354}, abstract = {Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.}, }
@article {pmid39199537, year = {2024}, author = {Fodor, MA and Herschel, H and Cantürk, A and Heisenberg, G and Volosyak, I}, title = {Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).}, journal = {Brain sciences}, volume = {14}, number = {8}, pages = {}, pmid = {39199537}, issn = {2076-3425}, support = {101118964//European Union/ ; }, abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.}, }
@article {pmid39199527, year = {2024}, author = {Ail, BE and Ramele, R and Gambini, J and Santos, JM}, title = {An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.}, journal = {Brain sciences}, volume = {14}, number = {8}, pages = {}, pmid = {39199527}, issn = {2076-3425}, support = {ITBACyT-2020//Instituto Tecnol&#xf3;gico de Buenos Aires (ITBA)/ ; }, abstract = {This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).}, }
@article {pmid39196743, year = {2024}, author = {El Ouahidi, Y and Gripon, V and Pasdeloup, B and Bouallegue, G and Farrugia, N and Lioi, G}, title = {A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3338-3347}, doi = {10.1109/TNSRE.2024.3451010}, pmid = {39196743}, issn = {1558-0210}, mesh = {Humans ; *Deep Learning ; *Brain-Computer Interfaces ; *Electroencephalography ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Male ; Adult ; Female ; Machine Learning ; Movement/physiology ; }, abstract = {We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, batch normalisations, ReLU activation functions and pooling functions. EEG-SimpleConv architecture is accompanied by a straightforward and tailored training routine, which is subjected to an extensive ablation study to quantify the influence of its components. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We believe that using standard components and ingredients can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.}, }
@article {pmid39196738, year = {2024}, author = {Li, Z and Zhang, R and Li, W and Li, M and Chen, X and Cui, H}, title = {Enhancement of Hybrid BCI System Performance Based on Motor Imagery and SSVEP by Transcranial Alternating Current Stimulation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3222-3230}, doi = {10.1109/TNSRE.2024.3451015}, pmid = {39196738}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Algorithms ; Female ; *Transcranial Direct Current Stimulation/methods ; *Electroencephalography ; Adult ; Young Adult ; Healthy Volunteers ; Psychomotor Performance/physiology ; Hand/physiology ; Reproducibility of Results ; Evoked Potentials, Visual/physiology ; Evoked Potentials, Motor/physiology ; }, abstract = {The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.}, }
@article {pmid39196465, year = {2024}, author = {Zou, Y}, title = {Genetic enhancement from the perspective of transhumanism: exploring a new paradigm of transhuman evolution.}, journal = {Medicine, health care, and philosophy}, volume = {27}, number = {4}, pages = {529-544}, pmid = {39196465}, issn = {1572-8633}, support = {2023EZX004//Shanghai Office of Philosophy and Social Science/ ; 23FZXA012//National Social Science Fund of China/ ; }, mesh = {Humans ; *Biological Evolution ; *Genetic Enhancement/ethics ; Philosophy, Medical ; Artificial Intelligence ; Humanism ; }, abstract = {Transhumanism is a movement that advocates for the enhancement of human capabilities through the use of advanced technologies such as genetic enhancement. This article explores the definition, history, and development of transhumanism. Then, it compares the stance on genetic enhancement from the perspectives of bio-conservatism, bio-liberalism, and transhumanism. This article posits that transhuman evolution has twofold implications, allowing for the integration of transhumanist research and evolutionary biology. First, it offers a compelling scientific framework for understanding genetic enhancement, avoiding technological progressivism, and incorporating concepts of evolutionary biology. Second, it represents a new evolutionary paradigm distinct from traditional Lamarckism and Darwinism. It marks the third synthesis of evolutionary biology, offering fresh perspectives on established concepts such as artificial selection and gene-culture co-evolution. In recent decades, human enhancement has captivated not only evolutionary biologists, neurobiologists, psychologists, and philosophers, but also those in fields such as cybernetics and artificial intelligence. In addition to genetic enhancement, other human enhancement technologies, including brain-computer interfaces and brain uploading, are currently under development, which the paradigm of transhuman evolution can better integrate into its framework.}, }
@article {pmid39194652, year = {2024}, author = {Jiang, Q and Liu, M}, title = {Recent Progress in Artificial Neurons for Neuromodulation.}, journal = {Journal of functional biomaterials}, volume = {15}, number = {8}, pages = {}, pmid = {39194652}, issn = {2079-4983}, abstract = {Driven by the rapid advancement and practical implementation of biomaterials, fabrication technologies, and artificial intelligence, artificial neuron devices and systems have emerged as a promising technology for interpreting and transmitting neurological signals. These systems are equipped with multi-modal bio-integrable sensing capabilities, and can facilitate the benefits of neurological monitoring and modulation through accurate physiological recognition. In this article, we provide an overview of recent progress in artificial neuron technology, with a particular focus on the high-tech applications made possible by innovations in material engineering, new designs and technologies, and potential application areas. As a rapidly expanding field, these advancements have a promising potential to revolutionize personalized healthcare, human enhancement, and a wide range of other applications, making artificial neuron devices the future of brain-machine interfaces.}, }
@article {pmid39194625, year = {2024}, author = {Ullah, A and Zhang, F and Song, Z and Wang, Y and Zhao, S and Riaz, W and Li, G}, title = {Surface Electromyography-Based Recognition of Electronic Taste Sensations.}, journal = {Biosensors}, volume = {14}, number = {8}, pages = {}, pmid = {39194625}, issn = {2079-6374}, mesh = {Humans ; *Electromyography ; *Taste ; Brain-Computer Interfaces ; Adult ; }, abstract = {Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.}, }
@article {pmid39194597, year = {2024}, author = {Savić, AM and Novičić, M and Miler-Jerković, V and Djordjević, O and Konstantinović, L}, title = {Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients.}, journal = {Biosensors}, volume = {14}, number = {8}, pages = {}, pmid = {39194597}, issn = {2079-6374}, support = {6066223//Science Fund of the Republic of Serbia/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke/physiopathology ; *Electroencephalography ; Female ; Middle Aged ; Aged ; Feasibility Studies ; Evoked Potentials, Somatosensory/physiology ; Stroke Rehabilitation/methods ; Adult ; Touch ; }, abstract = {This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.}, }
@article {pmid39194438, year = {2024}, author = {Omari, S and Omari, A and Abu-Dakka, F and Abderrahim, M}, title = {EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {8}, pages = {}, pmid = {39194438}, issn = {2313-7673}, support = {2012/00605/003//Universidad Carlos III de Madrid/ ; S2018/NMT-4331//RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub/ ; KK-2022/00024 and HELDU KK-2023/00055//ELKARTEK, Basque Government (Spain)/ ; }, abstract = {Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain-computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.}, }
@article {pmid39194182, year = {2024}, author = {Rabbani, Q and Shah, S and Milsap, G and Fifer, M and Hermansky, H and Crone, N}, title = {Iterative alignment discovery of speech-associated neural activity.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {39194182}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Algorithms ; *Electrocorticography/methods ; Male ; Female ; Adult ; Neural Networks, Computer ; }, abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.}, }
@article {pmid39193830, year = {2024}, author = {Xu, Q and Xi, Y and Wang, L and Du, Z and Xu, M and Ruan, T and Cao, J and Zheng, K and Wang, X and Yang, B and Liu, J}, title = {An Opto-electrophysiology Neural Probe with Photoelectric Artifact-Free for Advanced Single-Neuron Analysis.}, journal = {ACS nano}, volume = {18}, number = {36}, pages = {25193-25204}, doi = {10.1021/acsnano.4c07379}, pmid = {39193830}, issn = {1936-086X}, mesh = {*Neurons/physiology ; *Artifacts ; Brain-Computer Interfaces ; Animals ; Single-Cell Analysis/instrumentation ; Humans ; }, abstract = {Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.}, }
@article {pmid39191641, year = {2025}, author = {Al-Khouja, F and Grigorian, A and Emigh, B and Schellenberg, M and Diaz, G and Duncan, TK and Tuli, R and Coimbra, R and Gilbert-Gard, K and Johnson, A and Marty, M and Jebbia, M and Obaid-Schmid, AK and Fierro, N and Ley, E and Bayat, D and Biffl, W and Ebrahimian, S and Tillou M, A and Tay-Lasso, E and Alvarez, C and Nahmias, J}, title = {24-hour Telemetry Monitoring May Not be Necessary for Patients With an Isolated Sternal Fracture and Minor ECG Abnormalities or Troponin Elevation: A Southern California Multicenter Study.}, journal = {The American surgeon}, volume = {91}, number = {1}, pages = {126-132}, doi = {10.1177/00031348241278904}, pmid = {39191641}, issn = {1555-9823}, mesh = {Humans ; Male ; Female ; Retrospective Studies ; Middle Aged ; *Electrocardiography ; *Fractures, Bone/complications ; *Telemetry ; *Sternum/injuries ; California ; *Troponin/blood ; Adult ; Aged ; Wounds, Nonpenetrating/complications/diagnosis/blood ; Heart Injuries/diagnosis/blood/complications/etiology ; Monitoring, Physiologic/methods ; }, abstract = {BACKGROUND: Current guidelines recommend 24-hour telemetry monitoring for isolated sternal fractures (ISFs) with electrocardiogram (ECG) abnormalities or troponin elevation. However, a single-center study suggested ISF patients with minor ECG abnormalities (sinus tachycardia/bradycardia, nonspecific arrhythmia/ST-changes, and bundle branch block) may not require 24-hour telemetry monitoring. This study sought to corroborate this, hypothesizing ISF patients would not develop blunt cardiac injury (BCI).<br><br>MATERIALS &amp; METHODS: A retrospective study was performed at 8 trauma centers (1/2018-8/2020). Patients with ISF (abbreviated injury scale <2 for the head/neck/face/abdomen/extremities) and minor ECG abnormalities or troponin elevations were included. Patients with multiple rib fractures or hemothorax/pneumothorax were excluded. The primary outcome was an echocardiogram confirmed BCI. The secondary outcome was significant BCI defined as cardiogenic shock, dysrhythmia requiring treatment, post-traumatic cardiac structural defects, unexplained hypotension, or cardiac-related procedures. Descriptive statistics were performed.<br><br>RESULTS: Of 124 ISF patients with minor ECG abnormalities or troponin elevation, 90% were admitted with a mean stay of 35 hours. Echocardiogram was performed for 31.5% of patients, 10 (25.6%) of which had abnormalities. However, no patient had BCI diagnosed on echocardiography. In total, 2 patients (1.6%) had a significant BCI (atrial fibrillation and supraventricular tachycardia at 10 and 82&#xa0;hours after injury). No patient died.<br><br>CONCLUSIONS: Following ISF with minor ECG changes or troponin elevation, <2% suffered significant BCI, and none had an echocardiogram diagnosed BCI, despite >30% receiving echocardiogram. These findings challenge the dogma of mandatory observation periods following ISF with associated ECG abnormalities and support the lack of utility for routine echocardiography in these patients.}, }
@article {pmid39190517, year = {2024}, author = {Liu, K and Yang, T and Yu, Z and Yi, W and Yu, H and Wang, G and Wu, W}, title = {MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {12}, pages = {7126-7137}, doi = {10.1109/JBHI.2024.3450753}, pmid = {39190517}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Brain-Computer Interfaces ; Adult ; Algorithms ; Male ; Young Adult ; Female ; }, abstract = {OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms.<br><br>METHODS: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers.<br><br>RESULTS: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i.e., the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses.<br><br>CONCLUSION: MSVTNet shows superiority and robustness in enhancing MI decoding performance.}, }
@article {pmid39190511, year = {2024}, author = {Zhou, J and Duan, Y and Chang, YC and Wang, YK and Lin, CT}, title = {BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3278-3288}, doi = {10.1109/TNSRE.2024.3450795}, pmid = {39190511}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Natural Language Processing ; Language ; Semantics ; Supervised Machine Learning ; Male ; Female ; Adult ; Reproducibility of Results ; }, abstract = {Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.}, }
@article {pmid39188923, year = {2024}, author = {Schwarck, S and Voelkle, MC and Becke, A and Busse, N and Glanz, W and Düzel, E and Ziegler, G}, title = {Interplay of physical and recognition performance using hierarchical continuous-time dynamic modeling and a dual-task training regime in Alzheimer's patients.}, journal = {Alzheimer's &amp; dementia (Amsterdam, Netherlands)}, volume = {16}, number = {3}, pages = {e12629}, pmid = {39188923}, issn = {2352-8729}, abstract = {UNLABELLED: Training studies typically investigate the cumulative rather than the analytically challenging immediate effect of exercise on cognitive outcomes. We investigated the dynamic interplay between single-session exercise intensity and time-locked recognition speed-accuracy scores in older adults with Alzheimer's dementia (N = 17) undergoing a 24-week dual-task regime. We specified a state-of-the-art hierarchical Bayesian continuous-time dynamic model with fully connected state variables to analyze the bi-directional effects between physical and recognition scores over time. Higher physical performance was dynamically linked to improved recognition (-1.335, SD = 0.201, 95% Bayesian credible interval [BCI] [-1.725, -0.954]). The effect was short-term, lasting up to 5 days (-0.368, SD = 0.05, 95% BCI [-0.479, -0.266]). Clinical scores supported the validity of the model and observed temporal dynamics. Higher physical performance predicted improved recognition speed accuracy in a day-by-day manner, providing a proof-of-concept for the feasibility of linking exercise training and recognition in patients with Alzheimer's dementia.<br><br>HIGHLIGHTS: Hierarchical Bayesian continuous-time dynamic modeling approachA total of 72 repeated physical exercise (PP) and integrated recognition speed-accuracy (IRSA) measurementsPP is dynamically linked to session-to-session variability of IRSAHigher PP improved IRSA in subsequent sessions in subjects with Alzheimer's dementiaShort-term effect: lasting up to 4 days after training session.}, }
@article {pmid39187733, year = {2024}, author = {Gu, J and Shao, W and Liu, L and Wang, Y and Yang, Y and Zhang, Z and Wu, Y and Xu, Q and Gu, L and Zhang, Y and Shen, Y and Zhao, H and Zeng, C and Zhang, H}, title = {Challenges and future directions of SUDEP models.}, journal = {Lab animal}, volume = {53}, number = {9}, pages = {226-243}, pmid = {39187733}, issn = {1548-4475}, support = {81771403//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81974205//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001379//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ20H090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, mesh = {Animals ; *Disease Models, Animal ; *Sudden Unexpected Death in Epilepsy/prevention &amp; control ; Humans ; Epilepsy/physiopathology ; Artificial Intelligence ; }, abstract = {Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among patients with epilepsy, causing a global public health burden. The underlying mechanisms of SUDEP remain elusive, and effective prevention or treatment strategies require further investigation. A major challenge in current SUDEP research is the lack of an ideal model that maximally mimics the human condition. Animal models are important for revealing the potential pathogenesis of SUDEP and preventing its occurrence; however, they have potential limitations due to species differences that prevent them from precisely replicating the intricate physiological and pathological processes of human disease. This Review provides a comprehensive overview of several available SUDEP animal models, highlighting their pros and cons. More importantly, we further propose the establishment of an ideal model based on brain-computer interfaces and artificial intelligence, hoping to offer new insights into potential advancements in SUDEP research. In doing so, we hope to provide valuable information for SUDEP researchers, offer new insights into the pathogenesis of SUDEP and open new avenues for the development of strategies to prevent SUDEP.}, }
@article {pmid39187218, year = {2024}, author = {Ciaramidaro, A and Toppi, J and Vogel, P and Freitag, CM and Siniatchkin, M and Astolfi, L}, title = {Synergy of the mirror neuron system and the mentalizing system in a single brain and between brains during joint actions.}, journal = {NeuroImage}, volume = {299}, number = {}, pages = {120783}, doi = {10.1016/j.neuroimage.2024.120783}, pmid = {39187218}, issn = {1095-9572}, mesh = {Humans ; *Mirror Neurons/physiology ; Male ; Female ; *Electroencephalography ; Adult ; Young Adult ; *Theory of Mind/physiology ; *Brain/physiology ; Cooperative Behavior ; Mentalization/physiology ; Social Interaction ; }, abstract = {Cooperative action involves the simulation of actions and their co-representation by two or more people. This requires the involvement of two complex brain systems: the mirror neuron system (MNS) and the mentalizing system (MENT), both of critical importance for successful social interaction. However, their internal organization and the potential synergy of both systems during joint actions (JA) are yet to be determined. The aim of this study was to examine the role and interaction of these two fundamental systems-MENT and MNS-during continuous interaction. To this hand, we conducted a multiple-brain connectivity analysis in the source domain during a motor cooperation task using high-density EEG dual-recordings providing relevant insights into the roles of MNS and MENT at the intra- and interbrain levels. In particular, the intra-brain analysis demonstrated the essential function of both systems during JA, as well as the crucial role played by single brain regions of both neural mechanisms during cooperative activities. Specifically, our intra-brain analysis revealed that both neural mechanisms are essential during Joint Action (JA), showing a solid connection between MNS and MENT and a central role of the single brain regions of both mechanisms during cooperative actions. Additionally, our inter-brain study revealed increased inter-subject connections involving the motor system, MENT and MNS. Thus, our findings show a mutual influence between two interacting agents, based on synchronization of MNS and MENT systems. Our results actually encourage more research into the still-largely unknown realm of inter-brain dynamics and contribute to expand the body of knowledge in social neuroscience.}, }
@article {pmid39186838, year = {2024}, author = {Qiu, L and Zhong, L and Li, J and Feng, W and Zhou, C and Pan, J}, title = {SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {180}, number = {}, pages = {106643}, doi = {10.1016/j.neunet.2024.106643}, pmid = {39186838}, issn = {1879-2782}, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Consciousness/physiology ; Supervised Machine Learning ; Attention/physiology ; Adult ; Male ; Female ; }, abstract = {Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT). First, we model multi-channel EEG signals by constructing a graph structure that dynamically captures the spatiotemporal topological features of EEG signals. Second, we employ a self-supervised graph attention neural network to facilitate model training, mitigating the impact of signal noise on the model. Finally, a semi-supervised approach is used to fine-tune the model, enhancing its generalization ability in cross-subject classification. By combining supervised and unsupervised learning techniques, the SFT-SGAT maximizes the utility of limited labeled data in EEG emotion recognition tasks, thereby enhancing the model's performance. Experiments based on leave-one-subject-out cross-validation demonstrate that SFT-SGAT achieves state-of-the-art cross-subject emotion recognition performance on the SEED and SEED-IV datasets, with accuracies of 92.04% and 82.76%, respectively. Furthermore, experiments conducted on a self-collected dataset comprising ten healthy subjects and eight patients with disorders of consciousness (DOCs) revealed that the SFT-SGAT attains high classification performance in healthy subjects (maximum accuracy of 95.84%) and was successfully applied to DOC patients, with four patients achieving emotion recognition accuracies exceeding 60%. The experiments demonstrate the effectiveness of the proposed SFT-SGAT model in cross-subject EEG emotion recognition and its potential for assessing levels of consciousness in patients with DOC.}, }
@article {pmid39186407, year = {2025}, author = {Edelman, BJ and Zhang, S and Schalk, G and Brunner, P and Muller-Putz, G and Guan, C and He, B}, title = {Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.}, journal = {IEEE reviews in biomedical engineering}, volume = {18}, number = {}, pages = {26-49}, pmid = {39186407}, issn = {1941-1189}, support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography/methods ; *Brain/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.}, }
@article {pmid39185373, year = {2024}, author = {Yi, D and Yao, Y and Wang, Y and Chen, L}, title = {Design, Fabrication, and Implantation of Invasive Microelectrode Arrays as in vivo Brain Machine Interfaces: A Comprehensive Review.}, journal = {Journal of manufacturing processes}, volume = {126}, number = {}, pages = {185-207}, pmid = {39185373}, issn = {1526-6125}, support = {R15 NS133861/NS/NINDS NIH HHS/United States ; }, abstract = {Invasive Microelectrode Arrays (MEAs) have been a significant and useful tool for us to gain a fundamental understanding of how the brain works through high spatiotemporal resolution neuron-level recordings and/or stimulations. Through decades of research, various types of microwire, silicon, and flexible substrate-based MEAs have been developed using the evolving new materials, novel design concepts, and cutting-edge advanced manufacturing capabilities. Surgical implantation of the latest minimal damaging flexible MEAs through the hard-to-penetrate brain membranes introduces new challenges and thus the development of implantation strategies and instruments for the latest MEAs. In this paper, studies on the design considerations and enabling manufacturing processes of various invasive MEAs as in vivo brain-machine interfaces have been reviewed to facilitate the development as well as the state-of-art of such brain-machine interfaces from an engineering perspective. The challenges and solution strategies developed for surgically implanting such interfaces into the brain have also been evaluated and summarized. Finally, the research gaps have been identified in the design, manufacturing, and implantation perspectives, and future research prospects in invasive MEA development have been proposed.}, }
@article {pmid39185208, year = {2024}, author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP}, title = {A posture subspace in primary motor cortex.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39185208}, issn = {2692-8205}, support = {T32 GM081760/GM/NIGMS NIH HHS/United States ; R01 EY035896/EY/NEI NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; RF1 NS127107/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; }, abstract = {To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture information in M1 than previously recognized. The compartmentalization of posture and goal information might allow the two to be flexibly combined in the service of our broad repertoire of actions.}, }
@article {pmid39185198, year = {2024}, author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL}, title = {Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39185198}, issn = {2692-8205}, support = {K12 HD073945/HD/NICHD NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; }, abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (μECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm[2] area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate μECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.}, }
@article {pmid39184953, year = {2024}, author = {Raghuram, V and Datye, AD and Fried, SI and Timko, BP}, title = {Transparent and Conformal Microcoil Arrays for Spatially Selective Neuronal Activation.}, journal = {Device}, volume = {2}, number = {4}, pages = {}, pmid = {39184953}, issn = {2666-9986}, support = {23TPA1057212/AHA/American Heart Association-American Stroke Association/United States ; R01 NS110575/NS/NINDS NIH HHS/United States ; R21 EB034527/EB/NIBIB NIH HHS/United States ; }, abstract = {Micromagnetic stimulation (μMS) using small, implantable microcoils is a promising method for achieving neuronal activation with high spatial resolution and low toxicity. Herein, we report a microcoil array for localized activation of cortical neurons and retinal ganglion cells. We developed a computational model to relate the electric field gradient (activating function) to the geometry and arrangement of microcoils, and selected a design that produced an anisotropic region of activation <50 μm wide. The device was comprised of an SU-8/Cu/SU-8 tri-layer structure, which was flexible, transparent and conformal and featured four individually-addressable microcoils. Interfaced with cortex or retina explants from GCaMP6-expressing mice, we observed that individual neurons localized within 40 μm of a microcoil tip could be activated repeatedly and in a dose- (power-) dependent fashion. These results demonstrate the potential of μMS devices for brain-machine interfaces and could enable routes toward bioelectronic therapies including prosthetic vision devices.}, }
@article {pmid39184542, year = {2024}, author = {Jeon, H and Park, IM}, title = {Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {39184542}, issn = {2331-8422}, support = {RF1 DA056404/DA/NIDA NIH HHS/United States ; }, abstract = {Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for building brain machine interfaces and understanding the neural computation underlying meaningful behavior. We derive a practical upper bound to the signal-to-noise ratio (SNR) of inferred neural latent trajectories using Fisher information. We show that the SNR bound is proportional to the overdispersion factor and the Fisher information per neuron. Further numerical experiments show that inference methods that exploit the temporal regularities can achieve higher SNRs that are proportional to the bound. Our results provide insights for fitting models to data, simulating neural responses, and design of experiments.}, }
@article {pmid39181508, year = {2024}, author = {Ho, L and Ramanujan, S and Pramod, N and Tang, S and Bena, JF and De, S}, title = {Clinical Outcomes in Patients With Hypocontractile Bladders Undergoing Holmium Laser Enucleation of the Prostate.}, journal = {Urology}, volume = {194}, number = {}, pages = {231-237}, doi = {10.1016/j.urology.2024.08.023}, pmid = {39181508}, issn = {1527-9995}, mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/complications ; *Lasers, Solid-State/therapeutic use ; Retrospective Studies ; Aged ; *Urodynamics ; Treatment Outcome ; Prostatectomy/methods ; Urinary Bladder/surgery/physiopathology ; Middle Aged ; Laser Therapy/methods ; }, abstract = {OBJECTIVE: To compare post-operative outcomes in patients who underwent holmium laser enucleation of the prostate (HoLEP) for benign prostatic hyperplasia (BPH) and had urodynamic evidence of bladder hypocontractility versus those with normocontractile bladders.<br><br>METHODS: We retrospectively&#xa0;reviewed HoLEP patients with pre-operative urodynamic studies at a single institution, categorizing them into normocontractile and hypocontractile groups based on the bladder contractility index (BCI) (hypocontractile defined as BCI < 100). Post-void residual (PVR) volume was measured at 6 weeks and 6 months. Secondary outcomes included maximum flow rate (Qmax) and catheterization status.<br><br>RESULTS: Among 114 HoLEP patients with pre-operative urodynamic data, 49 had hypocontractile bladders. The median pre-operative PVR was 305 (202-446) mL in the hypocontractile group, higher than the median PVR of 190 (60-361) mL in the normocontractile group (P&#xa0;=&#xa0;.013). At 6 weeks post-op, the median PVR was higher in the hypocontractile compared to normocontractile group (38 [3-61]&#xa0;vs&#xa0;5 [0-44]&#xa0;mL, P&#xa0;=&#xa0;.016), but at 6 months post-op there was no significant difference (18 [0-39]&#xa0;vs&#xa0;12 (0-70) mL, P&#xa0;=&#xa0;.97). Among men who were catheter-dependent pre-operatively, 98% of hypocontractile and 100% of normocontractile patients were catheter-free post-operatively. Qmax and symptom scores were similar at both follow-up time points.<br><br>CONCLUSION: HoLEP can be an effective surgical option for BPH patients with hypocontractile bladders, including those who are catheter-dependent, with minimal differences in post-operative voiding parameters compared to those with normal bladder function.}, }
@article {pmid39180976, year = {2024}, author = {Wang, K and Wei, W and Yi, W and Qiu, S and He, H and Xu, M and Ming, D}, title = {Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {179}, number = {}, pages = {106617}, doi = {10.1016/j.neunet.2024.106617}, pmid = {39180976}, issn = {1879-2782}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Male ; *Arousal/physiology ; Female ; Adult ; Young Adult ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.}, }
@article {pmid39178905, year = {2024}, author = {Kothe, C and Hanada, G and Mullen, S and Mullen, T}, title = {Decoding working-memory load duringn-back task performance from high channel fNIRS data.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad731b}, pmid = {39178905}, issn = {1741-2552}, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Memory, Short-Term/physiology ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Machine Learning ; Psychomotor Performance/physiology ; }, abstract = {Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.}, }
@article {pmid39178898, year = {2024}, author = {Kim, M and Kim, SP}, title = {Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad731e}, pmid = {39178898}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Attention/physiology ; Young Adult ; Adult ; *Electroencephalography/methods ; Evoked Potentials/physiology ; Photic Stimulation/methods ; Speech/physiology ; }, abstract = {Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.}, }
@article {pmid39178637, year = {2024}, author = {Zheng, M and Hong, T and Zhou, H and Garland, EL and Hu, Y}, title = {The acute effect of mindfulness-based regulation on neural indices of cue-induced craving in smokers.}, journal = {Addictive behaviors}, volume = {159}, number = {}, pages = {108134}, doi = {10.1016/j.addbeh.2024.108134}, pmid = {39178637}, issn = {1873-6327}, mesh = {Humans ; *Craving/physiology ; *Cues ; Male ; *Mindfulness/methods ; Adult ; *Magnetic Resonance Imaging ; *Smokers/psychology ; Young Adult ; Amygdala/physiopathology ; Ventral Striatum/physiopathology/diagnostic imaging ; Reward ; Prefrontal Cortex/physiopathology/diagnostic imaging ; Interoception/physiology ; Emotions/physiology ; Insular Cortex/physiopathology ; Brain/physiopathology/diagnostic imaging ; Cigarette Smoking/psychology/therapy ; }, abstract = {Mindfulness has garnered attention for its potential in alleviating cigarette cravings; however, the neural mechanisms underlying its efficacy remain inadequately understood. This study (N=46, all men) aims to examine the impact of a mindfulness strategy on regulating cue-induced craving and associated brain activity. Twenty-three smokers, consuming over 10 cigarettes daily for at least 2 years， were compared to twenty-three non-smokers. During a regulation of craving task, participants were asked to practice mindfulness during smoking cue-exposure or passively view smoking cues while fMRI scans were completed. A 2 (condition: mindfulness-cigarette and look-cigarette) × 2 (phase: early, late of whole smoking cue-exposure period) repeated measures ANOVA showed a significant interaction of the craving scores between condition and phase, indicating that the mindfulness strategy dampened late-phase craving. Additionally, within the smoker group, the fMRI analyses revealed a significant main effect of mindfulness condition and its interaction with time in several brain networks involving reward, emotion, and interoception. Specifically, the bilateral insula, ventral striatum, and amygdala showed lower activation in the mindfulness condition, whereas the activation of right orbitofrontal cortex mirrored the strategy-time interaction effect of the craving change. This study illuminates the dynamic interplay between mindfulness, smoking cue-induced craving, and neural activity, offering insights into how mindfulness may effectively regulate cigarette cravings.}, }
@article {pmid39178112, year = {2024}, author = {Tu, WY and Xu, W and Bai, L and Liu, J and Han, Y and Luo, B and Wang, B and Zhang, K and Shen, C}, title = {Local protein synthesis at neuromuscular synapses is required for motor functions.}, journal = {Cell reports}, volume = {43}, number = {9}, pages = {114661}, doi = {10.1016/j.celrep.2024.114661}, pmid = {39178112}, issn = {2211-1247}, mesh = {Animals ; *Neuromuscular Junction/metabolism ; *Protein Biosynthesis ; Mice ; *Motor Neurons/metabolism ; RNA, Messenger/metabolism/genetics ; Synaptic Transmission ; Agrin/metabolism ; Mice, Inbred C57BL ; Motor Activity ; Synapses/metabolism ; Axons/metabolism ; }, abstract = {Motor neurons are highly polarized, and their axons extend over great distances to form connections with myofibers via neuromuscular junctions (NMJs). Local translation at the NMJs in vivo has not been identified. Here, we utilized motor neuron-labeled RiboTag mice and the TRAP (translating ribosome affinity purification) technique to spatiotemporally profile the translatome at NMJs. We found that mRNAs associated with glucose catabolism, synaptic connection, and protein homeostasis are enriched at presynapses. Local translation at the synapse shifts from the assembly of cytoskeletal components during early developmental stages to energy production in adulthood. The mRNA of neuronal Agrin (Agrn), the key molecule for NMJ assembly, is present at motor axon terminals and locally translated. Disrupting the axonal location of Agrn mRNA causes impairment of synaptic transmission and motor functions in adult mice. Our findings indicate that spatiotemporal regulation of mRNA local translation at NMJs plays critical roles in synaptic transmission and motor functions in vivo.}, }
@article {pmid39175558, year = {2024}, author = {Azadi Moghadam, M and Maleki, A}, title = {Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number.}, journal = {Journal of biomedical physics &amp; engineering}, volume = {14}, number = {4}, pages = {365-378}, pmid = {39175558}, issn = {2251-7200}, abstract = {BACKGROUND: A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems.<br><br>OBJECTIVE: The current study aimed to examine the effect of data characteristics on frequency recognition accuracy.<br><br>MATERIAL AND METHODS: In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics.<br><br>RESULTS: The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method.<br><br>CONCLUSION: Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.}, }
@article {pmid39175425, year = {2024}, author = {Yao, J and Li, Z and Zhou, Z and Bao, A and Wang, Z and Wei, H and He, H}, title = {Distinct regional vulnerability to Aβ and iron accumulation in post mortem AD brains.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {20}, number = {10}, pages = {6984-6997}, pmid = {39175425}, issn = {1552-5279}, support = {2020AAA0109502//National Key R&amp;D Program of China/ ; 82372036//National Natural Science Foundation of China/ ; 226-2023-00095//Fundamental Research Funds for the Central Universities/ ; 82151303//National Natural Science Foundation/ ; 2021ZD0204000//STI2030-Major Projects/ ; }, mesh = {Aged ; Aged, 80 and over ; Female ; Humans ; Male ; *Alzheimer Disease/pathology/metabolism/genetics ; *Amyloid beta-Peptides/metabolism ; Autopsy ; *Brain/pathology/metabolism ; *Iron/metabolism ; Magnetic Resonance Imaging ; Plaque, Amyloid/pathology/metabolism ; }, abstract = {INTRODUCTION: The paramagnetic iron, diamagnetic amyloid beta (Aβ) plaques and their interaction are crucial in Alzheimer's disease (AD) pathogenesis, complicating non-invasive magnetic resonance imaging for prodromal AD detection.<br><br>METHODS: We used a state-of-the-art sub-voxel quantitative susceptibility mapping method to simultaneously measure A&#x3b2; and iron levels in post mortem human brains, validated by histology. Further transcriptomic analysis using Allen Human Brain Atlas elucidated the underlying biological processes.<br><br>RESULTS: Regional increased paramagnetic and diamagnetic susceptibility were observed in medial prefrontal, medial parietal, and para-hippocampal cortices associated with iron deposition (R&#xa0;=&#xa0;0.836, p&#xa0;=&#xa0;0.003) and A&#x3b2; accumulation (R&#xa0;=&#xa0;0.853, p&#xa0;=&#xa0;0.002) in AD brains. Higher levels of gene expression relating to cell cycle, post-translational protein modifications, and cellular response to stress were observed.<br><br>DISCUSSION: These findings provide quantitative insights into the variable vulnerability of cortical regions to higher levels of A&#x3b2; aggregation, iron overload, and subsequent neurodegeneration, indicating changes preceding clinical symptoms.<br><br>HIGHLIGHTS: The vulnerability of distinct brain regions to amyloid beta (A&#x3b2;) and iron accumulation varies. Histological validation was performed on stained sections of ex-vivo human brains. Regional variations in susceptibility were linked to gene expression profiles. Iron and A&#x3b2; levels in ex-vivo brains were simultaneously quantified.}, }
@article {pmid39173928, year = {2024}, author = {Cao, HL and Yu, H and Xue, R and Yang, X and Ma, X and Wang, Q and Deng, W and Guo, WJ and Li, ML and Li, T}, title = {Convergence and divergence in neurostructural signatures of unipolar and bipolar depressions: Insights from surface-based morphometry and prospective follow-up.}, journal = {Journal of affective disorders}, volume = {366}, number = {}, pages = {8-15}, doi = {10.1016/j.jad.2024.08.101}, pmid = {39173928}, issn = {1573-2517}, mesh = {Humans ; *Bipolar Disorder/diagnostic imaging/pathology ; Female ; Male ; *Magnetic Resonance Imaging ; Adult ; Follow-Up Studies ; Prospective Studies ; Depressive Disorder/diagnostic imaging/pathology ; Cerebral Cortex/diagnostic imaging/pathology ; Brain/diagnostic imaging/pathology ; Middle Aged ; Young Adult ; Case-Control Studies ; }, abstract = {BACKGROUND: Bipolar disorder (BD) is often misidentified as unipolar depression (UD) during its early stages, typically until the onset of the first manic episode. This study aimed to explore both shared and unique neurostructural changes in patients who transitioned from UD to BD during follow-up, as compared to those with UD.<br><br>METHODS: This study utilized high-resolution structural magnetic resonance imaging (MRI) to collect brain data from individuals initially diagnosed with UD. During the average 3-year follow-up, 24 of the UD patients converted to BD (cBD). For comparison, the study included 48 demographically matched UD patients who did not convert and 48 healthy controls. The MRI data underwent preprocessing using FreeSurfer, followed by surface-based morphometry (SBM) analysis to identify cortical thickness (CT), surface area (SA), and cortical volume (CV) among groups.<br><br>RESULTS: The SBM analysis identified shared neurostructural characteristics between the cBD and UD groups, specifically thinner CT in the right precentral cortex compared to controls. Unique to the cBD group, there was a greater SA in the right inferior parietal cortex compared to the UD group. Furthermore, no significant correlations were observed between cortical morphological measures and cognitive performance and clinical features in the cBD and UD groups.<br><br>LIMITATIONS: The sample size is relatively small.<br><br>CONCLUSIONS: Our findings suggest that while cBD and UD exhibit some common alterations in cortical macrostructure, numerous distinct differences are also present. These differences offer valuable insights into the neuropathological underpinnings that distinguish these two conditions.}, }
@article {pmid39171098, year = {2024}, author = {Freudenburg, Z and Berezutskaya, J and Herbert, C}, title = {Editorial: The ethics of speech ownership in the context of neural control of augmented assistive communication.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1468938}, pmid = {39171098}, issn = {1662-5161}, }
@article {pmid39170600, year = {2024}, author = {Pan, H and Fu, Y and Zhang, Q and Zhang, J and Qin, X}, title = {The decoder design and performance comparative analysis for closed-loop brain-machine interface system.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {1}, pages = {147-164}, pmid = {39170600}, issn = {1871-4080}, abstract = {Brain-machine interface (BMI) can convert electroencephalography signals (EEGs) into the control instructions of external devices, and the key of control performance is the accuracy and efficiency of decoder. However, the performance of different decoders obtaining control instructions from complex and variable EEG signals is very different and irregular in the different neural information transfer model. Aiming at this problem, the off-line and on-line performance of eight decoders based on the improved single-joint information transmission (SJIT) model is compared and analyzed in this paper, which can provide a theoretical guidance for decoder design. Firstly, in order to avoid the different types of neural activities in the decoding process on the decoder performance, eight decoders based on the improved SJIT model are designed. And then the off-line decoding performance of these decoders is tested and compared. Secondly, a closed-loop BMI system which combining by the designed decoder and the random forest encoder based on the improved SJIT model is constructed. Finally, based on the constructed closed-loop BMI system, the on-line decoding performance of decoders is compared and analyzed. The results show that the LSTM-based decoder has better on-line decoding performance than others in the improved SJIT model.}, }
@article {pmid39167669, year = {2024}, author = {Matsiko, A}, title = {Bilingual speech neuroprosthesis.}, journal = {Science robotics}, volume = {9}, number = {93}, pages = {eads4122}, doi = {10.1126/scirobotics.ads4122}, pmid = {39167669}, issn = {2470-9476}, mesh = {Humans ; *Multilingualism ; *Speech ; *Brain-Computer Interfaces ; Neural Prostheses ; Brain/physiology ; Prosthesis Design ; Male ; }, abstract = {A neuroprosthesis could decode two languages from the brain activity of a bilingual participant who was unable to articulate speech.}, }
@article {pmid39166947, year = {2024}, author = {Zhang, H and Jiao, L and Yang, S and Li, H and Jiang, X and Feng, J and Zou, S and Xu, Q and Gu, J and Wang, X and Wei, B}, title = {Brain-computer interfaces: the innovative key to unlocking neurological conditions.}, journal = {International journal of surgery (London, England)}, volume = {110}, number = {9}, pages = {5745-5762}, pmid = {39166947}, issn = {1743-9159}, mesh = {Humans ; *Brain-Computer Interfaces ; *Nervous System Diseases ; Artificial Intelligence ; Electroencephalography/methods ; Parkinson Disease ; }, abstract = {Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.}, }
@article {pmid39166824, year = {2024}, author = {Hata, J and Matsuoka, K and Akaihata, H and Yaginuma, K and Meguro, S and Hoshi, S and Koguchi, T and Sato, Y and Kataoka, M and Ogawa, S and Uemura, M and Kojima, Y}, title = {Prognosis of lower urinary tract symptoms and function after robot-assisted radical prostatectomy in patients with preoperative low bladder contractility: A prospective, observational study.}, journal = {Neurourology and urodynamics}, volume = {43}, number = {8}, pages = {2240-2248}, doi = {10.1002/nau.25577}, pmid = {39166824}, issn = {1520-6777}, mesh = {Aged ; Humans ; Male ; Middle Aged ; *Lower Urinary Tract Symptoms/physiopathology/diagnosis/etiology/surgery ; *Muscle Contraction ; Prognosis ; Prospective Studies ; *Prostatectomy/adverse effects ; Prostatic Neoplasms/surgery/physiopathology ; *Robotic Surgical Procedures/adverse effects ; Time Factors ; Treatment Outcome ; *Urinary Bladder/physiopathology ; *Urodynamics ; *Preoperative Period ; }, abstract = {OBJECTIVES: To examine the prognosis of lower urinary tract symptoms and function after robot-assisted radical prostatectomy (RARP) in patients with low preoperative bladder contractility.<br><br>METHODS: A total of 115 patients who underwent RARP were enrolled and divided into two groups by preoperative urodynamic findings: normal (patients with bladder contractility index [BCI]&#x2009;&#x2265;&#x2009;100; n&#x2009;=&#x2009;70) and low contractility (patients with BCI&#x2009;<&#x2009;100; n&#x2009;=&#x2009;45) groups. Lower urinary tract symptoms and function parameters were prospectively evaluated at 1, 3, 6, 9, and 12 months after RARP in both groups.<br><br>RESULTS: International Prostatic Symptom Score voiding scores 1, 3, 6, 9, and 12 months after RARP were significantly higher (p&#x2009;<&#x2009;0.05), and the maximum flow rate (Qmax) values before and 1, 3, 9, and 12 months after RARP were significantly lower in the low contractility group (p&#x2009;<&#x2009;0.05). Comparing preoperative and postoperative parameters, IPSS voiding scores in the normal contractility group were significantly improved from 6 months after RARP, whereas those in the low contractility group were almost unchanged. Qmax and the 1-h pad test in both groups temporarily deteriorated 1 month after RARP, whereas voided volume and postvoiding residual volume significantly decreased from 1 to 12 months after RARP.<br><br>CONCLUSIONS: This observational study showed that patients with low preoperative bladder contractility might have a weak improvement in voiding symptoms and function after RARP.}, }
@article {pmid39165885, year = {2024}, author = {Singh, AK and Bianchi, L and Valeriani, D and Nakanishi, M}, title = {Editorial: Advances and challenges to bridge computational intelligence and neuroscience for brain-computer interface.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1461494}, pmid = {39165885}, issn = {2673-6195}, }
@article {pmid39165780, year = {2024}, author = {Tapia, JL and Lopez, A and Turner, DB and Fairley, T and Tomlin-Harris, T and Hawkins, M and Holbert, PR and Treviño, LS and Teteh-Brooks, DK}, title = {The bench to community initiative: community-based participatory research model for translating research discoveries into community solutions.}, journal = {Frontiers in public health}, volume = {12}, number = {}, pages = {1394069}, pmid = {39165780}, issn = {2296-2565}, support = {K01 MD018417/MD/NIMHD NIH HHS/United States ; L60 CA253971/CA/NCI NIH HHS/United States ; R01 CA217841/CA/NCI NIH HHS/United States ; R43 MD017966/MD/NIMHD NIH HHS/United States ; }, mesh = {*Community-Based Participatory Research ; Humans ; *Breast Neoplasms ; Female ; Translational Research, Biomedical ; Black or African American ; Quality of Life ; }, abstract = {UNLABELLED: Community-based participatory research (CBPR) is an effective methodology for translating research findings from academia to community interventions. The Bench to Community Initiative (BCI), a CBPR program, builds on prior research to engage stakeholders across multiple disciplines with the goal of disseminating interventions to reduce breast cancer disparities and improve quality of life of Black communities.<br><br>METHODS: The BCI program was established to understand sociocultural determinants of personal care product use, evaluate the biological impact of endocrine disrupting chemicals, and develop community interventions. The three pillars of the program include research, outreach and engagement as well as advocacy activities. The research pillar of the BCI includes development of multidisciplinary partnerships to understand the sociocultural and biological determinants of harmful chemical (e.g., endocrine disrupting chemicals) exposures from personal care products and to implement community interventions. The outreach and engagement pillar includes education and translation of research into behavioral practice. The research conducted through the initiative provides the foundation for advocacy engagement with applicable community-based organizations. Essential to the mission of the BCI is the participation of community members and trainees from underrepresented backgrounds who are affected by breast cancer disparities.<br><br>RESULTS: Two behavioral interventions will be developed building on prior research on environmental exposures with the focus on personal care products including findings from the BCI. In person and virtual education activities include tabling at community events with do-it-yourself product demonstrations, Salon Conversations-a virtual platform used to bring awareness, education, and pilot behavior change interventions, biennial symposiums, and social media engagement. BCI's community advisory board members support activities across the three pillars, while trainees participate in personal and professional activities that enhance their skills in research translation.<br><br>DISCUSSION: This paper highlights the three pillars of the BCI, lessons learned, testimonies from community advisory board members and trainees on the impact of the initiative, as well as BCI's mission driven approaches to achieving health equity.}, }
@article {pmid39165718, year = {2024}, author = {Qiu, Y and Lian, YN and Wu, C and Liu, L and Zhang, C and Li, XY}, title = {Coordination between midcingulate cortex and retrosplenial cortex in pain regulation.}, journal = {Frontiers in molecular neuroscience}, volume = {17}, number = {}, pages = {1405532}, pmid = {39165718}, issn = {1662-5099}, abstract = {INTRODUCTION: The cingulate cortex, with its subregions ACC, MCC, and RSC, is key in pain processing. However, the detailed interactions among these regions in modulating pain sensation have remained unclear.<br><br>METHODS: In this study, chemogenetic tools were employed to selectively activate or inhibit neuronal activity in the MCC and RSC of rodents to elucidate their roles in pain regulation.Results: Our results showed that chemogenetic activation in both the RSC and MCC heightened pain sensitivity. Suppression of MCC activity disrupted the RSC's regulation of both mechanical and thermal pain, while RSC inhibition specifically affected the MCC's regulation of thermal pain.<br><br>DISCUSSION: The findings indicate a complex interplay between the MCC and RSC, with the MCC potentially governing the RSC's pain regulatory mechanisms. The RSC, in turn, is crucial for the MCC's control over thermal sensation, revealing a collaborative mechanism in pain processing.<br><br>CONCLUSION: This study provides evidence for the MCC and RSC's collaborative roles in pain regulation, highlighting the importance of their interactions for thermal and mechanical pain sensitivity. Understanding these mechanisms could aid in developing targeted therapies for pain disorders.}, }
@article {pmid39164787, year = {2024}, author = {Li, JK and Tang, T and Zong, H and Wu, EM and Zhao, J and Wu, RR and Zheng, XN and Zhang, H and Li, YF and Zhou, XH and Zhang, CC and Zhang, ZL and Zhang, YH and Feng, WZ and Zhou, Y and Wang, J and Zhu, QY and Deng, Q and Zheng, JM and Yang, L and Wei, Q and Shen, BR}, title = {Intelligent medicine in focus: the 5 stages of evolution in robot-assisted surgery for prostate cancer in the past 20 years and future implications.}, journal = {Military Medical Research}, volume = {11}, number = {1}, pages = {58}, pmid = {39164787}, issn = {2054-9369}, support = {2023SCU12057//Fundamental Research Funds for the Central Universities/ ; 82373106//National Natural Science Foundation of China/ ; 82372831//National Natural Science Foundation of China/ ; 32270690//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; *Robotic Surgical Procedures/methods/history/trends ; *Prostatic Neoplasms/surgery ; Artificial Intelligence/trends ; }, abstract = {Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.}, }
@article {pmid39164105, year = {2024}, author = {Kabir, A and Dhami, P and Dussault Gomez, MA and Blumberger, DM and Daskalakis, ZJ and Moreno, S and Farzan, F}, title = {Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {39}, pages = {}, pmid = {39164105}, issn = {1529-2401}, mesh = {Humans ; Male ; *Transcranial Magnetic Stimulation/methods ; Female ; Adult ; *Electroencephalography/methods ; Young Adult ; *Prefrontal Cortex/physiology ; *Brain/physiology ; Evoked Potentials/physiology ; }, abstract = {Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.}, }
@article {pmid39163821, year = {2024}, author = {Cheng, C and Liu, W and Feng, L and Jia, Z}, title = {Emotion recognition using hierarchical spatial-temporal learning transformer from regional to global brain.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {179}, number = {}, pages = {106624}, doi = {10.1016/j.neunet.2024.106624}, pmid = {39163821}, issn = {1879-2782}, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; }, abstract = {Emotion recognition is an essential but challenging task in human-computer interaction systems due to the distinctive spatial structures and dynamic temporal dependencies associated with each emotion. However, current approaches fail to accurately capture the intricate effects of electroencephalogram (EEG) signals across different brain regions on emotion recognition. Therefore, this paper designs a transformer-based method, denoted by R2G-STLT, which relies on a spatial-temporal transformer encoder with regional to global hierarchical learning that learns the representative spatiotemporal features from the electrode level to the brain-region level. The regional spatial-temporal transformer (RST-Trans) encoder is designed to obtain spatial information and context dependence at the electrode level aiming to learn the regional spatiotemporal features. Then, the global spatial-temporal transformer (GST-Trans) encoder is utilized to extract reliable global spatiotemporal features, reflecting the impact of various brain regions on emotion recognition tasks. Moreover, the multi-head attention mechanism is placed into the GST-Trans encoder to empower it to capture the long-range spatial-temporal information among the brain regions. Finally, subject-independent experiments are conducted on each frequency band of the DEAP, SEED, and SEED-IV datasets to assess the performance of the proposed model. Results indicate that the R2G-STLT model surpasses several state-of-the-art approaches.}, }
@article {pmid39161850, year = {2024}, author = {Dillen, A and Omidi, M and Díaz, MA and Ghaffari, F and Roelands, B and Vanderborght, B and Romain, O and De Pauw, K}, title = {Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1448584}, pmid = {39161850}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.}, }
@article {pmid39161655, year = {2024}, author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y}, title = {Considerations and discussions on the clear definition and definite scope of brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1449208}, pmid = {39161655}, issn = {1662-4548}, abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.}, }
@article {pmid39160882, year = {2024}, author = {Pitkin, M and Park, H and Frossard, L and Klishko, AN and Prilutsky, BI}, title = {Transforming the Anthropomorphic Passive Free-Flow Foot Prosthesis Into a Powered Foot Prosthesis With Intuitive Control and Sensation (Bionic FFF).}, journal = {Military medicine}, volume = {189}, number = {Suppl 3}, pages = {439-447}, doi = {10.1093/milmed/usae149}, pmid = {39160882}, issn = {1930-613X}, support = {MR150051//U.S. Department of Defense/ ; AR43290//National Center for Medical Rehabilitation Research/ ; MR150051//U.S. Department of Defense/ ; AR43290//National Center for Medical Rehabilitation Research/ ; }, mesh = {*Artificial Limbs/statistics &amp; numerical data ; Animals ; *Prosthesis Design/methods ; Cats ; Foot/physiology/physiopathology ; Amputees/rehabilitation ; Electromyography/methods/instrumentation ; Bionics/methods/instrumentation ; Walking/physiology/statistics &amp; numerical data ; Humans ; }, abstract = {INTRODUCTION: Approximately 89% of all service members with amputations do not return to duty. Restoring intuitive neural control with somatosensory sensation is a key to improving the safety and efficacy of prosthetic locomotion. However, natural somatosensory feedback from lower-limb prostheses has not yet been incorporated into any commercial prostheses.<br><br>MATERIALS AND METHODS: We developed a neuroprosthesis with intuitive bidirectional control and somatosensation and evoking phase-dependent locomotor reflexes, we aspire to significantly improve the prosthetic rehabilitation and long-term functional outcomes of U.S. amputees. We implanted the skin and bone integrated pylon with peripheral neural interface pylon into the cat distal tibia, electromyographic electrodes into the residual gastrocnemius muscle, and nerve cuff electrodes on the distal tibial and sciatic nerves. Results. The bidirectional neural interface that was developed was integrated into the existing passive Free-Flow Foot and Ankle prosthesis, WillowWood, Mount Sterling, OH. The Free-Flow Foot was chosen because it had the highest Index of Anthropomorphicity among lower-limb prostheses and was the first anthropomorphic prosthesis brought to market. Conclusion. The cats walked on a treadmill with no cutaneous feedback from the foot in the control condition and with their residual distal tibial nerve stimulated during the stance phase of walking.}, }
@article {pmid39160021, year = {2024}, author = {Zhu, L and Wang, W and Huang, A and Ying, N and Xu, P and Zhang, J}, title = {An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.}, journal = {Medical engineering &amp; physics}, volume = {130}, number = {}, pages = {104213}, doi = {10.1016/j.medengphy.2024.104213}, pmid = {39160021}, issn = {1873-4030}, mesh = {Humans ; *Electroencephalography ; *Signal Processing, Computer-Assisted ; *Seizures/diagnosis/physiopathology ; *Neural Networks, Computer ; Epilepsy/physiopathology/diagnosis ; Algorithms ; }, abstract = {Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.}, }
@article {pmid39159588, year = {2024}, author = {Liu, L and Wang, D and Luo, Y and Liu, Y and Guo, Y and Yang, GZ and Qiu, G}, title = {Intraoperative assessment of microimplantation-induced acute brain inflammation with titanium oxynitride-based plasmonic biosensor.}, journal = {Biosensors &amp; bioelectronics}, volume = {264}, number = {}, pages = {116664}, doi = {10.1016/j.bios.2024.116664}, pmid = {39159588}, issn = {1873-4235}, mesh = {Animals ; *Titanium/chemistry ; *Surface Plasmon Resonance ; Mice ; Biosensing Techniques ; Encephalitis/etiology ; Microelectrodes ; Interleukin-6/analysis/cerebrospinal fluid ; Brain ; Brain-Computer Interfaces ; Equipment Design ; Electrodes, Implanted/adverse effects ; Humans ; }, abstract = {Implantable devices for brain-machine interfaces and managing neurological disorders have experienced rapid growth in recent years. Although functional implants offer significant benefits, issues related to transient trauma and long-term biocompatibility and safety are of significant concern. Acute inflammatory reaction in the brain tissue caused by microimplants is known to be an issue but remains poorly studied. This study presents the use of titanium oxynitride (TiNO) nanofilm with defined surface plasmon resonance (SPR) properties for point-of-care characterizing of acute inflammatory responses during robot-controlled micro-neuro-implantation. By leveraging surface-enriched oxynitride, TiNO nanofilms can be biomolecular-functionalized through silanization. This label-free TiNO-SPR biosensor exhibits a high sensitivity toward the inflammatory cytokine interleukin-6 with a detection limit down to 6.3 fg ml[-1] and a short assay time of 25 min. Additionally, intraoperative monitoring of acute inflammatory responses during microelectrode implantation in the mice brain has been accomplished using the TiNO-SPR biosensors. Through intraoperative cerebrospinal fluid sampling and point-of-care plasmonic biosensing, the rhythm of acute inflammatory responses induced by the robot-controlled brain microelectrodes implantation has been successfully depicted, offering insights into intraoperative safety assessment of invasive brain-machine interfaces.}, }
@article {pmid39156926, year = {2024}, author = {Zhu, H and Deng, X and Yakovlev, VV and Zhang, D}, title = {Dynamics of CH/n hydrogen bond networks probed by time-resolved CARS spectroscopy.}, journal = {Chemical science}, volume = {15}, number = {35}, pages = {14344-14351}, pmid = {39156926}, issn = {2041-6520}, support = {R01 GM127696/GM/NIGMS NIH HHS/United States ; R21 CA269099/CA/NCI NIH HHS/United States ; R21 GM142107/GM/NIGMS NIH HHS/United States ; }, abstract = {Hydrogen bond (HB) networks are essential for stabilizing molecular structures in solution and govern the solubility and functionality of molecules in an aqueous environment. HBs are important in biological processes such as enzyme-substrate interactions, protein folding, and DNA replication. However, the exact role of weakly polarized C-H bonds as HB proton donors in solution, such as CH/n HBs, remains mostly unknown. Here, we employ a novel approach focusing on vibrational dephasing to investigate the coherence relaxation of induced dipoles in C-H bonds within CH/n HB networks, utilizing time-resolved coherent anti-Stokes Raman scattering (T-CARS) spectroscopy. Using a representative binary system of dimethyl sulfoxide (DMSO)-water, known for its C-H backboned HB system (i.e., C-H⋯S), we observed an increase in the dephasing time of the C-H bending mode with increasing water content until a percolation threshold at a 6 : 1 water : DMSO molar ratio, where the trend is reversed. These results provide compelling evidence for the existence of C-H⋯S structures and underscore the presence of a percolation effect, suggesting a critical threshold where long-range connectivity is disputed.}, }
@article {pmid39156581, year = {2024}, author = {Xiong, L and Cao, J and Dong, H and Song, W and Ming, D}, title = {Multidisciplinary integration of frontier technologies facilitating the development of anesthesiology and perioperative medicine in aging society.}, journal = {Fundamental research}, volume = {4}, number = {4}, pages = {795-796}, pmid = {39156581}, issn = {2667-3258}, }
@article {pmid39155721, year = {2024}, author = {Li, Y and Su, C and Pan, Y}, title = {Spontaneous movement synchrony as an exogenous source for interbrain synchronization in cooperative learning.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1911}, pages = {20230155}, pmid = {39155721}, issn = {1471-2970}, support = {//Humanities and Social Sciences Research Project from the Ministry of Education of China/ ; //National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Brain/physiology ; Male ; *Movement/physiology ; Female ; *Learning/physiology ; Young Adult ; Cooperative Behavior ; Spectroscopy, Near-Infrared ; Adult ; }, abstract = {Learning through cooperation with conspecifics-'cooperative learning'-is critical to cultural evolution and survival. Recent progress has established that interbrain synchronization (IBS) between individuals predicts success in cooperative learning. However, the likely sources of IBS during learning interactions remain poorly understood. To address this dearth of knowledge, we tested whether movement synchrony serves as an exogenous factor that drives IBS, taking an embodiment perspective. We formed dyads of individuals with varying levels of prior knowledge (high-high (HH), high-low (HL), low-low (LL) dyads) and instructed them to collaboratively analyse an ancient Chinese poem. During the task, we simultaneously recorded their brain activity using functional near-infrared spectroscopy and filmed the entire experiment to parse interpersonal movement synchrony using the computer-vision motion energy analysis. Interestingly, the homogeneous groups (HH and/or LL) exhibited stronger movement synchrony and IBS compared with the heterogeneous group. Importantly, mediation analysis revealed that spontaneous and synchronized body movements between individuals contribute to IBS, hence facilitating learning. This study therefore fills a critical gap in our understanding of how interpersonal transmission of information between individual brains, associated with behavioural entrainment, shapes social learning. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.}, }
@article {pmid39154786, year = {2024}, author = {Li, X and Wang, S and Ruan, Y and Pan, Y and Huang, Y}, title = {Taste or health: The impact of packaging cues on consumer decision-making in healthy foods.}, journal = {Appetite}, volume = {203}, number = {}, pages = {107636}, doi = {10.1016/j.appet.2024.107636}, pmid = {39154786}, issn = {1095-8304}, mesh = {Humans ; *Consumer Behavior ; *Cues ; Male ; Female ; Adult ; *Food Packaging/methods ; *Food Preferences/psychology ; *Taste ; Young Adult ; Diet, Healthy/psychology ; Decision Making ; Intention ; Choice Behavior ; Food, Organic ; Middle Aged ; Adolescent ; }, abstract = {According to the theory of dietary regulation, consumers frequently encounter conflicts between healthiness and tastiness when selecting healthy foods. This study explores how packaging cue that highlight "tasty" versus "healthy" affect consumers' intentions to purchase healthy food. After an Implicit Association Test (IAT) confirmed a perceived lack of tastiness in health foods in the Preliminary study, Study 1 analyzed pricing and packaging details of the top 200 most-popular items in each of the ten healthy food categories on a major online shopping platform. Results showed that products with taste-focused cues commanded higher prices, indicating stronger consumer acceptance of healthy foods marketed as delicious. To address the causality limitations of observational studies, Study 2 used an experimental design to directly measure the impact of these cues on purchase intentions and perceptions of energy, healthiness, and tastiness. Findings revealed that taste-focused cues significantly boosted purchase intentions compared to health-focused cues, although they also diminished the perceived healthiness of the products. Moreover, in the control group exposed to unhealthy food options, health-emphasized packaging also increased purchase intentions, indicating that consumers seek a balance between healthiness and tastiness, rather than prioritizing health alone. Study 3 further explored the impact of cognitive load over these cue influences, revealing a heightened inclination among consumers to purchase healthy products with taste-focused cue under high cognitive load state. These insights have direct implications for food packaging design, suggesting that emphasizing a balance of taste and health benefits can effectively enhance consumer engagement. The study, which conducted in China, also opens avenues for future research to explore similar effects, maybe in different cultural contexts, different consumer groups, and under varied cognitive conditions.}, }
@article {pmid39151656, year = {2024}, author = {Kuo, CH and Liu, GT and Lee, CE and Wu, J and Casimo, K and Weaver, KE and Lo, YC and Chen, YY and Huang, WC and Ojemann, JG}, title = {Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.}, journal = {Journal of neuroscience methods}, volume = {411}, number = {}, pages = {110251}, doi = {10.1016/j.jneumeth.2024.110251}, pmid = {39151656}, issn = {1872-678X}, mesh = {Humans ; *Fingers/physiology ; *Electrocorticography/methods ; *Movement/physiology ; *Neural Networks, Computer ; Adult ; Male ; Female ; Epilepsy/physiopathology ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction.<br><br>NEW METHOD: This study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories.<br><br>RESULTS: The 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26-0.38 for single finger movements and 0.20-0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control.<br><br>The 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models.<br><br>CONCLUSIONS: The proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control.}, }
@article {pmid39150815, year = {2024}, author = {Wu, H and Ma, Z and Guo, Z and Wu, Y and Zhang, J and Zhou, G and Long, J}, title = {Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3059-3070}, doi = {10.1109/TNSRE.2024.3445115}, pmid = {39150815}, issn = {1558-0210}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; *Machine Learning ; Privacy ; Online Systems ; Transfer, Psychology/physiology ; Adult ; Male ; }, abstract = {Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial information from previous subjects, i.e., source domains. Nevertheless, EEG signals involve sensitive personal mental and health information. Thus, privacy concern becomes a critical issue. In addition, existing methods mostly assume that a portion of the new subject's data is available and perform alignment or adaptation between the source and target domains. However, in some practical scenarios, new subjects prefer prompt BCI utilization over the time-consuming process of collecting data for calibration and adaptation, which makes the above assumption difficult to hold. To address the above challenges, we propose Online Source-Free Transfer Learning (OSFTL) for privacy-preserving EEG classification. Specifically, the learning procedure contains offline and online stages. At the offline stage, multiple model parameters are obtained based on the EEG samples from multiple source subjects. OSFTL only needs access to these source model parameters to preserve the privacy of the source subjects. At the online stage, a target classifier is trained based on the online sequence of EEG instances. Subsequently, OSFTL learns a weighted combination of the source and target classifiers to obtain the final prediction for each target instance. Moreover, to ensure good transferability, OSFTL dynamically updates the transferred weight of each source domain based on the similarity between each source classifier and the target classifier. Comprehensive experiments on both simulated and real-world applications demonstrate the effectiveness of the proposed method, indicating the potential of OSFTL to facilitate the deployment of BCI applications outside of controlled laboratory settings.}, }
@article {pmid39149481, year = {2024}, author = {Liu, CW and Chen, SY and Wang, YM and Lu, LY and Chen, P and Liang, TY and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK}, title = {The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {39149481}, issn = {2693-5015}, support = {R01 NS104423/NS/NINDS NIH HHS/United States ; R01 NS118179/NS/NINDS NIH HHS/United States ; R01 NS124854/NS/NINDS NIH HHS/United States ; }, abstract = {Cross-individual variability is considered the essence of biology, preventing precise mathematical descriptions of biological motion[1-7] like the physics law of motion. Here we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in-vivo electrophysiology and optogenetics in mice, we confirmed that deep cerebellar neurons encoded frequencies via populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism was consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform, or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validated the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating-current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for brain-computer interface for motor control.}, }
@article {pmid39145953, year = {2024}, author = {O'Regan, RM and Zhang, Y and Fleming, GF and Francis, PA and Kammler, R and Viale, G and Dell'Orto, P and Lang, I and Bellet, M and Bonnefoi, HR and Tondini, C and Villa, F and Bernardo, A and Ciruelos, EM and Neven, P and Karlsson, P and Müller, B and Jochum, W and Zaman, K and Martino, S and Geyer, CE and Jerzak, KJ and Davidson, NE and Coleman, RE and Ingle, JN and van Mackelenbergh, MT and Loi, S and Colleoni, M and Schnabel, CA and Treuner, K and Regan, MM}, title = {Breast Cancer Index in Premenopausal Women With Early-Stage Hormone Receptor-Positive Breast Cancer.}, journal = {JAMA oncology}, volume = {10}, number = {10}, pages = {1379-1389}, pmid = {39145953}, issn = {2374-2445}, mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/pathology/genetics ; *Premenopause ; Adult ; Prospective Studies ; *Tamoxifen/therapeutic use ; *Biomarkers, Tumor/genetics/metabolism ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; *Antineoplastic Agents, Hormonal/therapeutic use ; Middle Aged ; Retrospective Studies ; Receptors, Interleukin-17 ; Receptors, Estrogen/metabolism ; Chemotherapy, Adjuvant ; Homeodomain Proteins/genetics ; Receptors, Progesterone/metabolism ; Androstadienes/therapeutic use/administration &amp; dosage ; Neoplasm Staging ; Treatment Outcome ; Predictive Value of Tests ; Aromatase Inhibitors/therapeutic use ; }, abstract = {IMPORTANCE: Adjuvant ovarian function suppression (OFS) with oral endocrine therapy improves outcomes for premenopausal patients with hormone receptor-positive (HR+) breast cancer but adds adverse effects. A genomic biomarker for selecting patients most likely to benefit from OFS-based treatment is lacking.<br><br>OBJECTIVE: To assess the predictive and prognostic performance of the Breast Cancer Index (BCI) for OFS benefit in premenopausal women with HR+ breast cancer.<br><br>This prospective-retrospective translational study used all available tumor tissue samples from female patients from the Suppression of Ovarian Function Trial (SOFT). These individuals were randomized to receive 5 years of adjuvant tamoxifen alone, tamoxifen plus OFS, or exemestane plus OFS. BCI testing was performed blinded to clinical data and outcome. The a priori hypothesis was that BCI HOXB13/IL17BR ratio (BCI[H/I])-high tumors would benefit more from OFS and high BCI portended poorer prognosis in this population. Settings spanned multiple centers internationally. Participants included premenopausal female patients with HR+ early breast cancer with specimens in the International Breast Cancer Study Group tumor repository available for RNA extraction. Data were collected from December 2003 to April 2021 and were analyzed from May 2022 to October 2022.<br><br>MAIN OUTCOMES AND MEASURES: Primary end points were breast cancer-free interval (BCFI) for the predictive analysis and distant recurrence-free interval (DRFI) for the prognostic analyses.<br><br>RESULTS: Tumor specimens were available for 1718 of the 3047 female patients in the SOFT intention-to-treat population. The 1687 patients (98.2%) who had specimens that yielded sufficient RNA for BCI testing represented the parent trial population. The median (IQR) follow-up time was 12 (10.5-13.4) years, and 512 patients (30.3%) were younger than 40 years. Tumors were BCI(H/I)-low for 972 patients (57.6%) and BCI(H/I)-high for 715 patients (42.4%). Patients with tumors classified as BCI(H/I)-low exhibited a 12-year absolute benefit in BCFI of 11.6% from exemestane plus OFS (hazard ratio [HR], 0.48 [95% CI, 0.33-0.71]) and an absolute benefit of 7.3% from tamoxifen plus OFS (HR, 0.69 [95% CI, 0.48-0.97]) relative to tamoxifen alone. In contrast, patients with BCI(H/I)-high tumors did not benefit from either exemestane plus OFS (absolute benefit, -0.4%; HR, 1.03 [95% CI, 0.70-1.53]; P for interaction&#x2009;=&#x2009;.006) or tamoxifen plus OFS (absolute benefit, -1.2%; HR, 1.05 [95% CI, 0.72-1.54]; P for interaction&#x2009;=&#x2009;.11) compared with tamoxifen alone. BCI continuous index was significantly prognostic in the N0 subgroup for DRFI (n&#x2009;=&#x2009;1110; P&#x2009;=&#x2009;.004), with 12-year DRFI of 95.9%, 90.8%, and 86.3% in BCI low-risk, intermediate-risk, and high-risk N0 cancers, respectively.<br><br>CONCLUSIONS AND RELEVANCE: In this prospective-retrospective translational study of patients enrolled in SOFT, BCI was confirmed as prognostic in premenopausal women with HR+ breast cancer. The benefit from OFS-containing adjuvant endocrine therapy was greater for patients with BCI(H/I)-low tumors than BCI(H/I)-high tumors. BCI(H/I)-low status may identify premenopausal patients who are likely to benefit from this more intensive endocrine therapy.}, }
@article {pmid39145233, year = {2024}, author = {Lakshminarayanan, K and Ramu, V and Shah, R and Haque Sunny, MS and Madathil, D and Brahmi, B and Wang, I and Fareh, R and Rahman, MH}, title = {Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.}, journal = {PeerJ. Computer science}, volume = {10}, number = {}, pages = {e2174}, pmid = {39145233}, issn = {2376-5992}, abstract = {BACKGROUND: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients.<br><br>METHODS: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot.<br><br>RESULTS: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals.<br><br>DISCUSSION: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.}, }
@article {pmid39144712, year = {2024}, author = {Kim, MS and Park, H and Kwon, I and An, KO and Shin, JH}, title = {Brain-computer interface on wrist training with or without neurofeedback in subacute stroke: a study protocol for a double-blinded, randomized control pilot trial.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1376782}, pmid = {39144712}, issn = {1664-2295}, abstract = {BACKGROUND: After a stroke, damage to the part of the brain that controls movement results in the loss of motor function. Brain-computer interface (BCI)-based stroke rehabilitation involves patients imagining movement without physically moving while the system measures the perceptual-motor rhythm in the motor cortex. Visual feedback through virtual reality and functional electrical stimulation is provided simultaneously. The superiority of real BCI over sham BCI in the subacute phase of stroke remains unclear. Therefore, we aim to compare the effects of real and sham BCI on motor function and brain activity among patients with subacute stroke with weak wrist extensor strength.<br><br>METHODS: This is a double-blinded randomized controlled trial. Patients with stroke will be categorized into real BCI and sham BCI groups. The BCI task involves wrist extension for 60&#x2009;min/day, 5 times/week for 4&#x2009;weeks. Twenty sessions will be conducted. The evaluation will be conducted four times, as follows: before the intervention, 2&#x2009;weeks after the start of the intervention, immediately after the intervention, and 4&#x2009;weeks after the intervention. The assessments include a clinical evaluation, electroencephalography, and electromyography using motor-evoked potentials.<br><br>DISCUSSION: Patients will be categorized into two groups, as follows: those who will be receiving neurofeedback and those who will not receive this feedback during the BCI rehabilitation training. We will examine the importance of motor imaging feedback, and the effect of patients' continuous participation in the training rather than their being passive.Clinical Trial Registration: KCT0008589.}, }
@article {pmid39141859, year = {2024}, author = {Chang, EF}, title = {Brain-Computer Interfaces for Restoring Communication.}, journal = {The New England journal of medicine}, volume = {391}, number = {7}, pages = {654-657}, doi = {10.1056/NEJMe2407363}, pmid = {39141859}, issn = {1533-4406}, mesh = {Humans ; *Brain-Computer Interfaces ; Communication ; *Articulation Disorders/etiology/rehabilitation ; *Amyotrophic Lateral Sclerosis/complications/rehabilitation ; }, }
@article {pmid39141854, year = {2024}, author = {Vansteensel, MJ and Leinders, S and Branco, MP and Crone, NE and Denison, T and Freudenburg, ZV and Geukes, SH and Gosselaar, PH and Raemaekers, M and Schippers, A and Verberne, M and Aarnoutse, EJ and Ramsey, NF}, title = {Longevity of a Brain-Computer Interface for Amyotrophic Lateral Sclerosis.}, journal = {The New England journal of medicine}, volume = {391}, number = {7}, pages = {619-626}, pmid = {39141854}, issn = {1533-4406}, support = {INTENSE, 17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; UH3 NS114439/NS/NINDS NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UGT7685, Economic Affairs SSM06011 and STW 12803//Netherlands Institute of Government/ ; }, mesh = {Female ; Humans ; Middle Aged ; *Amyotrophic Lateral Sclerosis/complications/diagnostic imaging/rehabilitation ; *Atrophy/diagnostic imaging/etiology/prevention &amp; control ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Time Factors ; Treatment Failure ; Electrodes, Implanted ; }, abstract = {The durability of communication with the use of brain-computer interfaces in persons with progressive neurodegenerative disease has not been extensively examined. We report on 7 years of independent at-home use of an implanted brain-computer interface for communication by a person with advanced amyotrophic lateral sclerosis (ALS), the inception of which was reported in 2016. The frequency of at-home use increased over time to compensate for gradual loss of control of an eye-gaze-tracking device, followed by a progressive decrease in use starting 6 years after implantation. At-home use ended when control of the brain-computer interface became unreliable. No signs of technical malfunction were found. Instead, the amplitude of neural signals declined, and computed tomographic imaging revealed progressive atrophy, which suggested that ALS-related neurodegeneration ultimately rendered the brain-computer interface ineffective after years of successful use, although alternative explanations are plausible. (Funded by the National Institute on Deafness and Other Communication Disorders and others; ClinicalTrials.gov number, NCT02224469.).}, }
@article {pmid39141853, year = {2024}, author = {Card, NS and Wairagkar, M and Iacobacci, C and Hou, X and Singer-Clark, T and Willett, FR and Kunz, EM and Fan, C and Vahdati Nia, M and Deo, DR and Srinivasan, A and Choi, EY and Glasser, MF and Hochberg, LR and Henderson, JM and Shahlaie, K and Stavisky, SD and Brandman, DM}, title = {An Accurate and Rapidly Calibrating Speech Neuroprosthesis.}, journal = {The New England journal of medicine}, volume = {391}, number = {7}, pages = {609-618}, pmid = {39141853}, issn = {1533-4406}, support = {U01DC17844/DC/NIDCD NIH HHS/United States ; AL220043//Congressionally Directed Medical Research Programs/ ; U01 DC019430/DC/NIDCD NIH HHS/United States ; R01 MH060974/MH/NIMH NIH HHS/United States ; 872146SPI//Simons Foundation/ ; A2295-R//U.S. Department of Veterans Affairs/ ; DP2DC021055/DC/NIDCD NIH HHS/United States ; U01DC019430/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; Male ; Middle Aged ; *Amyotrophic Lateral Sclerosis/complications/rehabilitation ; *Brain-Computer Interfaces ; Calibration ; Communication Devices for People with Disabilities ; *Dysarthria/rehabilitation/etiology ; Electrodes, Implanted ; Microelectrodes ; Quadriplegia/etiology/rehabilitation ; *Speech ; }, abstract = {BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.<br><br>METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.<br><br>RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.<br><br>CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).}, }
@article {pmid39140323, year = {2024}, author = {Nakamura, T and He, X and Hattori, N and Hida, E and Hirata, M}, title = {Dilemma in patients with amyotrophic lateral sclerosis and expectations from brain-machine interfaces.}, journal = {Annals of medicine}, volume = {56}, number = {1}, pages = {2386516}, pmid = {39140323}, issn = {1365-2060}, mesh = {Humans ; *Amyotrophic Lateral Sclerosis/psychology/therapy ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; Aged ; Surveys and Questionnaires ; *Motivation ; *Caregivers/psychology ; *Anxiety/psychology/etiology ; Adult ; Tracheostomy ; Caregiver Burden/psychology ; Locked-In Syndrome/psychology ; }, abstract = {OBJECTIVE: We hypothesized that patients with amyotrophic lateral sclerosis (ALS) face a dilemma between motivation to live and difficulty in living, and brain-machine interfaces (BMIs) can reduce this dilemma. This study aimed to investigate the present situation of patients with ALS and their expectations from BMIs.<br><br>MATERIALS AND METHODS: Our survey design consisted of an anonymous mail-in questionnaire comprising questions regarding the use of tracheostomy positive pressure ventilation (TPPV), motivation to live, anxiety about the totally locked-in state (TLS), anxiety about caregiver burden, and expectations regarding the use of BMI. Primary outcomes were scores for motivation to live and anxiety about caregiver burden and the TLS. Outcomes were evaluated using the visual analogue scale.<br><br>RESULTS: Among 460 participants, 286 (62.6%) were already supported by or had decided to use TPPV. The median scores for motivation to live, anxiety about TLS, and anxiety about caregiver burden were 8.0, 9.0, and 7.0, respectively. Overall, 49% of patients intended to use BMI. Among patients who had refused TPPV, 15.9% intended to use BMI and TPPV. Significant factors for the use of BMI were motivation to live (p = .003), anxiety about TLS (p < .001), younger age (p < .001), and advanced disease stage (p < .001).<br><br>CONCLUSIONS: These results clearly revealed a serious dilemma among patients with ALS between motivation to live and their anxiety about TLS and caregiver burden. Patients expected BMI to reduce this dilemma. Thus, the development of better BMIs may meet these expectations.}, }
@article {pmid39139629, year = {2024}, author = {Liu, X and Mu, J and Pang, M and Fan, X and Zhou, Z and Guo, F and Yu, K and Yu, H and Ming, D}, title = {A Male Patient with Hydrocephalus via Multimodality Diagnostic Approaches: A Case Report.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {5}, number = {}, pages = {0135}, pmid = {39139629}, issn = {2692-7632}, abstract = {Introduction: Idiopathic normal pressure hydrocephalus (iNPH) is a kind of hydrocephalus that is easily to be misdiagnosed with brain atrophy due to the similarity of ventricular dilation and cognitive impairment. In this case, we present an old male patient who was diagnosed with iNPH by multimodality approaches. Outcomes: A 68-year-old male patient, with deteriorated gait, declined cognitive function for at least 3 years and urinary incontinence for 3 months. The doctors suspected him a patient with hydrocephalus or Alzheimer's disease based on his symptoms. We used multimodality diagnostic approaches including brain imaging, cerebrospinal fluid tap test, continuous intracranial pressure monitoring, and infusion study to make the final diagnosis of iNPH. He underwent ventriculoperitoneal shunt surgery and was well recovered. Conclusion: This case demonstrates the efficacy of using multimodality approaches for iNPH diagnosis, which saves patient time and clinical cost, worthy of further promotion.}, }
@article {pmid39138452, year = {2024}, author = {Starke, G and Akmazoglu, TB and Colucci, A and Vermehren, M and van Beinum, A and Buthut, M and Soekadar, SR and Bublitz, C and Chandler, JA and Ienca, M}, title = {Qualitative studies involving users of clinical neurotechnology: a scoping review.}, journal = {BMC medical ethics}, volume = {25}, number = {1}, pages = {89}, pmid = {39138452}, issn = {1472-6939}, support = {HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Qualitative Research ; Self Concept ; }, abstract = {BACKGROUND: The rise of a new generation of intelligent neuroprostheses, brain-computer interfaces (BCI) and adaptive closed-loop brain stimulation devices hastens the clinical deployment of neurotechnologies to treat neurological and neuropsychiatric disorders. However, it remains unclear how these nascent technologies may impact the subjective experience of their users. To inform this debate, it is crucial to have a solid understanding how more established current technologies already affect their users. In recent years, researchers have used qualitative research methods to explore the subjective experience of individuals who become users of clinical neurotechnology. Yet, a synthesis of these more recent findings focusing on qualitative methods is still lacking.<br><br>METHODS: To address this gap in the literature, we systematically searched five databases for original research articles that investigated subjective experiences of persons using or receiving neuroprosthetics, BCIs or neuromodulation with qualitative interviews and raised normative questions.<br><br>RESULTS: 36 research articles were included and analysed using qualitative content analysis. Our findings synthesise the current scientific literature and reveal a pronounced focus on usability and other technical aspects of user experience. In parallel, they highlight a relative neglect of considerations regarding agency, self-perception, personal identity and subjective experience.<br><br>CONCLUSIONS: Our synthesis of the existing qualitative literature on clinical neurotechnology highlights the need to expand the current methodological focus as to investigate also non-technical aspects of user experience. Given the critical role considerations of agency, self-perception and personal identity play in assessing the ethical and legal significance of these technologies, our findings reveal a critical gap in the existing literature. This review provides a comprehensive synthesis of the current qualitative research landscape on neurotechnology and the limitations thereof. These findings can inform researchers on how to study the subjective experience of neurotechnology users more holistically and build patient-centred neurotechnology.}, }
@article {pmid39138070, year = {2024}, author = {Zhou, S and Chen, W and Yang, H}, title = {Dopamine.}, journal = {Trends in endocrinology and metabolism: TEM}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tem.2024.07.005}, pmid = {39138070}, issn = {1879-3061}, }
@article {pmid39137009, year = {2024}, author = {Denis-Robichaud, J and Rees, EE and Daley, P and Zarowsky, C and Diouf, A and Nasri, BR and de Montigny, S and Carabin, H}, title = {Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada.}, journal = {Journal of medical Internet research}, volume = {26}, number = {}, pages = {e51325}, pmid = {39137009}, issn = {1438-8871}, mesh = {Humans ; *COVID-19/prevention &amp; control ; Cross-Sectional Studies ; Canada ; *Social Media/statistics &amp; numerical data ; Adult ; Male ; Female ; *Public Health ; Middle Aged ; Bayes Theorem ; Young Adult ; Masks/statistics &amp; numerical data ; Aged ; SARS-CoV-2 ; Surveys and Questionnaires ; Adolescent ; Patient Compliance/statistics &amp; numerical data ; Self Report ; Vaccination/statistics &amp; numerical data ; }, abstract = {BACKGROUND: The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised.<br><br>OBJECTIVE: The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter.<br><br>METHODS: Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination.<br><br>RESULTS: Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users.<br><br>CONCLUSIONS: Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.}, }
@article {pmid39136271, year = {2024}, author = {Tong, L and Zhang, D and Huang, Z and Gao, F and Zhang, S and Chen, F and Liu, C}, title = {Calcium Ion-Coupled Polyphosphates with Different Degrees of Polymerization for Bleeding Control.}, journal = {ACS applied materials &amp; interfaces}, volume = {16}, number = {33}, pages = {43244-43256}, doi = {10.1021/acsami.4c06698}, pmid = {39136271}, issn = {1944-8252}, mesh = {Animals ; *Polyphosphates/chemistry/pharmacology ; *Calcium/chemistry ; Rats ; *Hemorrhage/prevention &amp; control/drug therapy ; *Polymerization ; Hemostatics/chemistry/pharmacology ; Blood Coagulation/drug effects ; Rats, Sprague-Dawley ; Male ; Hemostasis/drug effects ; Ions/chemistry ; }, abstract = {The development of efficient hemostatic materials is crucial for achieving rapid hemorrhage control and effective wound healing. Inorganic polyphosphate (polyP) is recognized as an effective modulator of the blood coagulation process. However, the specific effect of polyP chain length on coagulation is not yet fully understood. Furthermore, calcium ions (Ca[2+]) are essential for the coagulation process, promoting multiple enzyme-catalyzed reactions within the coagulation cascade. Hence, calcium ion-coupled polyphosphate powders with three different degrees of polymerization (CaPP-n, n = 20, 50, and 1500) are synthesized by an ion-exchange reaction. CaPP exhibits a crystalline phase at a low polymerization degree and transitions to an amorphous phase as the polymerization degree increases. Notably, the addition of Ca[2+] enhances the wettability of polyP, and CaPP promotes hemostasis, with varying degrees of effectiveness related to chain length. CaPP-50 exhibits the most promising hemostatic performance, with the lowest blood clotting index (BCI, 12.1 ± 0.7%) and the shortest clotting time (302.0 ± 10.5 s). By combining Ca[2+] with polyP of medium-chain length, CaPP-50 demonstrates an enhanced ability to accelerate the adhesion and activation of blood cells, initiate the intrinsic coagulation cascade, and form a stable blood clot, outperforming both CaPP-20 and CaPP-1500. The hemostatic efficacy of CaPP-50 is further validated using rat liver bleeding and femoral artery puncture models. CaPP-50 is proven to possess hemostatic properties comparable to those of commercial calcium-based zeolite hemostatic powder and superior to kaolin. In addition, CaPP-50 exhibits excellent biocompatibility and long-term storage stability. These results suggest that CaPP-50 has significant clinical and commercial potential as an active inorganic hemostatic agent for rapid control of bleeding.}, }
@article {pmid39135208, year = {2024}, author = {Liu, XY and Song, X and Czosnyka, M and Robba, C and Czosnyka, Z and Summers, JL and Yu, HJ and Gao, GY and Smielewski, P and Guo, F and Pang, MJ and Ming, D}, title = {Congenital hydrocephalus: a review of recent advances in genetic etiology and molecular mechanisms.}, journal = {Military Medical Research}, volume = {11}, number = {1}, pages = {54}, pmid = {39135208}, issn = {2054-9369}, support = {2021YFF1200602//National Key Technologies Research and Development Program/ ; }, mesh = {Humans ; *Hydrocephalus/genetics/etiology ; Animals ; Genetic Predisposition to Disease ; }, abstract = {The global prevalence rate for congenital hydrocephalus (CH) is approximately one out of every five hundred births with multifaceted predisposing factors at play. Genetic influences stand as a major contributor to CH pathogenesis, and epidemiological evidence suggests their involvement in up to 40% of all cases observed globally. Knowledge about an individual's genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making processes. However, the precise genetic etiology has only been pinpointed in fewer than 5% of human instances. More occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci. A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain disorder. This review provides a summary of pertinent genes identified through gene sequencing technologies in humans, in addition to the 4 genes currently associated with CH (two X-linked genes L1CAM and AP1S2, two autosomal recessive MPDZ and CCDC88C). Others predominantly participate in aqueduct abnormalities, ciliary movement, and nervous system development. The prospective CH-related genes revealed through animal model gene-editing techniques are further outlined, focusing mainly on 4 pathways, namely cilia synthesis and movement, ion channels and transportation, Reissner's fiber (RF) synthesis, cell apoptosis, and neurogenesis. Notably, the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid (CSF) circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this condition. So far, only a limited number of CH-associated genes have been identified in humans. The integration of genotype and phenotype for disease diagnosis represents a new trend in the medical field. Animal models provide insights into the pathogenesis of CH and contribute to our understanding of its association with related complications, such as renal cysts, scoliosis, and cardiomyopathy, as these genes may also play a role in the development of these diseases. Genes discovered in animals present potential targets for new treatments but require further validation through future human studies.}, }
@article {pmid39134592, year = {2024}, author = {Krueger, J and Krauth, R and Reichert, C and Perdikis, S and Vogt, S and Huchtemann, T and Dürschmid, S and Sickert, A and Lamprecht, J and Huremovic, A and Görtler, M and Nasuto, SJ and Tsai, IC and Knight, RT and Hinrichs, H and Heinze, HJ and Lindquist, S and Sailer, M and Millán, JDR and Sweeney-Reed, CM}, title = {Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {18700}, pmid = {39134592}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Neuronal Plasticity ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Stroke/physiopathology/complications ; Aged ; *Electroencephalography ; *Recovery of Function ; *Motor Cortex/physiopathology ; *Transcranial Magnetic Stimulation/methods ; *Evoked Potentials, Motor ; }, abstract = {Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain-computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.}, }
@article {pmid39134021, year = {2024}, author = {Bashford, L and Rosenthal, IA and Kellis, S and Bjånes, D and Pejsa, K and Brunton, BW and Andersen, RA}, title = {Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {39134021}, issn = {1741-2552}, support = {R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; }, mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Movement/physiology ; *Parietal Lobe/physiology ; Middle Aged ; Single-Case Studies as Topic ; }, abstract = {Objective.A crucial goal in brain-machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data.Approach.Over a period of 1106 and 871 d respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus).Main results.We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans.Significance.These results can be practically applied to significantly expand the longevity and generalizability of brain-computer interfaces.Clinical TrialsNCT01849822, NCT01958086, NCT01964261.}, }
@article {pmid39133845, year = {2024}, author = {Wong, MMK and Sha, Z and Lütje, L and Kong, XZ and van Heukelum, S and van de Berg, WDJ and Jonkman, LE and Fisher, SE and Francks, C}, title = {The neocortical infrastructure for language involves region-specific patterns of laminar gene expression.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {34}, pages = {e2401687121}, pmid = {39133845}, issn = {1091-6490}, support = {N/A//Max Planck Instituut voor Psycholingu&#xef;stiek (MAX-PLANCK-INSTITUT)/ ; 024.001.006//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)/ ; }, mesh = {Humans ; *Neocortex/metabolism ; *Language ; Temporal Lobe/metabolism ; Male ; Female ; Schizophrenia/genetics/metabolism ; Neurons/metabolism ; Frontal Lobe/metabolism ; Transcriptome ; Adult ; }, abstract = {The language network of the human brain has core components in the inferior frontal cortex and superior/middle temporal cortex, with left-hemisphere dominance in most people. Functional specialization and interconnectivity of these neocortical regions is likely to be reflected in their molecular and cellular profiles. Excitatory connections between cortical regions arise and innervate according to layer-specific patterns. Here, we generated a gene expression dataset from human postmortem cortical tissue samples from core language network regions, using spatial transcriptomics to discriminate gene expression across cortical layers. Integration of these data with existing single-cell expression data identified 56 genes that showed differences in laminar expression profiles between the frontal and temporal language cortex together with upregulation in layer II/III and/or layer V/VI excitatory neurons. Based on data from large-scale genome-wide screening in the population, DNA variants within these 56 genes showed set-level associations with interindividual variation in structural connectivity between the left-hemisphere frontal and temporal language cortex, and with the brain-related disorders dyslexia and schizophrenia which often involve affected language. These findings identify region-specific patterns of laminar gene expression as a feature of the brain's language network.}, }
@article {pmid39133589, year = {2024}, author = {Zhang, Z and He, Y and Mai, W and Luo, Y and Li, X and Cheng, Y and Huang, X and Lin, R}, title = {Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2024.3437676}, pmid = {39133589}, issn = {2162-2388}, abstract = {The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms.}, }
@article {pmid39133582, year = {2024}, author = {He, C and Chen, YY and Phang, CR and Chen, IP and Tzou, SC and Jung, TP and Ko, LW}, title = {Exploring Embodied Cognition and Brain Dynamics Under Multi-Tasks Target Detection in Immerse Projector-Based Augmented Reality (IPAR) Scenarios.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3476-3485}, doi = {10.1109/TNSRE.2024.3442241}, pmid = {39133582}, issn = {1558-0210}, mesh = {Humans ; Male ; *Electroencephalography ; Female ; *Cognition/physiology ; *Augmented Reality ; Adult ; Young Adult ; *Walking/physiology ; Brain/physiology ; Multitasking Behavior/physiology ; Standing Position ; Wireless Technology ; Attention/physiology ; Healthy Volunteers ; Theta Rhythm/physiology ; Beta Rhythm/physiology ; Brain-Computer Interfaces ; }, abstract = {Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.}, }
@article {pmid39132484, year = {2024}, author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, NG and Downey, JE and Chase, SM and Boninger, ML and Dekleva, BD and Collinger, JL}, title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39132484}, support = {R01 NS121079/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, abstract = {The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g., reaching vs. wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.}, }
@article {pmid39131629, year = {2024}, author = {Huang, Z and Zhang, D and Tong, L and Gao, F and Zhang, S and Wang, X and Xie, Y and Chen, F and Liu, C}, title = {Protonated-chitosan sponge with procoagulation activity for hemostasis in coagulopathy.}, journal = {Bioactive materials}, volume = {41}, number = {}, pages = {174-192}, pmid = {39131629}, issn = {2452-199X}, abstract = {Hemostatic materials are essential for managing acute bleeding in medical settings. Chitosan (CS) shows promise in hemostasis but its underlying mechanism remains incompletely understood. We unexpectedly discovered that certain protonated-chitosan (PCS) rapidly assembled plasma proteins to form protein membrane (PM) upon contact with platelet-poor plasma (PPP). We hypothesized that the novel observation was intricately related to the procoagulant effect of chitosan. Herein, the study aimed to elucidate the conditions necessary and mechanism for PM formation, identify the proteins within the PM and PCS's procoagulant action at the molecule levels. We confirmed that the amount of -NH3 [+] groups (>4.9 mmol/g) on PCS molecules played a crucial role in promoting coagulation. The -NH3 [+] group interacted with blood's multiple active components to exert hemostatic effects: assembling plasma proteins including coagulation factors such as FII, FV, FX, activating blood cells and promoting the secretion of coagulation-related substances (FV, ADP, etc) by platelets. Notably, the hemostatic mechanism can be extended to protonated-chitosan derivatives like quaternized, alkylated, and catechol-chitosan. In the blood clotting index (BCI) experiment, compared to other groups, PCS95 achieved the lowest BCI value (∼6 %) within 30 s. Protonated-chitosan exhibited excellent biocompatibility and antibacterial properties, with PCS95 demonstrating inhibition effectiveness of over 95 % against Escherichia coli (E.coil) and Staphylococcus aureus (S. aureus). Moreover, PCS performed enhanced hemostatic effectiveness over chitosan-based commercially agents (Celox™ and ChitoGauze®XR) in diverse bleeding models. In particular, PCS95 reduced bleeding time by 70 % in rabbit models of coagulopathy. Overall, this study investigated the coagulation mechanism of materials at the molecular level, paving the way for innovative approaches in designing new hemostatic materials.}, }
@article {pmid39131333, year = {2024}, author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, JL and Hatsopoulos, NG and Downey, JE}, title = {How different immersive environments affect intracortical brain computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39131333}, issn = {2692-8205}, support = {R01 NS130302/NS/NINDS NIH HHS/United States ; T32 NS121763/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; }, abstract = {As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, either viewed immersively through virtual reality goggles or at a distance on a flat television monitor. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.}, }
@article {pmid39130131, year = {2024}, author = {Ferro, MD and Proctor, CM and Gonzalez, A and Jayabal, S and Zhao, E and Gagnon, M and Slézia, A and Pas, J and Dijk, G and Donahue, MJ and Williamson, A and Raymond, J and Malliaras, GG and Giocomo, L and Melosh, NA}, title = {NeuroRoots, a bio-inspired, seamless brain machine interface for long-term recording in delicate brain regions.}, journal = {AIP advances}, volume = {14}, number = {8}, pages = {085109}, pmid = {39130131}, issn = {2158-3226}, support = {R56 MH106475/MH/NIMH NIH HHS/United States ; R01 MH106475/MH/NIMH NIH HHS/United States ; R01 NS072406/NS/NINDS NIH HHS/United States ; R01 DC004154/DC/NIDCD NIH HHS/United States ; R21 NS104861/NS/NINDS NIH HHS/United States ; R21 EY026365/EY/NEI NIH HHS/United States ; }, abstract = {Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain-machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain-machine interfacing.}, }
@article {pmid39127752, year = {2024}, author = {Wei, W and Wang, K and Qiu, S and He, H}, title = {A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {867}, pmid = {39127752}, issn = {2052-4463}, mesh = {Humans ; Male ; Attention ; *Brain-Computer Interfaces ; Electrocardiography ; *Electroencephalography ; Electromyography ; Electrooculography ; *Evoked Potentials, Visual ; Eye Movements ; Female ; Young Adult ; Adult ; }, abstract = {Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.}, }
@article {pmid39124036, year = {2024}, author = {Kabir, MH and Akhtar, NI and Tasnim, N and Miah, ASM and Lee, HS and Jang, SW and Shin, J}, title = {Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {15}, pages = {}, pmid = {39124036}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; }, abstract = {The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.}, }
@article {pmid39123966, year = {2024}, author = {Zhou, S and Zhang, P and Chen, H}, title = {Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {15}, pages = {}, pmid = {39123966}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; Cluster Analysis ; *Brain-Computer Interfaces ; Epilepsy/diagnosis/physiopathology ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; }, abstract = {Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.}, }
@article {pmid39120996, year = {2024}, author = {Li, Y and Zhu, X and Qi, Y and Wang, Y}, title = {Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {39120996}, issn = {2050-084X}, support = {no. 62336007//National Natural Science Foundation of China/ ; no. 2022C03011//Key Research and Development Program of Zhejiang/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; }, mesh = {*Motor Cortex/physiology ; Animals ; *Macaca mulatta/physiology ; *Neurons/physiology ; Behavior, Animal/physiology ; Male ; Movement/physiology ; }, abstract = {In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.}, }
@article {pmid39120991, year = {2024}, author = {He, X and Allison, BZ and Qin, K and Liang, W and Wang, X and Cichocki, A and Jin, J}, title = {Leveraging Transfer Superposition Theory for Stable-State Visual Evoked Potential Cross-Subject Frequency Recognition.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {11}, pages = {3071-3084}, doi = {10.1109/TBME.2024.3406603}, pmid = {39120991}, issn = {1558-2531}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Young Adult ; Female ; }, abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the source-subject transfer mode, the global transfer mode, and the sine-cosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.}, }
@article {pmid39119268, year = {2024}, author = {Li, P and Kim, S and Tian, B}, title = {Beyond 25 years of biomedical innovation in nano-bioelectronics.}, journal = {Device}, volume = {2}, number = {7}, pages = {}, pmid = {39119268}, issn = {2666-9986}, support = {R56 EB034289/EB/NIBIB NIH HHS/United States ; }, abstract = {Nano-bioelectronics, which blend the precision of nanotechnology with the complexity of biological systems, are evolving with innovations such as silicon nanowires, carbon nanotubes, and graphene. These elements serve applications from biochemical sensing to brain-machine interfacing. This review examines nano-bioelectronics' role in advancing biomedical interventions and discusses their potential in environmental monitoring, agricultural productivity, energy efficiency, and creative fields. The field is transitioning from molecular to ecosystem-level applications, with research exploring complex cellular mechanisms and communication. This fosters understanding of biological interactions at various levels, such as suggesting transformative approaches for ecosystem management and food security. Future research is expected to focus on refining nano-bioelectronic devices for integration with biological systems and on scalable manufacturing to broaden their reach and functionality.}, }
@article {pmid39117588, year = {2024}, author = {Cassinadri, G and Ienca, M}, title = {Non-voluntary BCI explantation: assessing possible neurorights violations in light of contrasting mental ontologies.}, journal = {Journal of medical ethics}, volume = {}, number = {}, pages = {}, doi = {10.1136/jme-2023-109830}, pmid = {39117588}, issn = {1473-4257}, abstract = {In research involving patients with implantable brain-computer interfaces (BCIs), there is a regulatory gap concerning post-trial responsibilities and duties of sponsors and investigators towards implanted patients. In this article, we analyse the case of patient R, who underwent non-voluntary explantation of an implanted BCI, causing a discontinuation in her sense of agency and self. To clarify the post-trial duties and responsibilities involved in this case, we first define the ontological status of the BCI using both externalist (EXT) and internalist (INT) theories of cognition. We then give particular focus to the theories of extended and embedded cognition, hence considering the BCI either as a constitutive component of the patient's mind or as a causal supporter of her brain-based cognitive capacities. We argue that patient R can legitimately be considered both as an embedded and extended cognitive agent. Then, we analyse whether the non-voluntary explantation violated patient R's (neuro)rights to cognitive liberty, mental integrity, psychological continuity and mental privacy. We analyse whether and how different mental ontologies may imply morally relevant differences in interpreting these prima facie neurorights violations and the correlational duties of sponsors and investigators. We conclude that both mental ontologies support the identification of emerging neurorights of the patient and give rise to post-trial obligations of sponsors and investigators to provide for continuous technical maintenance of implanted BCIs that play a significant role in patients' agency and sense of self. However, we suggest that externalist mental ontologies better capture patient R's self-conception and support the identification of a more granular form of mental harm and associated neurorights violation, thus eliciting stricter post-trial obligations.}, }
@article {pmid39116892, year = {2024}, author = {Xiong, H and Li, J and Liu, J and Song, J and Han, Y}, title = {Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6cf5}, pmid = {39116892}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Deep Learning ; Wavelet Analysis ; }, abstract = {Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.}, }
@article {pmid39116878, year = {2024}, author = {Wei, B and Cheng, G and Bi, Q and Lu, C and Sun, Q and Li, L and Chen, N and Hu, M and Lu, H and Xu, X and Mao, G and Wan, S and Hu, Z and Gu, Y and Zheng, J and Zhao, L and Shen, XZ and Liu, X and Shi, P}, title = {Microglia in the hypothalamic paraventricular nucleus sense hemodynamic disturbance and promote sympathetic excitation in hypertension.}, journal = {Immunity}, volume = {57}, number = {9}, pages = {2030-2042.e8}, doi = {10.1016/j.immuni.2024.07.011}, pmid = {39116878}, issn = {1097-4180}, mesh = {*Paraventricular Hypothalamic Nucleus/metabolism ; Animals ; *Microglia/metabolism ; *Hypertension/physiopathology ; Mice ; *Hemodynamics ; *Sympathetic Nervous System/physiopathology ; Male ; Mice, Inbred C57BL ; Adenosine Triphosphate/metabolism ; Receptors, Purinergic P2Y12/metabolism ; Inflammation/immunology ; Blood Pressure ; Neurons/metabolism ; }, abstract = {Hypertension is usually accompanied by elevated sympathetic tonicity, but how sympathetic hyperactivity is triggered is not clear. Recent advances revealed that microglia-centered neuroinflammation contributes to sympathetic excitation in hypertension. In this study, we performed a temporospatial analysis of microglia at both morphological and transcriptomic levels and found that microglia in the hypothalamic paraventricular nucleus (PVN), a sympathetic center, were early responders to hypertensive challenges. Vasculature analyses revealed that the PVN was characterized by high capillary density, thin vessel diameter, and complex vascular topology relative to other brain regions. As such, the PVN was susceptible to the penetration of ATP released from the vasculature in response to hemodynamic disturbance after blood pressure increase. Mechanistically, ATP ligation to microglial P2Y12 receptor was responsible for microglial inflammatory activation and the eventual sympathetic overflow. Together, these findings identified a distinct vasculature pattern rendering vulnerability of PVN pre-sympathetic neurons to hypertension-associated microglia-mediated inflammatory insults.}, }
@article {pmid39116374, year = {2024}, author = {Aboubakr, O and Houillier, C and Alentorn, A and Choquet, S and Dupont, S and Mokhtari, K and Leclercq, D and Nichelli, L and Kas, A and Rozenblum, L and Le Garff-Tavernier, M and Hoang-Xuan, K and Carpentier, A and Mathon, B}, title = {Epilepsy in Patients With Primary CNS Lymphoma: Prevalence, Risk Factors, and Prognostic Significance.}, journal = {Neurology}, volume = {103}, number = {5}, pages = {e209748}, doi = {10.1212/WNL.0000000000209748}, pmid = {39116374}, issn = {1526-632X}, mesh = {Humans ; Male ; Female ; Aged ; Retrospective Studies ; Middle Aged ; Risk Factors ; Prevalence ; Prognosis ; *Epilepsy/epidemiology ; *Central Nervous System Neoplasms/epidemiology/complications ; Lymphoma/epidemiology/complications ; Adult ; Aged, 80 and over ; }, abstract = {BACKGROUND AND OBJECTIVES: Epilepsy is a common comorbidity of brain tumors; however, little is known about the prevalence, onset time, semiology, and risk factors of seizures in primary CNS lymphoma (PCNSL). Our objectives were to determine the prevalence of epilepsy in PCNSL, to identify factors associated with epilepsy, and to investigate the prognostic significance of seizures in PCNSL.<br><br>METHODS: We performed an observational, retrospective single-center study at a tertiary neuro-oncology center (2011-2023) including immunocompetent patients with PCNSL and no history of seizures. We collected clinical, imaging, and treatment data; seizure status over the course of PCNSL; and oncological and seizure outcome. The primary outcome was to determine the prevalence of epilepsy. Furthermore, we aimed to identify clinical, radiologic, and treatment-related factors associated with epilepsy. Univariate analyses were conducted using the &#x3c7;[2] test for categorical variables and unpaired t test for continuous variables. Predictors identified in the unadjusted analysis were included in backward stepwise logistic regression models.<br><br>RESULTS: We included 330 patients, 157 (47.6%) were male, median age at diagnosis was 68 years, and the median Karnofsky Performance Status score was 60. Eighty-three (25.2%) patients had at least 1 seizure from initial diagnosis to the last follow-up, 40 (12.1%) as the onset symptom, 16 (4.8%) during first line of treatment, 27 (8.2%) at tumor progression and 6 (1.8%) while in remission. Focal aware seizures were the most frequent seizure type, occurring in 43 (51.8%) patients. Seizure freedom under antiseizure medication was observed in 97.6% patients. Cortical contact (odds ratio [OR] 8.6, 95% CI 4.2-15.5, p < 0.001) and a higher proliferation index (OR 5.7, 95% CI 1.3-26.2, p = 0.02) were identified as independent risk factors of epilepsy. Patients with PCNSL and epilepsy had a significantly shorter progression-free survival (median progression-free survival 9.6 vs 14.1 months, adjusted hazard ratio 1.4, 95% CI 1.0-1.9, p = 0.03), but not a significantly shorter overall survival (17 vs 44.1 months, log-rank test, p = 0.09).<br><br>DISCUSSION: Epilepsy affects a quarter of patients with PCNSL, with half experiencing it at the time of initial presentation and potentially serving as a marker of disease progression. Further research is necessary to assess the broader applicability of these findings because they are subject to the constraints of a retrospective design and tertiary center setting.}, }
@article {pmid39116252, year = {2024}, author = {Chen, M and Ma, S and Liu, H and Dong, Y and Tang, J and Ni, Z and Tan, Y and Duan, C and Li, H and Huang, H and Li, Y and Cao, X and Lingle, CJ and Yang, Y and Hu, H}, title = {Brain region-specific action of ketamine as a rapid antidepressant.}, journal = {Science (New York, N.Y.)}, volume = {385}, number = {6709}, pages = {eado7010}, pmid = {39116252}, issn = {1095-9203}, support = {R35 GM118114/GM/NIGMS NIH HHS/United States ; }, mesh = {Animals ; Male ; Mice ; *Antidepressive Agents/pharmacology ; Brain-Derived Neurotrophic Factor/metabolism/genetics ; *Depression/drug therapy/metabolism ; *Habenula/drug effects/metabolism ; Hippocampus/drug effects/metabolism ; *Ketamine/pharmacology/administration &amp; dosage ; Mice, Inbred C57BL ; Mice, Knockout ; Pyramidal Cells/drug effects/metabolism ; *Receptors, N-Methyl-D-Aspartate/antagonists &amp; inhibitors/genetics ; Serotonin/metabolism ; }, abstract = {Ketamine has been found to have rapid and potent antidepressant activity. However, despite the ubiquitous brain expression of its molecular target, the N-methyl-d-aspartate receptor (NMDAR), it was not clear whether there is a selective, primary site for ketamine's antidepressant action. We found that ketamine injection in depressive-like mice specifically blocks NMDARs in lateral habenular (LHb) neurons, but not in hippocampal pyramidal neurons. This regional specificity depended on the use-dependent nature of ketamine as a channel blocker, local neural activity, and the extrasynaptic reservoir pool size of NMDARs. Activating hippocampal or inactivating LHb neurons swapped their ketamine sensitivity. Conditional knockout of NMDARs in the LHb occluded ketamine's antidepressant effects and blocked the systemic ketamine-induced elevation of serotonin and brain-derived neurotrophic factor in the hippocampus. This distinction of the primary versus secondary brain target(s) of ketamine should help with the design of more precise and efficient antidepressant treatments.}, }
@article {pmid39115894, year = {2025}, author = {Hu, B and Lu, D and Meng, L and Zhang, Y}, title = {When time theft promotes performance: Measure development and validation of time theft motives.}, journal = {The Journal of applied psychology}, volume = {110}, number = {2}, pages = {256-281}, doi = {10.1037/apl0001229}, pmid = {39115894}, issn = {1939-1854}, support = {//National Natural Science Foundation of China/ ; //Shanghai Philosophy and Social Science Planning Project/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; }, mesh = {Humans ; *Motivation ; Adult ; Male ; *Employment/psychology ; Female ; *Psychometrics/instrumentation/standards ; *Work Performance ; Young Adult ; Middle Aged ; }, abstract = {The prevailing viewpoint has long depicted employee time theft as inherently detrimental. However, this perspective may stem from a limited understanding of the underlying motives that drive such behavior. Time theft can paradoxically be motivated by neutral and even laudable intentions, such as promoting work efficiency, thus rendering it potentially beneficial and constructive. Across three mixed-methods studies, we explore the motives behind employee time theft, develop and validate an instrument to assess these motives, and examine how they differentially predict time theft behavior. Specifically, in Study 1, we use a qualitative method and identify 11 types of time theft motives. Study 2 embarks on the development of measures of these motives, subsequently validating their factor structure. Study 3 examines their incremental variance in predicting time theft behavior by controlling for personality and demographic variables. Overall, these studies reveal that employees' engagement in time theft can be driven not solely by self-oriented motives but also by others- and work-oriented motives. Further, each of these motives provides incremental value in understanding time theft behavior. Implications for both research and practice emanating from these findings are also discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).}, }
@article {pmid39115403, year = {2024}, author = {Jeong, E and Seo, M and Kim, KS}, title = {Design of an fNIRS-EEG hybrid terminal for wearable BCI systems.}, journal = {The Review of scientific instruments}, volume = {95}, number = {8}, pages = {}, doi = {10.1063/5.0187070}, pmid = {39115403}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/instrumentation/methods ; *Electroencephalography/instrumentation ; Humans ; *Wearable Electronic Devices ; Equipment Design ; }, abstract = {The importance of brain-computer interfaces (BCI) is increasing, and various methods have been developed. Among the developed BCI methods, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are favored due to their non-invasive feature and compact device sizes. EEG monitors the electrical potentials generated by the activation of neurons, and fNIRS monitors the blood flow also generated by neurons, resulting in signals with different properties between the two methods. As the two BCI methods greatly differ in the characteristics of the acquired neural activity signals, for cases of estimating the intention or thought of a subject by BCI, it has been proven that further accurate information may be extracted by utilizing both methods simultaneously. Both systems are powered by electricity, and as EEG systems are greatly sensitive to electrical noises, application of two separate fNIRS and EEG systems together may result in electrical interference as the systems are required to be in contact with the skin and stray currents from the fNIRS system may flow along the surface of the skin into the EEG system. This research proposes a wearable fNIRS-EEG hybrid BCI system, where a single terminal is capable of operating both as a continuous wave fNIRS emitter and as a detector, and also as an EEG electrode. The system has been designed such that the fNIRS and EEG components are electrically separated to avoid electrical interference between each other. It is expected that by utilizing the developed fNIRS-EEG hybrid terminals, the development of BCI analysis may be further accelerated in various fields.}, }
@article {pmid39114591, year = {2024}, author = {Berkmush-Antipova, A and Syrov, N and Yakovlev, L and Miroshnikov, A and Golovanov, F and Shusharina, N and Kaplan, A}, title = {Yes or no? A study of ErrPs in the "guess what I am thinking" paradigm with stimuli of different visual content.}, journal = {Frontiers in psychology}, volume = {15}, number = {}, pages = {1394496}, pmid = {39114591}, issn = {1664-1078}, abstract = {Error-related potentials (ErrPs) have attracted attention in part because of their practical potential for building brain-computer interface (BCI) paradigms. BCIs, facilitating direct communication between the brain and machines, hold great promise for brain-AI interaction. Therefore, a comprehensive understanding of ErrPs is crucial to ensure reliable BCI outcomes. In this study, we investigated ErrPs in the context of the "guess what I am thinking" paradigm. 23 healthy participants were instructed to imagine an object from a predetermined set, while an algorithm randomly selected another object that was either the same as or different from the imagined object. We recorded and analyzed the participants' EEG activity to capture their mental responses to the algorithm's "predictions". The study identified components distinguishing correct from incorrect responses. It discusses their nature and how they differ from ErrPs extensively studied in other BCI paradigms. We observed pronounced variations in the shape of ErrPs across different stimulus sets, underscoring the significant influence of visual stimulus appearance on ErrP peaks. These findings have implications for designing effective BCI systems, especially considering the less conventional BCI paradigm employed. They emphasize the necessity of accounting for stimulus factors in BCI development.}, }
@article {pmid39113778, year = {2024}, author = {Bartkowiak, J and Agarwal, V and Lebehn, M and Nazif, TM and George, I and Kodali, SK and Vahl, TP and Hahn, RT}, title = {Strain assessment in patients with aortic regurgitation undergoing transcatheter aortic valve implantation: case series.}, journal = {European heart journal. Case reports}, volume = {8}, number = {8}, pages = {ytae261}, pmid = {39113778}, issn = {2514-2119}, abstract = {BACKGROUND: Limited data exist on strain changes after transcatheter aortic valve implantation (TAVI) in patients with aortic regurgitation (AR).<br><br>CASE SUMMARY: Three patients with AR undergoing TAVI showed an initial reduction in global longitudinal strain (GLS), followed by sustained GLS improvement within the first year.<br><br>DISCUSSION: Findings align with those of surgically treated patients with AR. There is a possible superiority of GLS to left ventricular end-diastolic diameter ratio in assessing patients with severe volume overload.}, }
@article {pmid39112576, year = {2024}, author = {Naddaf, M and Drew, L}, title = {Second brain implant by Elon Musk's Neuralink: will it fare better than the first?.}, journal = {Nature}, volume = {632}, number = {8025}, pages = {481-482}, doi = {10.1038/d41586-024-02368-8}, pmid = {39112576}, issn = {1476-4687}, mesh = {Humans ; *Brain/surgery ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Spinal Cord Injuries/surgery/therapy ; Treatment Outcome ; }, }
@article {pmid39112461, year = {2024}, author = {Qiu, L and Liang, C and Kochunov, P and Hutchison, KE and Sui, J and Jiang, R and Zhi, D and Vergara, VM and Yang, X and Zhang, D and Fu, Z and Bustillo, JR and Qi, S and Calhoun, VD}, title = {Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {326}, pmid = {39112461}, issn = {2158-3188}, support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; 62376124//National Natural Science Foundation of China (National Science Foundation of China)/ ; RF1 MH123163/MH/NIMH NIH HHS/United States ; R01 AA012207/AA/NIAAA NIH HHS/United States ; BK20220889//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; S10 OD023696/OD/NIH HHS/United States ; R01MH118695//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01 MH118695/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; Female ; Male ; Adult ; *Psychotic Disorders/diagnostic imaging ; *Tobacco Use/adverse effects ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Young Adult ; Depressive Disorder, Major/diagnostic imaging ; Middle Aged ; Multimodal Imaging ; Alcohol Drinking/adverse effects ; Neuroimaging ; Adolescent ; Autism Spectrum Disorder/diagnostic imaging ; }, abstract = {People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol and tobacco use. However, the further associations between alcohol/tobacco use and symptoms/cognition in these disorders remain unexplored. We identified multimodal brain networks involving alcohol use (n = 707) and tobacco use (n = 281) via supervised multimodal fusion and evaluated if these networks affected symptoms and cognition in people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n = 178/134/143), depressive (major depressive disorder, n = 260) and developmental (autism spectrum disorder/attention deficit hyperactivity disorder, n = 421/346) disorders. Alcohol and tobacco use scores were used as references to guide functional and structural imaging fusion to identify alcohol/tobacco use associated multimodal patterns. Correlation analyses between the extracted brain features and symptoms or cognition were performed to evaluate the relationships between alcohol/tobacco use with symptoms/cognition in 6 psychiatric disorders. Results showed that (1) the default mode network (DMN) and salience network (SN) were associated with alcohol use, whereas the DMN and fronto-limbic network (FLN) were associated with tobacco use; (2) the DMN and fronto-basal ganglia (FBG) related to alcohol/tobacco use were correlated with symptom and cognition in psychosis; (3) the middle temporal cortex related to alcohol/tobacco use was associated with cognition in depression; (4) the DMN related to alcohol/tobacco use was related to symptom, whereas the SN and limbic system (LB) were related to cognition in developmental disorders. In summary, alcohol and tobacco use were associated with structural and functional abnormalities in DMN, SN and FLN and had significant associations with cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.}, }
@article {pmid39111412, year = {2024}, author = {Tian, H}, title = {Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance.}, journal = {Journal of neuroscience methods}, volume = {411}, number = {}, pages = {110240}, doi = {10.1016/j.jneumeth.2024.110240}, pmid = {39111412}, issn = {1872-678X}, mesh = {Humans ; *Upper Extremity/physiology ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; *Imagination/physiology ; Male ; Adult ; Female ; Deep Learning ; Young Adult ; Electroencephalography/methods ; Neural Networks, Computer ; Algorithms ; }, abstract = {BACKGROUND: Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.<br><br>NEW METHODS: In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.<br><br>Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.<br><br>RESULTS: To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7&#x202f;%.<br><br>CONCLUSION: This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.}, }
@article {pmid39111203, year = {2024}, author = {Suwandjieff, P and Müller-Putz, GR}, title = {EEG Analyses of visual cue effects on executed movements.}, journal = {Journal of neuroscience methods}, volume = {410}, number = {}, pages = {110241}, doi = {10.1016/j.jneumeth.2024.110241}, pmid = {39111203}, issn = {1872-678X}, mesh = {Humans ; *Cues ; Adult ; Young Adult ; Male ; Female ; *Electroencephalography/methods ; Adolescent ; Psychomotor Performance/physiology ; Movement/physiology ; Brain/physiology ; Visual Perception/physiology ; Hand/physiology ; Photic Stimulation/methods ; Motor Activity/physiology ; }, abstract = {BACKGROUND: In electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks.<br><br>NEW METHOD: To address this concern, we introduced four new visual cues (Fade, Rotation, Reference, and Star) and investigated their impact on brain signals. Our objective was to identify a cue that minimizes its influence on brain activity, facilitating cue-effect free classifier training for asynchronous applications, particularly aiding individuals with severe paralysis.<br><br>RESULTS: 22 able-bodied, right-handed participants aged 18-30 performed hand movements upon presentation of the visual cues. Analysis of time-variability between movement onset and cue-aligned data, grand average MRCP, and classification outcomes revealed significant differences among cues. Rotation and Reference cue exhibited favorable results in minimizing temporal variability, maintaining MRCP patterns, and achieving comparable accuracy to self-paced signals in classification.<br><br>Our study contrasts with traditional cue-based paradigms by introducing novel visual cues designed to mitigate unintended neural activity. We demonstrate the effectiveness of Rotation and Reference cue in eliciting consistent and accurate MRCPs during motor tasks, surpassing previous methods in achieving precise timing and high discriminability for classifier training.<br><br>CONCLUSIONS: Precision in cue timing is crucial for training classifiers, where both Rotation and Reference cue demonstrate minimal variability and high discriminability, highlighting their potential for accurate classifications in online scenarios. These findings offer promising avenues for refining brain-computer interface systems, particularly for individuals with motor impairments, by enabling more reliable and intuitive control mechanisms.}, }
@article {pmid39110623, year = {2024}, author = {Sun, Y and Liang, L and Li, Y and Chen, X and Gao, X}, title = {Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface.}, journal = {GigaScience}, volume = {13}, number = {}, pages = {}, pmid = {39110623}, issn = {2047-217X}, support = {U2241208//National Natural Science Foundation of China/ ; 2023YFF1205300//National Key Research and Development Program of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual ; *Electroencephalography ; Male ; Female ; Adult ; Young Adult ; Algorithms ; }, abstract = {BACKGROUND: The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field.<br><br>FINDINGS: This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks.<br><br>CONCLUSIONS: The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.}, }
@article {pmid39110554, year = {2025}, author = {Wei, W and Li, X and Qiu, S and He, H}, title = {Preliminary Study on Rapid Serial Visualization Presentation Multi-Class Target EEG Classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {1}, pages = {90-101}, doi = {10.1109/TBME.2024.3439820}, pmid = {39110554}, issn = {1558-2531}, mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Young Adult ; Algorithms ; Brain/physiology ; }, abstract = {OBJECTIVE: Brain-Computer Interface (BCI) provides a direct communication channel between the brain and external devices. After combining with the Rapid Serial Visualization Presentation (RSVP) paradigm, the RSVP-BCI system can be utilized for human vision-based fast information retrieval. Currently only binary classification of single-trial EEG can be achieved, also the research on the multi-class target RSVP is few, which limited information transfer rate and the application scenarios of the system. In this paper, we focus on the RSVP multi-class target image retrieval task that contains two classes of targets for achieving triple classification for RSVP-EEG.<br><br>METHODS: Designed two experiments, each containing two tasks with different task difficulties. We recruited 30 subjects to participate in the experiments, collected EEG data, and made the data publicly available. Moreover, we conducted behavioral analysis, ERP analysis, and proposed a model, MDCNet, for EEG classification to study the feasibility of multi-class target RSVP and the impact of task difficulty.<br><br>RESULTS: The experimental results indicated that (1) RSVP-EEG classification that includes non-target and 2-class target is feasibility; (2) the different targets in the same task will evoke P300 with the same latency and different amplitudes, and the hit rate of the target in EEG classification is positively correlated with its amplitude; (3) the information hidden in the time dimension play an important role in EEG classification; (4) the harder the task is, the latency of P300 is longer.<br><br>CONCLUSION/SIGNIFICANCE: The experimental analysis obtained meaningful results, which provided a theoretical basis for subsequent research.}, }
@article {pmid39110553, year = {2025}, author = {Blanco-Diaz, CF and Serafini, ERDS and Bastos-Filho, T and Dantas, AFOA and Santo, CCDE and Delisle-Rodriguez, D}, title = {A Gait Imagery-Based Brain-Computer Interface With Visual Feedback for Spinal Cord Injury Rehabilitation on Lokomat.}, journal = {IEEE transactions on bio-medical engineering}, volume = {72}, number = {1}, pages = {102-111}, doi = {10.1109/TBME.2024.3440036}, pmid = {39110553}, issn = {1558-2531}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Adult ; Male ; Female ; *Gait/physiology ; *Feedback, Sensory/physiology ; *Imagination/physiology ; Neurofeedback/methods ; Young Adult ; Signal Processing, Computer-Assisted ; Middle Aged ; }, abstract = {OBJECTIVE: Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery-based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms.<br><br>METHODS: As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to (8-12 Hz) and (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms.<br><br>RESULTS: The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI.<br><br>CONCLUSION: The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz.<br><br>SIGNIFICANCE: This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.}, }
@article {pmid39107957, year = {2024}, author = {Xu, P and Liu, Y and Wang, J and Zhang, A and Wang, K and Wang, Z and Fang, Y and Wang, X and Zhang, J}, title = {Gender-specific prognosis models reveal differences in subarachnoid hemorrhage patients between sexes.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {8}, pages = {e14894}, pmid = {39107957}, issn = {1755-5949}, support = {LGD22H090001//Natural Science Foundation of Zhejiang Province/ ; 81870916//National Natural Science Foundation of China/ ; 82071287//National Natural Science Foundation of China/ ; 82201430//National Natural Science Foundation of China/ ; 82271301//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Subarachnoid Hemorrhage/diagnosis ; Male ; Female ; Middle Aged ; Prognosis ; Aged ; *Sex Characteristics ; Adult ; Retrospective Studies ; }, abstract = {BACKGROUND: Subarachnoid hemorrhage (SAH) represents a severe stroke subtype. Our study aims to develop gender-specific prognostic prediction models derived from distinct prognostic factors observed among different-gender patients.<br><br>METHODS: Inclusion comprised SAH-diagnosed patients from January 2014 to March 2016 in our institution. Collected data encompassed patients' demographics, admission severity, treatments, imaging findings, and complications. Three-month post-discharge prognoses were obtained via follow-ups. Analyses assessed gender-based differences in patient information. Key factors underwent subgroup analysis, followed by univariate and multivariate analyses to identify gender-specific prognostic factors and establish/validate gender-specific prognostic models.<br><br>RESULTS: A total of 929 patients, with a median age of 57 (16) years, were analyzed; 372 (40%) were male, and 557 (60%) were female. Differences in age, smoking history, hypertension, aneurysm presence, and treatment interventions existed between genders (p&#x2009;<&#x2009;0.01), yet no disparity in prognosis was noted. Subgroup analysis explored hypertension history, aneurysm presence, and treatment impact, revealing gender-specific variations in these factors' influence on the disease. Screening identified independent prognostic factors: age, SEBES score, admission GCS score, and complications for males; and age, admission GCS score, intraventricular hemorrhage, treatment interventions, symptomatic vasospasm, hydrocephalus, delayed cerebral ischemia, and seizures for females. Evaluation and validation of gender-specific models yielded an AUC of 0.916 (95% CI: 0.878-0.954) for males and 0.914 (95% CI: 0.885-0.944) for females in the ROC curve. Gender-specific prognostic models didn't significantly differ from the overall population-based model (model 3) but exhibited robust discriminative ability and clinical utility.<br><br>CONCLUSION: Variations in baseline and treatment-related factors among genders contribute partly to gender-based prognosis differences. Independent prognostic factors vary by gender. Gender-specific prognostic models exhibit favorable prognostic performance.}, }
@article {pmid39107319, year = {2024}, author = {Luo, Y and Wang, L and Cao, Y and Shen, Y and Gu, Y and Wang, L}, title = {Reduced excitatory activity in the developing mPFC mediates a PVH-to-PVL transition and impaired social cognition in autism spectrum disorders.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {325}, pmid = {39107319}, issn = {2158-3188}, support = {LR21C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 32070975//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071021//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {*Autism Spectrum Disorder/physiopathology ; Animals ; *Prefrontal Cortex/physiopathology/metabolism ; Mice ; *Interneurons/metabolism/physiology ; *Parvalbumins/metabolism ; *Disease Models, Animal ; Male ; *Social Cognition ; Behavior, Animal/physiology ; Social Behavior ; Mice, Inbred C57BL ; Female ; }, abstract = {Understanding the neuropathogenesis of impaired social cognition in autism spectrum disorders (ASD) is challenging. Altered cortical parvalbumin-positive (PV[+]) interneurons have been consistently observed in ASD, but their roles and the underlying mechanisms remain poorly understood. In our study, we observed a downward-shifted spectrum of PV expression in the developing medial prefrontal cortex (mPFC) of ASD mouse models due to decreased activity of PV[+] neurons. Surprisingly, chemogenetically suppressing PV[+] neuron activity during postnatal development failed to induce ASD-like behaviors. In contrast, lowering excitatory activity in the developing mPFC not only dampened the activity state and PV expression of individual PV[+] neurons, but also replicated ASD-like social deficits. Furthermore, enhancing excitation, but not PV[+] interneuron-mediated inhibition, rescued social deficits in ASD mouse models. Collectively, our findings propose that reduced excitatory activity in the developing mPFC may serve as a shared local circuitry mechanism triggering alterations in PV[+] interneurons and mediating impaired social functions in ASD.}, }
@article {pmid39106199, year = {2024}, author = {Dash, D and Ferrari, P and Wang, J}, title = {Neural Decoding of Spontaneous Overt and Intended Speech.}, journal = {Journal of speech, language, and hearing research : JSLHR}, volume = {67}, number = {11}, pages = {4216-4225}, doi = {10.1044/2024_JSLHR-24-00046}, pmid = {39106199}, issn = {1558-9102}, mesh = {Humans ; *Magnetoencephalography ; Male ; Female ; *Speech/physiology ; Adult ; Young Adult ; Neural Networks, Computer ; Brain/physiology/diagnostic imaging ; Machine Learning ; Discriminant Analysis ; Brain-Computer Interfaces ; Speech Perception/physiology ; }, abstract = {PURPOSE: The aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli).<br><br>METHOD: Magnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals.<br><br>RESULTS: LDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN.<br><br>CONCLUSIONS: This study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.}, }
@article {pmid39106147, year = {2024}, author = {Lyu, J and Yang, Y and Zong, Y and Leng, Y and Zheng, W and Ge, S}, title = {Novel Sinusoidal Signal Assisted Multivariate Variational Mode Decomposition Combined With Task-Related Component Analysis for Enhancing SSVEP-Based BCI Performance.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {11}, pages = {6474-6485}, doi = {10.1109/JBHI.2024.3439391}, pmid = {39106147}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Young Adult ; Female ; }, abstract = {Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) have a broad application prospect owing to their multiple command output and high performance. Each harmonic component of SSVEP individually contains unique features, which can be utilized to enhance the recognition performance of SSVEP-based BCIs. However, the existing subband analysis methods for SSVEP, including those based on filter banks and existing mode decomposition methods, have limitations in extracting and utilizing independent harmonic components. This study proposes a sinusoidal signal assisted multivariate variational mode decomposition (SA-MVMD) algorithm that allows the constraint of the center frequencies and narrowband filtering structures of the intrinsic mode functions (IMFs) based on the prior frequency knowledge of the signal. It preserves the target information of the signal during decomposition while avoiding mode mixing and incorrect decomposition, thereby enabling the effective extraction of each independent harmonic component of SSVEP. Building on this, a SA-MVMD based task-related component analysis (SA-MVMD-TRCA) method is further proposed to fully utilize the features within the overall SSVEP as well as its independent harmonics, thereby enhancing the recognition performance. Testing on the public SSVEP Benchmark dataset demonstrates that the proposed method significantly outperforms the filter bank-based control methods. This study confirms the effectiveness of SA-MVMD and the potential of this approach, which analyzes and utilizes each independent harmonic of SSVEP, providing new strategies and perspectives for performance enhancement in SSVEP-based BCIs.}, }
@article {pmid39104699, year = {2024}, author = {Rabbani, MHR and Islam, SMR}, title = {Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {4}, pages = {1489-1506}, pmid = {39104699}, issn = {1871-4080}, abstract = {The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications: functioning of brain-computer interface (BCI), detection of neuronal disorders, neurorehabilitation for disabled patients, and many others. Recent studies show that the combination or fusion of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) demonstrates improved classification and detection performance compared to sole-EEG and sole-fNIRS. Deep learning (DL) networks are suitable for the classification of large volume time-series data like EEG and fNIRS. This study performs the decision fusion of EEG and fNIRS. The classification of EEG, fNIRS, and decision-fused EEG-fNIRSinto cognitive task labels is performed by DL networks. Two different open-source datasets of simultaneously recorded EEG and fNIRS are examined in this study. Dataset 01 is comprised of 26 subjects performing 3 cognitive tasks: n-back, discrimination or selection response (DSR), and word generation (WG). After data acquisition, fNIRS is converted to oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in Dataset 01. Dataset 02 is comprised of 29 subjects who performed 2 tasks: motor imagery and mental arithmetic. The classification procedure of EEG and fNIRS (or HbO2, HbR) are carried out by 7 DL classifiers: convolutional neural network (CNN), long short-term memory network (LSTM), gated recurrent unit (GRU), CNN-LSTM, CNN-GRU, LSTM-GRU, and CNN-LSTM-GRU. After the classification of single modalities, their prediction scores or decisions are combined to obtain the decision-fused modality. The classification performance is measured by overall accuracy and area under the ROC curve (AUC). The highest accuracy and AUC recorded in Dataset 01 are 96% and 100% respectively; both by the decision fusion modality using CNN-LSTM-GRU. For Dataset 02, the highest accuracy and AUC are 82.76% and 90.44% respectively; both by the decision fusion modality using CNN-LSTM. The experimental result shows that decision-fused EEG-HbO2-HbR and EEG-fNIRSdeliver higher performances compared to their constituent unimodalities in most cases. For DL classifiers, CNN-LSTM-GRU in Dataset 01 and CNN-LSTM in Dataset 02 yield the highest performance.}, }
@article {pmid39104685, year = {2024}, author = {Yin, X and Lin, M and Liang, J and Zeng, F}, title = {Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {4}, pages = {1733-1741}, pmid = {39104685}, issn = {1871-4080}, abstract = {Multivariate synchronization index (MSI), as an effective recognition algorithm for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI), can accurately decode target frequencies without training. To further consider temporal features or extract harmonic components, extended MSI (EMSI), temporally local MSI (TMSI), and filter bank MSI (FBMSI) have been proposed. However, the promotion effects of the above three strategies on MSI have not been compared in detail. In this paper, the performance of EMSI, TMSI, and FBMSI under different time windows was analyzed with the same dataset. The results indicated that the improvement effect of the temporally local method on MSI was better than that of the other two methods under the short time window, and the effect of the filter bank method was better when the time window was greater than 0.8 s. Based on the idea of simultaneously extracting time-frequency features, FBEMSI and FBTMSI were proposed by integrating time delay embedding and temporally local method into FBMSI respectively. The two improved methods, which has no significant difference, can improve the recognition effect of FBMSI. But the computing time of FBEMSI was shorter, which can be a potential method for SSVEP-BCI.}, }
@article {pmid39104677, year = {2024}, author = {Li, M and Qi, E and Xu, G and Jin, J and Zhao, Q and Guo, M and Liao, W}, title = {A delayed matching task-based study on action sequence of motor imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {4}, pages = {1593-1607}, pmid = {39104677}, issn = {1871-4080}, abstract = {The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of imitation, learning, and memory for motor behavior. Whether it influences the MI-BCI is unknown, and how to manifest this influence is difficult since the MI is a spontaneous brain activity. To investigate the influence of the action sequence, this study proposes a novel paradigm named action sequences observing and delayed matching task to use images and videos to guide people to observe, match and reinforce the memory of sequence. Seven subjects' ERPs and MI performance are analyzed under four different levels of complexities or orders of the sequence. Results demonstrated that the action sequence in terms of complexity and sequence order significantly affects the MI. The complex action in positive order obtains stronger ERD/ERS and more pronounced MI feature distributions, and yields an MI classification accuracy that is 12.3% higher than complex action in negative order (p < 0.05). In addition, the ERP amplitudes derived from the supplementary motor area show a positive correlation to the MI. This study demonstrates a new perspective of improving imagery in the MI-BCI by considering the complexity and order of the action sequences, and provides a novel index for manifesting the MI performance by ERP.}, }
@article {pmid39104673, year = {2024}, author = {Rezvani, S and Hosseini-Zahraei, SH and Tootchi, A and Guger, C and Chaibakhsh, Y and Saberi, A and Chaibakhsh, A}, title = {A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {4}, pages = {1419-1443}, pmid = {39104673}, issn = {1871-4080}, abstract = {Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.}, }
@article {pmid39103461, year = {2024}, author = {Chockboondee, M and Jatupornpoonsub, T and Lertsukprasert, K and Wongsawat, Y}, title = {Effects of daily listening to 6 Hz binaural beats over one month: an event-related potentials study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {18059}, pmid = {39103461}, issn = {2045-2322}, support = {B42G670043//This project is supported by the National Higher Education Science Research and Innovation Policy Council, PMU B/ ; }, mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; *Acoustic Stimulation ; Evoked Potentials, Auditory/physiology ; Electroencephalography ; Evoked Potentials/physiology ; Auditory Perception/physiology ; Event-Related Potentials, P300/physiology ; Cognition/physiology ; }, abstract = {The aim of the present study was to identify cognitive alterations, as indicated by event-related potentials (ERPs), after one month of daily exposure to theta binaural beats (BBs) for 10 minutes. The recruited healthy subjects (n = 60) were equally divided into experimental and control groups. For a month, the experimental group was required to practice BBs listening daily, while the control group did not. ERPs were assessed at three separate visits over a span of one month, with a two-week interval between each visit. At each visit, ERPs were measured before and after listening. The auditory and visual ERPs significantly increased the auditory and visual P300 amplitudes consistently at each visit. BBs enhanced the auditory N200 amplitude consistently across all visits, but the visual N200 amplitude increased only at the second and third visits. Compared to the healthy controls, daily exposure to BBs for two weeks resulted in increased auditory P300 amplitude. Additionally, four weeks of BBs exposure not only increased auditory P300 amplitude but also reduced P300 latency. These preliminary findings suggest that listening to BBs at 6 Hz for 10 minutes daily may enhance certain aspects of cognitive function. However, further research is needed to confirm these effects and to understand the underlying mechanisms. Identifying the optimal duration and practice of listening to 6 Hz BBs could potentially contribute to cognitive enhancement strategies in healthy individuals.}, }
@article {pmid39103336, year = {2024}, author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF}, title = {40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.}, journal = {Cell discovery}, volume = {10}, number = {1}, pages = {81}, pmid = {39103336}, issn = {2056-5968}, support = {31600859//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970948//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101556//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ22H090013//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {The glymphatic-lymphatic system is increasingly recognized as fundamental for the homeostasis of the brain milieu since it defines cerebral spinal fluid flow in the brain parenchyma and eliminates metabolic waste. Animal and human studies have uncovered several important physiological factors regulating the glymphatic system including sleep, aquaporin-4, and hemodynamic factors. Yet, our understanding of the modulation of the glymphatic system is limited, which has hindered the development of glymphatic-based treatment for aging and neurodegenerative disorders. Here, we present the evidence from fluorescence tracing, two-photon recording, and dynamic contrast-enhanced magnetic resonance imaging analyses that 40 Hz light flickering enhanced glymphatic influx and efflux independently of anesthesia and sleep, an effect attributed to increased astrocytic aquaporin-4 polarization and enhanced vasomotion. Adenosine-A2A receptor (A2AR) signaling emerged as the neurochemical underpinning of 40 Hz flickering-induced enhancement of glymphatic flow, based on increased cerebrofluid adenosine levels, the abolishment of enhanced glymphatic flow by pharmacological or genetic inactivation of equilibrative nucleotide transporters-2 or of A2AR, and by the physical and functional A2AR-aquaporin-4 interaction in astrocytes. These findings establish 40 Hz light flickering as a novel non-invasive strategy of enhanced glymphatic flow, with translational potential to relieve brain disorders.}, }
@article {pmid39103043, year = {2024}, author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X}, title = {EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.}, journal = {Neuroscience}, volume = {556}, number = {}, pages = {42-51}, doi = {10.1016/j.neuroscience.2024.07.051}, pmid = {39103043}, issn = {1873-7544}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Brain/physiology ; }, abstract = {Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.}, }
@article {pmid39102329, year = {2024}, author = {Shin, J and Chung, W}, title = {Multiband Convolutional Riemannian Network With Band-Wise Riemannian Triplet Loss for Motor Imagery Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {12}, pages = {7230-7238}, doi = {10.1109/JBHI.2024.3438167}, pmid = {39102329}, issn = {2168-2208}, mesh = {Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; Brain-Computer Interfaces ; *Deep Learning ; }, abstract = {This paper presents a novel motor imagery classification algorithm that uses an overlapping multiscale multiband convolutional Riemannian network with band-wise Riemannian triplet loss to improve classification performance. Despite the superior performance of the Riemannian approach over the common spatial pattern filter approach, deep learning methods that generalize the Riemannian approach have received less attention. The proposed algorithm develops a state-of-the-art multiband Riemannian network that reduces the potential overfitting problem of Riemannian networks, a drawback of Riemannian networks due to their inherent large feature dimension from covariance matrix, by using fewer subbands with discriminative frequency diversity, by inserting convolutional layers before computing the subband covariance matrix, and by regularizing subband networks with Riemannian triplet loss. The proposed method is evaluated using the publicly available datasets, the BCI Competition IV dataset 2a and the OpenBMI dataset. The experimental results confirm that the proposed method improves performance, in particular achieving state-of-the-art classification accuracy among the currently studied Riemannian networks.}, }
@article {pmid39099770, year = {2024}, author = {Chen, W and Liao, Y and Dai, R and Dong, Y and Huang, L}, title = {EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.}, journal = {Frontiers in computational neuroscience}, volume = {18}, number = {}, pages = {1416494}, pmid = {39099770}, issn = {1662-5188}, abstract = {EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.}, }
@article {pmid39099169, year = {2024}, author = {Wang, J and Qiu, Y and Yang, L and Wang, J and He, J and Tang, C and Yang, Z and Hong, W and Yang, B and He, Q and Weng, Q}, title = {Preserving mitochondrial homeostasis protects against drug-induced liver injury via inducing OPTN (optineurin)-dependent Mitophagy.}, journal = {Autophagy}, volume = {20}, number = {12}, pages = {2677-2696}, pmid = {39099169}, issn = {1554-8635}, mesh = {*Mitophagy/drug effects/physiology ; *Homeostasis/drug effects ; *Membrane Transport Proteins/metabolism ; Animals ; *Cell Cycle Proteins/metabolism ; Humans ; *Valosin Containing Protein/metabolism ; *Chemical and Drug Induced Liver Injury/metabolism ; *Hepatocytes/metabolism/drug effects ; Mitochondria/metabolism/drug effects ; Mice ; Transcription Factor TFIIIA/metabolism/genetics ; Mice, Inbred C57BL ; Beclin-1/metabolism ; Mitochondria, Liver/metabolism/drug effects ; }, abstract = {Disruption of mitochondrial function is observed in multiple drug-induced liver injuries (DILIs), a significant global health threat. However, how the mitochondrial dysfunction occurs and whether maintain mitochondrial homeostasis is beneficial for DILIs remains unclear. Here, we show that defective mitophagy by OPTN (optineurin) ablation causes disrupted mitochondrial homeostasis and aggravates hepatocytes necrosis in DILIs, while OPTN overexpression protects against DILI depending on its mitophagic function. Notably, mass spectrometry analysis identifies a new mitochondrial substrate, GCDH (glutaryl-CoA dehydrogenase), which can be selectively recruited by OPTN for mitophagic degradation, and a new cofactor, VCP (valosin containing protein) that interacts with OPTN to stabilize BECN1 during phagophore assembly, thus boosting OPTN-mediated mitophagy initiation to clear damaged mitochondria and preserve mitochondrial homeostasis in DILIs. Then, the accumulation of OPTN in different DILIs is further validated with a protective effect, and pyridoxine is screened and established to alleviate DILIs by inducing OPTN-mediated mitophagy. Collectively, our findings uncover a dual role of OPTN in mitophagy initiation and implicate the preservation of mitochondrial homeostasis via inducing OPTN-mediated mitophagy as a potential therapeutic approach for DILIs.Abbreviation: AILI: acetaminophen-induced liver injury; ALS: amyotrophic lateral sclerosis; APAP: acetaminophen; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CHX: cycloheximide; Co-IP: co-immunoprecipitation; DILI: drug-induced liver injury; FL: full length; GCDH: glutaryl-CoA dehydrogenase; GOT1/AST: glutamic-oxaloacetic transaminase 1; GO: gene ontology; GSEA: gene set enrichment analysis; GPT/ALT: glutamic - pyruvic transaminase; INH: isoniazid; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MMP: mitochondrial membrane potential; MST: microscale thermophoresis; MT-CO2/COX-II: mitochondrially encoded cytochrome c oxidase II; OPTN: optineurin; PINK1: PTEN induced kinase 1; PRKN: parkin RBR E3 ubiquitin protein ligase; TIMM23: translocase of inner mitochondrial membrane 23; TOMM20: translocase of outer mitochondrial membrane 20; TSN: toosendanin; VCP: valosin containing protein, WIPI2: WD repeat domain, phosphoinositide interacting 2.}, }
@article {pmid39097741, year = {2024}, author = {El Kirat, H and van Belle, S and Khattabi, A and Belrhiti, Z}, title = {Behavioral change interventions, theories, and techniques to reduce physical inactivity and sedentary behavior in the general population: a scoping review.}, journal = {BMC public health}, volume = {24}, number = {1}, pages = {2099}, pmid = {39097741}, issn = {1471-2458}, mesh = {Humans ; *Sedentary Behavior ; Exercise/psychology ; Health Promotion/methods ; Behavior Therapy/methods ; Health Behavior ; }, abstract = {BACKGROUND: Worldwide, physical inactivity (PIA) and sedentary behavior (SB) are recognized as significant challenges hindering the achievement of the United Nations (UN) sustainable development goals (SDGs). PIA and SB are responsible for 1.6 million deaths attributed to non-communicable diseases (NCDs). The World Health Organization (WHO) has urged governments to implement interventions informed by behavioral theories aimed at reducing PIA and SB. However, limited attention has been given to the range of theories, techniques, and contextual conditions underlying the design of behavioral theories. To this end, we set out to map these interventions, their levels of action, their mode of delivery, and how extensively they apply behavioral theories, constructs, and techniques.<br><br>METHODS: Following the scoping review methodology of Arksey and O'Malley (2005), we included peer-reviewed articles on behavioral theories interventions centered on PIA and SB, published between 2010 and 2023 in Arabic, French, and English in four databases (Scopus, Web of Science [WoS], PubMed, and Google Scholar). We adopted a framework thematic analysis based on the upper-level ontology of behavior theories interventions, Behavioral theories taxonomies, and the first version (V1) taxonomy of behavior change techniques(BCTs).<br><br>RESULTS: We included 29 studies out of 1,173 that were initially screened/searched. The majority of interventions were individually focused (n&#x2009;=&#x2009;15). Few studies have addressed interpersonal levels (n&#x2009;=&#x2009;6) or organizational levels (n&#x2009;=&#x2009;6). Only two interventions can be described as systemic (i.e., addressing the individual, interpersonal, organizational, and institutional factors)(n&#x2009;=&#x2009;2). Most behavior change interventions&#xa0;use four theories: The Social cognitive theory (SCT), the socioecological model (SEM), SDT, and the transtheoretical model (TTM). Most behavior change interventions (BCIS) involve goal setting, social support, and action planning with various degrees of theoretical use&#xa0;(intensive [n&#x2009;=&#x2009;15], moderate [n&#x2009;=&#x2009;11], or low [n&#x2009;=&#x2009;3]).<br><br>DISCUSSION AND CONCLUSION: Our review suggests the need to develop systemic and complementary interventions that entail the micro-, meso- and macro-level barriers to behavioral changes. Theory informed BCI need to integrate synergistic BCTs into models that use&#xa0;micro-, meso- and macro-level theories to determine behavioral change. Future interventions need to appropriately use a mix of behavioral theories and BCTs to address the systemic nature of behavioral change as well as the heterogeneity of contexts and targeted populations.}, }
@article {pmid39095351, year = {2024}, author = {Tian, F and Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {6528}, pmid = {39095351}, issn = {2041-1723}, support = {U20A20221//National Natural Science Foundation of China (National Science Foundation of China)/ ; 819611280292//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; Cats ; *Visual Cortex/physiology ; *Infrared Rays ; Photic Stimulation/methods ; Optical Fibers ; Visual Prosthesis ; }, abstract = {Cutting edge advances in electrical visual cortical prosthetics have evoked perception of shapes, motion, and letters in the blind. Here, we present an alternative optical approach using pulsed infrared neural stimulation. To interface with dense arrays of cortical columns with submillimeter spatial precision, both linear array and 100-fiber bundle array optical fiber interfaces were devised. We deliver infrared stimulation through these arrays in anesthetized cat visual cortex and monitor effects by optical imaging in contralateral visual cortex. Infrared neural stimulation modulation of response to ongoing visual oriented gratings produce enhanced responses in orientation-matched domains and suppressed responses in non-matched domains, consistent with a known higher order integration mediated by callosal inputs. Controls include dynamically applied speeds, directions and patterns of multipoint stimulation. This provides groundwork for a distinct type of prosthetic targeted to maps of visual cortical columns.}, }
@article {pmid39092732, year = {2024}, author = {Islam, MM and Vashishat, A and Kumar, M}, title = {Advancements Beyond Limb Loss: Exploring the Intersection of AI and BCI in Prosthetic Evaluation.}, journal = {Current pharmaceutical design}, volume = {30}, number = {35}, pages = {2749-2752}, pmid = {39092732}, issn = {1873-4286}, }
@article {pmid39086375, year = {2024}, author = {Angelopoulou, A and Chihi, I and Hemanth, J}, title = {Editorial: Methods and protocols in Brain-Computer Interfaces.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1447973}, pmid = {39086375}, issn = {1662-5161}, }
@article {pmid39086374, year = {2024}, author = {Xavier Fidêncio, A and Klaes, C and Iossifidis, I}, title = {A generic error-related potential classifier based on simulated subjects.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1390714}, pmid = {39086374}, issn = {1662-5161}, abstract = {Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.}, }
@article {pmid39083601, year = {2024}, author = {Jia, K and Wang, M and Steinwurzel, C and Ziminski, JJ and Xi, Y and Emir, U and Kourtzi, Z}, title = {Recurrent inhibition refines mental templates to optimize perceptual decisions.}, journal = {Science advances}, volume = {10}, number = {31}, pages = {eado7378}, pmid = {39083601}, issn = {2375-2548}, mesh = {Humans ; *Decision Making/physiology ; *Magnetic Resonance Imaging/methods ; *Visual Cortex/physiology ; Male ; Adult ; Female ; gamma-Aminobutyric Acid/metabolism ; Visual Perception/physiology ; Neuronal Plasticity/physiology ; Young Adult ; Brain Mapping ; }, abstract = {Translating sensory inputs to perceptual decisions relies on building internal representations of features critical for solving complex tasks. Yet, we still lack a mechanistic account of how the brain forms these mental templates of task-relevant features to optimize decision-making. Here, we provide evidence for recurrent inhibition: an experience-dependent plasticity mechanism that refines mental templates by enhancing γ-aminobutyric acid (GABA)-mediated (GABAergic) inhibition and recurrent processing in superficial visual cortex layers. We combine ultrahigh-field (7 T) functional magnetic resonance imaging at submillimeter resolution with magnetic resonance spectroscopy to investigate the fine-scale functional and neurochemical plasticity mechanisms for optimized perceptual decisions. We demonstrate that GABAergic inhibition increases following training on a visual (i.e., fine orientation) discrimination task, enhancing the discriminability of orientation representations in superficial visual cortex layers that are known to support recurrent processing. Modeling functional and neurochemical plasticity interactions reveals that recurrent inhibitory processing optimizes brain computations for perpetual decisions and adaptive behavior.}, }
@article {pmid39082285, year = {2024}, author = {Khorev, V and Kurkin, S and Badarin, A and Antipov, V and Pitsik, E and Andreev, A and Grubov, V and Drapkina, O and Kiselev, A and Hramov, A}, title = {Review on the Use of Brain Computer Interface Rehabilitation Methods for Treating Mental and Neurological Conditions.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {7}, pages = {125}, doi = {10.31083/j.jin2307125}, pmid = {39082285}, issn = {0219-6352}, support = {123020600127-4//Russian Ministry of Health as part of the scientific work "Development of a multimodal biofeedback-based hardware and software system for rehabilitation of patients with cognitive and motor disorders of different nature"/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Mental Disorders/rehabilitation ; *Nervous System Diseases/rehabilitation ; Neurological Rehabilitation/methods ; }, abstract = {This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field and rehabilitation. By analyzing and comparing results obtained with various tools and techniques, we aim to offer a systematic understanding of BCI applications concerning different modalities of neurofeedback and input data utilized. Our primary objective is to address the existing gap in the area of meta-reviews, which provides a more comprehensive outlook on the field, allowing for the assessment of the current landscape and developments within the scope of BCI. Our main methodologies include meta-analysis, search queries employing relevant keywords, and a network-based approach. We are dedicated to delivering an unbiased evaluation of BCI studies, elucidating the primary vectors of research development in this field. Our review encompasses a diverse range of applications, incorporating the use of brain-computer interfaces for rehabilitation and the treatment of various diagnoses, including those related to affective spectrum disorders. By encompassing a wide variety of use cases, we aim to offer a more comprehensive perspective on the utilization of neurofeedback treatments across different contexts. The structured and organized presentation of information, complemented by accompanying visualizations and diagrams, renders this review a valuable resource for scientists and researchers engaged in the domains of biofeedback and brain-computer interfaces.}, }
@article {pmid39082284, year = {2024}, author = {Ou, Y and Wang, F and Feng, B and Tang, L and Pan, J}, title = {Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {7}, pages = {134}, doi = {10.31083/j.jin2307134}, pmid = {39082284}, issn = {0219-6352}, support = {2021A1515011853//Guangdong Basic and Applied Basic Research Foundation/ ; 61906019//National Natural Science Foundation of China/ ; 62006082//National Natural Science Foundation of China/ ; 2022ZD0208900//STI 2030-Major Projects/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Algorithms ; Sleep Stages/physiology ; Signal Processing, Computer-Assisted ; Adult ; }, abstract = {BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations.<br><br>METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales.<br><br>RESULTS: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance.<br><br>CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.}, }
@article {pmid39081972, year = {2023}, author = {Perez Garcia, G and Bicak, M and Buros, J and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Gage, FH and Barlow, C and Dudley, JT and Glicksberg, BS and Wang, Y and Readhead, B and Ehrlich, ME and Elder, GA and Gandy, S}, title = {Beneficial effects of physical exercise and an orally active mGluR2/3 antagonist pro-drug on neurogenesis and behavior in an Alzheimer's amyloidosis model.}, journal = {Frontiers in dementia}, volume = {2}, number = {}, pages = {1198006}, pmid = {39081972}, issn = {2813-3919}, abstract = {BACKGROUND: Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise (PE) involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is a negative allosteric modulator at group II metabotropic glutamate receptors (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of brain accumulation of oligomeric Aβ[E22Q] (APP [E693Q] = "Dutch APP") reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of Dutch APP mice.<br><br>METHODS: 3-month-old mice were administered BCI-838 and/or physical exercise for 1 month and then tested in novel object recognition, neurogenesis, and RNAseq.<br><br>RESULTS: Here we show that (i) administration of BCI-838 and a combination of BCI-838 and PE enhanced AHN in a 4-month old mouse model of AD amyloid pathology (APP [KM670/671NL] /PSEN1 [&#x394;exon9]= APP/PS1), (ii) administration of BCI-838 alone or with PE led to stimulation of AHN and improvement in recognition memory, (iii) the hippocampal dentate gyrus transcriptome of APP/PS1 mice following BCI-838 treatment showed up-regulation of brain-derived neurotrophic factor (BDNF), PIK3C2A of the PI3K-mTOR pathway, and metabotropic glutamate receptors, and down-regulation of EIF5A involved in modulation of mTOR activity by ketamine, and (iv) validation by qPCR of an association between increased BDNF levels and BCI-838 treatment.<br><br>CONCLUSION: Our study points to BCI-838 as a safe and orally active compound capable of mimicking the beneficial effect of PE on AHN and recognition memory in a mouse model of AD amyloid pathology.}, }
@article {pmid39080312, year = {2024}, author = {Wang, G and Marcucci, G and Peters, B and Braidotti, MC and Muckli, L and Faccio, D}, title = {Human-centred physical neuromorphics with visual brain-computer interfaces.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {6393}, pmid = {39080312}, issn = {2041-1723}, support = {EP/T021020/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Male ; Adult ; Brain/physiology ; Photic Stimulation ; Female ; Young Adult ; Neural Networks, Computer ; }, abstract = {Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.}, }
@article {pmid39078880, year = {2024}, author = {Codol, O and Michaels, JA and Kashefi, M and Pruszynski, JA and Gribble, PL}, title = {MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {39078880}, issn = {2050-084X}, support = {RGPIN/05458-2018//Natural Sciences and Engineering Research Council of Canada/ ; PJT-175010/CAPMC/CIHR/Canada ; RGPIN-2022-04421//Natural Sciences and Engineering Research Council of Canada/ ; PJT-156241/CAPMC/CIHR/Canada ; }, mesh = {*Neural Networks, Computer ; Biomechanical Phenomena ; *Software ; Humans ; Algorithms ; }, abstract = {Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet's focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.}, }
@article {pmid39074028, year = {2024}, author = {Lee, BH and Cho, JH and Kwon, BH and Lee, M and Lee, SW}, title = {Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2793-2804}, doi = {10.1109/TNSRE.2024.3434983}, pmid = {39074028}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Robotics ; Calibration ; *Algorithms ; Reproducibility of Results ; Male ; *Arm/physiology ; Adult ; Female ; Imagination/physiology ; Brain-Computer Interfaces ; Young Adult ; Neural Networks, Computer ; Speech/physiology ; }, abstract = {Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.}, }
@article {pmid39074021, year = {2024}, author = {Yi, Z and Pan, J and Chen, Z and Lu, D and Cai, H and Li, J and Xie, Q}, title = {A Hybrid BCI Integrating EEG and Eye-Tracking for Assisting Clinical Communication in Patients With Disorders of Consciousness.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2759-2771}, doi = {10.1109/TNSRE.2024.3435016}, pmid = {39074021}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Female ; *Consciousness Disorders/physiopathology/diagnosis ; Adult ; *Eye-Tracking Technology ; *Event-Related Potentials, P300/physiology ; Middle Aged ; *Algorithms ; Young Adult ; Neural Networks, Computer ; Communication Devices for People with Disabilities ; Communication ; Healthy Volunteers ; Attention/physiology ; }, abstract = {Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC. Specifically, the BCI presented a question and two randomly flashing answers (yes/no). The subjects were instructed to focus on an answer. A multimodal target recognition network (MTRN) is proposed to detect P300 potentials and eye-tracking responses (i.e., pupil constriction and gaze) and identify the target in real time. In the MTRN, the dual-stream feature extraction module with two independent multiscale convolutional neural networks extracts multiscale features from multimodal data. Then, the multimodal attention strategy adaptively extracts the most relevant information about the target from multimodal data. Finally, a prototype network is designed as a classifier to facilitate small-sample data classification. Ten healthy individuals, nine DOC patients and one LIS patient were included in this study. All healthy subjects achieved 100% accuracy. Five patients could communicate with our BCI, with 76.1±7.9% accuracy. Among them, two patients who were noncommunicative on the behavioral scale exhibited communication ability via our BCI. Additionally, we assessed the performance of unimodal BCIs and compared MTRNs with other methods. All the results suggested that our BCI can yield more sensitive outcomes than the CRS-R and can serve as a valuable communication tool.}, }
@article {pmid39071181, year = {2024}, author = {Kilani, S and Aghili, SN and Fathi, Y and Sburlea, AI}, title = {Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1360709}, pmid = {39071181}, issn = {1662-4548}, abstract = {INTRODUCTION: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.<br><br>METHODS: To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection.<br><br>RESULTS: The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <&#xbd; of the training samples needed by previous studies.<br><br>DISCUSSION: Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.}, }
@article {pmid39069564, year = {2024}, author = {Garvayo, M and Dupont, S and Frazzini, V and Bielle, F and Adam, C and Bendary, YE and Méré, M and Samson, S and Guesdon, A and Navarro, V and Mathon, B}, title = {Resective surgery for mesial temporal lobe epilepsy associated with hippocampal sclerosis in patients over 50 years: a case-control study.}, journal = {Journal of neurology}, volume = {271}, number = {9}, pages = {6197-6208}, pmid = {39069564}, issn = {1432-1459}, mesh = {Humans ; *Epilepsy, Temporal Lobe/surgery ; Middle Aged ; Male ; Female ; *Sclerosis/surgery ; *Hippocampus/surgery/pathology ; Case-Control Studies ; Adult ; Retrospective Studies ; Aged ; Young Adult ; Drug Resistant Epilepsy/surgery/etiology ; Age Factors ; Neurosurgical Procedures/adverse effects ; Adolescent ; Follow-Up Studies ; Treatment Outcome ; Hippocampal Sclerosis ; }, abstract = {BACKGROUND: Mesial temporal lobe epilepsy associated with hippocampal sclerosis (MTLE/HS) is the most common cause of drug-resistant focal seizures and surgical resection is the primary treatment option, with seizure-free rates ranging from 60 to 80%. However, data on postsurgical seizure outcomes in patients ≥ 50 years of age are limited. This study aimed to assess the efficacy and safety of surgery in this age group compared to younger patients.<br><br>METHODS: We performed a retrospective analysis of data from resective surgeries conducted in patients with MTLE/HS between 1990 and 2022. We focused on patients aged&#x2009;&#x2265;&#x2009;50&#xa0;years and compared the surgical safety and efficacy variables between this group and a control group of patients aged&#x2009;<&#x2009;50&#xa0;years through a case-control study.<br><br>RESULTS: Among the 450 MTLE/HS patients who underwent surgery during the inclusion period, 61 (13.6%) were aged&#x2009;&#x2265;&#x2009;50&#xa0;years and matched with 183 younger patients, totaling 244 study participants. The two groups had similar characteristics. At the last follow-up (median 5.7&#xa0;years), Engel I outcomes were achieved in 80.3% of the older patients and 81.4% of the younger patients, with no significant difference (p&#x2009;=&#x2009;0.85). Postoperative cognitive and psychiatric outcomes did not differ between the groups. Major complication rates were also comparable, at 3.3% in the older group and 2.7% in the younger group (p&#x2009;=&#x2009;0.83). The extratemporal ictal abnormalities observed on video-EEG were the only variable that demonstrated a significant association with an unfavorable seizure outcome in the older group (OR 9.3, 95% CI [1.8-47.6], p&#x2009;=&#x2009;0.005).<br><br>CONCLUSIONS: This study provides grade 3 evidence that resective surgery for MTLE/HS patients aged&#x2009;&#x2265;&#x2009;50&#xa0;years is as effective and safe as it is for younger patients, and thus should be considered as the primary treatment option for drug-resistant cases.}, }
@article {pmid39069222, year = {2024}, author = {Yeo, TS and Kim, JS and Kim, HJ and Chung, CK}, title = {Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction.}, journal = {NeuroImage}, volume = {297}, number = {}, pages = {120727}, doi = {10.1016/j.neuroimage.2024.120727}, pmid = {39069222}, issn = {1095-9572}, mesh = {Humans ; *Motor Cortex/physiology ; Male ; *Magnetoencephalography/methods ; Adult ; Female ; *Movement/physiology ; *Brain-Computer Interfaces ; Young Adult ; Brain Mapping/methods ; }, abstract = {This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.}, }
@article {pmid39067986, year = {2024}, author = {Xu, Q and Liang, R and Gao, J and Fan, Y and Dong, J and Wang, L and Zheng, C and Yang, J and Ming, D}, title = {rTMS Ameliorates time-varying depression and social behaviors in stimulated space complex environment associated with VEGF signaling.}, journal = {Life sciences in space research}, volume = {42}, number = {}, pages = {17-26}, doi = {10.1016/j.lssr.2024.04.001}, pmid = {39067986}, issn = {2214-5532}, mesh = {Animals ; *Vascular Endothelial Growth Factor A/metabolism ; Mice ; *Transcranial Magnetic Stimulation ; Male ; *Social Behavior ; *Space Flight ; *Depression ; *Mice, Inbred C57BL ; *Signal Transduction ; Prefrontal Cortex/physiology/metabolism ; Matrix Metalloproteinase 9/metabolism ; }, abstract = {Studies have indicated that medium- to long-duration spaceflight may adversely affect astronauts' emotional and social functioning. Emotion modulation can significantly impact astronauts' well-being, performance, mission safety and success. However, with the increase in flight time, the potential alterations in emotional and social performance during spaceflight and their underlying mechanisms remain to be investigated, and targeted therapeutic and preventive interventions have yet to be identified. We evaluated the changes of emotional and social functions in mice with the extension of the time in simulated space complex environment (SSCE), and simultaneously monitored changes in brain tissue of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), and inflammation-related factors. Furthermore, we assessed the regulatory role of repetitive transcranial magnetic stimulation (rTMS) in mood and socialization with the extension of the time in SSCE, as well as examining alterations of VEGF signaling in the medial prefrontal cortex (mPFC). Our findings revealed that mice exposed to SSCE for 7 days exhibited depressive-like behaviors, with these changes persisting throughout SSCE period. In addition, 14 days of rTMS treatment significantly ameliorated SSCE-induced emotional and social dysfunction, potentially through modulation of the level of VEGF signaling in mPFC. These results indicates that emotional and social disorders increase with the extension of SSCE time, and rTMS can improve the performance, which may be related to VEGF signaling. This study offers insights into potential pattern of change over time for mental health issues in astronauts. Further analysis revealed that rTMS modulates emotional and social dysfunction during SSCE exposure, with its mechanism potentially being associated with VEGF signaling.}, }
@article {pmid39066744, year = {2024}, author = {Bartkowiak, J and Dernektsi, C and Agarwal, V and Lebehn, MA and Williams, TA and Brandwein, RA and Brugger, N and Gräni, C and Windecker, S and Vahl, TP and Nazif, TM and George, I and Kodali, SK and Praz, F and Hahn, RT}, title = {3-Dimensional Echocardiographic Prediction of Left Ventricular Outflow Tract Area Prior to Transcatheter Mitral Valve Replacement.}, journal = {JACC. Cardiovascular imaging}, volume = {17}, number = {10}, pages = {1168-1178}, doi = {10.1016/j.jcmg.2024.05.011}, pmid = {39066744}, issn = {1876-7591}, mesh = {Humans ; *Echocardiography, Three-Dimensional ; Female ; Male ; *Predictive Value of Tests ; *Mitral Valve/diagnostic imaging/physiopathology/surgery ; Retrospective Studies ; Aged ; *Heart Valve Prosthesis Implantation/instrumentation/adverse effects ; *Cardiac Catheterization/adverse effects ; Reproducibility of Results ; *Echocardiography, Transesophageal ; Treatment Outcome ; *Ventricular Function, Left ; Aged, 80 and over ; Heart Valve Prosthesis ; Middle Aged ; Hemodynamics ; Mitral Valve Insufficiency/diagnostic imaging/physiopathology/surgery ; Image Interpretation, Computer-Assisted ; }, abstract = {BACKGROUND: New postprocessing software facilitates 3-dimensional (3D) echocardiographic determination of mitral annular (MA) and neo-left ventricular outflow tract (neo-LVOT) dimensions in patients undergoing transcatheter mitral valve replacement (TMVR).<br><br>OBJECTIVES: This study aims to test the accuracy of 3D echocardiographic analysis as compared to baseline computed tomography (CT).<br><br>METHODS: A total of 105 consecutive patients who underwent TMVR at 2 tertiary care centers between October 2017 and May 2023 were retrospectively included. A virtual valve was projected in both baseline CT and 3D transesophageal echocardiography (TEE) using dedicated software. MA dimensions were measured in baseline images and neo-LVOT dimensions were measured in baseline and postprocedural images. All measurements were compared to baseline CT as a reference. The predicted neo-LVOT area was correlated with postprocedural peak LVOT gradients.<br><br>RESULTS: There was no significant bias in baseline neo-LVOT prediction between both imaging modalities. TEE significantly underestimated MA area, perimeter, and medial-lateral dimension compared to CT. Both modalities significantly underestimated the actual neo-LVOT area (mean bias pre/post TEE: 25.6&#xa0;mm[2], limit of agreement:&#xa0;-92.2&#xa0;mm[2] to 143.3&#xa0;mm[2]; P&#xa0;< 0.001; mean bias pre/post CT: 28.3&#xa0;mm[2], limit of agreement:&#xa0;-65.8&#xa0;mm[2] to 122.4&#xa0;mm[2]; P = 0.046), driven by neo-LVOT underestimation in the group treated with dedicated mitral valve bioprosthesis. Both CT- and TEE-predicted-neo-LVOT areas exhibited an inverse correlation with postprocedural LVOT gradients (r[2]&#xa0;=&#xa0;0.481; P&#xa0;< 0.001 for TEE and r[2]&#xa0;=&#xa0;0.401; P&#xa0;< 0.001 for CT).<br><br>CONCLUSIONS: TEE-derived analysis provides comparable results with CT-derived metrics in predicting the neo-LVOT area and peak gradient after TMVR.}, }
@article {pmid39064141, year = {2024}, author = {Alvi, MA and Pedro, KM and Quddusi, AI and Fehlings, MG}, title = {Advances and Challenges in Spinal Cord Injury Treatments.}, journal = {Journal of clinical medicine}, volume = {13}, number = {14}, pages = {}, pmid = {39064141}, issn = {2077-0383}, abstract = {Spinal cord injury (SCI) is a debilitating condition that is associated with long-term physical and functional disability. Our understanding of the pathogenesis of SCI has evolved significantly over the past three decades. In parallel, significant advances have been made in optimizing the management of patients with SCI. Early surgical decompression, adequate bony decompression and expansile duraplasty are surgical strategies that may improve neurological and functional outcomes in patients with SCI. Furthermore, advances in the non-surgical management of SCI have been made, including optimization of hemodynamic management in the critical care setting. Several promising therapies have also been investigated in pre-clinical studies, with some being translated into clinical trials. Given the recent interest in advancing precision medicine, several investigations have been performed to delineate the role of imaging, cerebral spinal fluid (CSF) and serum biomarkers in predicting outcomes and curating individualized treatment plans for SCI patients. Finally, technological advancements in biomechanics and bioengineering have also found a role in SCI management in the form of neuromodulation and brain-computer interfaces.}, }
@article {pmid39061777, year = {2024}, author = {Manero, A and Rivera, V and Fu, Q and Schwartzman, JD and Prock-Gibbs, H and Shah, N and Gandhi, D and White, E and Crawford, KE and Coathup, MJ}, title = {Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {7}, pages = {}, pmid = {39061777}, issn = {2306-5354}, abstract = {As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.}, }
@article {pmid39061407, year = {2024}, author = {Allum, JHJ and Candreia, C and Honegger, F}, title = {Trunk Instability in the Pitch, Yaw, and Roll Planes during Clinical Balance Tests: Axis Differences and Correlations to vHIT Asymmetries Following Acute Unilateral Vestibular Loss.}, journal = {Brain sciences}, volume = {14}, number = {7}, pages = {}, pmid = {39061407}, issn = {2076-3425}, abstract = {BACKGROUND: Clinical dynamic posturography concentrates on the pitch and roll but not on the yaw plane instability measures. This emphasis may not represent the axis instability observed in clinical stance and gait tasks for patients with balance deficits in comparison to healthy control (HC) subjects, nor the expected instability based on correlations with vestibulo-ocular reflex (VOR) deficits. To examine the axis stability changes with vestibular loss, we measured trunk sway in all three directions (pitch, roll, and yaw) during the stance and gait tasks of patients with acute unilateral vestibular neuritis (aUVN) and compared the results with those of HC. Concurrent changes in VORs were also examined and correlated with trunk balance deficits.<br><br>METHODS: The results of 11 patients (mean age of 61 years) recorded within 6 days of aUVN onset were compared within those of 8 age-matched healthy controls (HCs). All subjects performed a two-legged stance task-standing with eyes closed on foam (s2ecf), a semi-gait task-walking eight tandem steps (tan8), and four gait tasks-walking 3 m with head rotating laterally, pitching, or eyes closed (w3hr, w3hp, w3ec), and walking over four barriers 24 cm high, spaced 1 m apart (barr). The tasks' peak-to-peak yaw, pitch and roll angles, and angular velocities were measured with a gyroscope system (SwayStar[TM]) mounted at L1-3 and combined into three, axis-specific, balance control indexes (BCI), using angles (a) for the tandem gait and barriers task, and angular velocities (v) for all other tasks, as follows: axis BCI = (2 &#xd7; 2ecf)v + 1.5 &#xd7; (w3hr + w3hp + w3ec)v + (tan8 + 12 &#xd7; barr)a.<br><br>RESULTS: Yaw and pitch BCIs were significantly (p &#x2264; 0.004) greater (88 and 30%, respectively) than roll BCIs for aUVN patients. For HCs, only yaw but not pitch BCIs were greater (p = 0.002) than those of roll (72%). The order of BCI aUVN vs. HC differences was pitch, yaw, and roll at 55, 44, and 31%, respectively (p &#x2264; 0.002). This difference with respect to roll corresponded to the known greater yaw plane than roll plane asymmetry (40 vs. 22%) following aUVN based on VOR responses. However, the lower pitch plane asymmetry (3.5%) in VOR responses did not correspond with the pitch plane instability observed in the balance control tests. The increases in pitch plane instability in UVL subjects were, however, highly correlated with those of roll and yaw.<br><br>CONCLUSIONS: These results indicate that greater yaw than pitch and roll trunk motion during clinical balance tasks is common for aUVN patients and HCs. However, aUVN leads to a larger increase in pitch than yaw plane instability and a smaller increase in roll plane instability. This difference with respect to roll corresponds to the known greater yaw plane than roll plane asymmetry (40 vs. 22%) following aUVN observed in VOR responses. However, the lower pitch plane asymmetry (3.5%) in VOR responses does not correspond with the enhanced movements in the pitch plane, observed in balance control tasks. Whether asymmetries in vestibular-evoked myogenic potentials (Vemps) are better correlated with the deficits in pitch plane balance control remains to be investigated. The current results provide a strong rationale for the clinical testing of directional specific balance responses, especially yaw and pitch, and the linking of balance results for yaw and roll to VOR asymmetries.}, }
@article {pmid39061384, year = {2024}, author = {Ruiz, S and Lee, S and Dalboni da Rocha, JL and Ramos-Murguialday, A and Pasqualotto, E and Soares, E and García, E and Fetz, E and Birbaumer, N and Sitaram, R}, title = {Motor Intentions Decoded from fMRI Signals.}, journal = {Brain sciences}, volume = {14}, number = {7}, pages = {}, pmid = {39061384}, issn = {2076-3425}, support = {1211510//Agencia Nacional de Investigaci&#xf3;n y Desarrollo/ ; }, abstract = {Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.}, }
@article {pmid39058783, year = {2024}, author = {Gu, S and Mattar, MG and Tang, H and Pan, G}, title = {Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection.}, journal = {Science advances}, volume = {10}, number = {30}, pages = {eadm8430}, pmid = {39058783}, issn = {2375-2548}, mesh = {*Neural Networks, Computer ; Humans ; Learning/physiology ; Artificial Intelligence ; Recognition, Psychology/physiology ; Algorithms ; }, abstract = {Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.}, }
@article {pmid39058459, year = {2024}, author = {Quan, W and Guo, X and Cui, H and Luo, L and Li, M}, title = {A data compression algorithm with the improved SRLE for high-throughput neural signal acquisition device.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {32}, number = {6}, pages = {3955-3966}, pmid = {39058459}, issn = {1878-7401}, mesh = {*Algorithms ; *Data Compression/methods ; *Wireless Technology/instrumentation ; *Signal Processing, Computer-Assisted/instrumentation ; Brain-Computer Interfaces ; Animals ; Brain/physiology ; }, abstract = {BACKGROUND: Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design.<br><br>OBJECTIVE: To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized.<br><br>METHODS: To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments.<br><br>RESULTS: From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding.<br><br>CONCLUSION: The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.}, }
@article {pmid39057734, year = {2024}, author = {Ávila, FJ}, title = {An Arduino-Powered Device for the Study of White Perception beyond the Visual Chromatic Critical Flicker Fusion Frequency.}, journal = {Journal of imaging}, volume = {10}, number = {7}, pages = {}, pmid = {39057734}, issn = {2313-433X}, abstract = {Arduino microcontrollers are used for a wide range of technological and biomedical applications, such as image classification, computer vision, brain-computer interaction and vision experiments. Here, we present a new cost-effective mini-device based on RGB LED flicker stimulation for the assessment of the chromatic temporal resolution of the visual function based on the concept of critical flicker fusion frequency (CFF). The assembly of the device and its testing in thirty young subjects demonstrate the steady white visual perception of a trichromatic flicker stimulus (mixture of red, green and blue stimuli) beyond the CFF. Macular function as measured by photo-stress recovery time (PRT) was found to be independent of the CFF measurements for red, green and blue lights. However, a statistical correlation was found between the contrast modulation for CFF for red and green stimuli and PRT. Finally, wavefront measurements demonstrate that high-order aberrations improve the temporal resolution of the visual function.}, }
@article {pmid39056472, year = {2024}, author = {Ardaillon, H and Ribault, S and Herault, C and Pisella, L and Lechopier, N and Reilly, KT and Rode, G}, title = {Striking the Balance: Embracing Technology While Upholding Humanistic Principles in Neurorehabilitation.}, journal = {Neurorehabilitation and neural repair}, volume = {38}, number = {9}, pages = {705-710}, doi = {10.1177/15459683241265887}, pmid = {39056472}, issn = {1552-6844}, mesh = {Humans ; Artificial Intelligence/ethics/trends ; Brain-Computer Interfaces/ethics/trends ; *Humanism ; *Neurological Rehabilitation/ethics/methods/trends ; Robotics/ethics/methods/trends ; Congresses as Topic ; }, abstract = {BACKGROUND: The rapid advancement of technology-focused strategies in neurorehabilitation has brought optimism to individuals with neurological disorders, caregivers, and physicians while reshaping medical practice and training.<br><br>OBJECTIVES: We critically examine the implications of technology in neurorehabilitation, drawing on discussions from the 2021 and 2024 World Congress for NeuroRehabilitation. While acknowledging the value of technology, it highlights inherent limitations and ethical concerns, particularly regarding the potential overshadowing of humanistic approaches. The integration of technologies such as robotics, artificial intelligence, neuromodulation, and brain-computer interfaces enriches neurorehabilitation by offering interdisciplinary solutions. However, ethical considerations arise regarding the balance between compensation for deficits, accessibility of technologies, and their alignment with fundamental principles of care. Additionally, the pitfalls of relying solely on neuroimaging data are discussed, stressing the necessity for a more comprehensive understanding of individual variability and clinical skills in rehabilitation.<br><br>RESULTS: From a clinical perspective, the article advocates for realistic solutions that prioritize individual needs, quality of life, and social inclusion over technological allure. It underscores the importance of modesty and honesty in responding to expectations while emphasizing the uniqueness of each individual's experience. Moreover, it argues for the preservation of human-centric approaches alongside technological advancements, recognizing the invaluable role of clinical observation and human interaction in rehabilitation.<br><br>CONCLUSION: Ultimately, the article calls for a balanced attitude that integrates both scientific and humanistic perspectives in neurorehabilitation. It highlights the symbiotic relationship between the sciences and humanities, advocating for philosophical questioning to guide the ethical implementation of new technologies and foster interdisciplinary dialogue.}, }
@article {pmid39056413, year = {2024}, author = {Qiu, J and Zhong, S}, title = {The impact of personalized BCI-VR rehabilitation programs on the recovery of motor functions in patients with stroke-induced hemiplegia.}, journal = {Minerva surgery}, volume = {}, number = {}, pages = {}, doi = {10.23736/S2724-5691.24.10370-X}, pmid = {39056413}, issn = {2724-5438}, }
@article {pmid39056313, year = {2025}, author = {Mathew, J and Adhia, DB and Smith, ML and De Ridder, D and Mani, R}, title = {Closed-Loop Infraslow Brain-Computer Interface can Modulate Cortical Activity and Connectivity in Individuals With Chronic Painful Knee Osteoarthritis: A Secondary Analysis of a Randomized Placebo-Controlled Clinical Trial.}, journal = {Clinical EEG and neuroscience}, volume = {56}, number = {2}, pages = {165-180}, pmid = {39056313}, issn = {2169-5202}, mesh = {Humans ; *Osteoarthritis, Knee/physiopathology/therapy ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; Double-Blind Method ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Chronic Pain/physiopathology/therapy ; Aged ; Brain/physiopathology ; Brain Mapping/methods ; *Cerebral Cortex/physiopathology ; }, abstract = {Introduction. Chronic pain is a percept due to an imbalance in the activity between sensory-discriminative, motivational-affective, and descending pain-inhibitory brain regions. Evidence suggests that electroencephalography (EEG) infraslow fluctuation neurofeedback (ISF-NF) training can improve clinical outcomes. It is unknown whether such training can induce EEG activity and functional connectivity (FC) changes. A secondary data analysis of a feasibility clinical trial was conducted to determine whether EEG ISF-NF training can significantly alter EEG activity and FC between the targeted cortical regions in people with chronic painful knee osteoarthritis (OA). Methods. A parallel, two-arm, double-blind, randomized, sham-controlled clinical trial was conducted. People with chronic knee pain associated with OA were randomized to receive sham NF training or source-localized ratio ISF-NF training protocol to down-train ISF bands at the somatosensory (SSC), dorsal anterior cingulate (dACC), and uptrain pregenual anterior cingulate cortices (pgACC). Resting state EEG was recorded at baseline and immediate post-training. Results. The source localization mapping demonstrated a reduction (P = .04) in the ISF band activity at the left dorsolateral prefrontal cortex (LdlPFC) in the active NF group. Region of interest analysis yielded significant differences for ISF (P = .008), slow (P = .007), beta (P = .043), and gamma (P = .012) band activities at LdlPFC, dACC, and bilateral SSC. The FC between pgACC and left SSC in the delta band was negatively correlated with pain bothersomeness in the ISF-NF group. Conclusion. The EEG ISF-NF training can modulate EEG activity and connectivity in individuals with chronic painful knee osteoarthritis, and the observed EEG changes correlate with clinical pain measures.}, }
@article {pmid39053935, year = {2024}, author = {Yan, Y and An, X and Ma, Y and Jiang, Z and Di, Y and Li, T and Wang, H and Ren, H and Ma, L and Luo, B and Huang, Y}, title = {Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment.}, journal = {Journal of neurointerventional surgery}, volume = {}, number = {}, pages = {}, doi = {10.1136/jnis-2024-022011}, pmid = {39053935}, issn = {1759-8486}, abstract = {BACKGROUND: Early neurological deterioration (END) is a serious complication in patients with large vessel occlusion (LVO) stroke. However, modalities to monitor neurological function after endovascular treatment (EVT) are lacking. This study aimed to evaluate the diagnostic accuracy of a quantitative electroencephalography (qEEG) system for detecting END.<br><br>METHODS: In this prospective, nested case-control study, we included 47 patients with anterior circulation LVO stroke and 34 healthy adults from different clinical centers in Tianjin, China, from May 2023 to January 2024. Patients with stroke underwent EEG at admission and after EVT. The diagnostic accuracy of qEEG features for END was evaluated by receiver operating characteristic curve analysis, and the feasibility was evaluated by the percentage of artifact-free data and device-related adverse events.<br><br>RESULTS: 14 patients with stroke had END (29.8%, 95% CI 16.2% to 43.4%), with most developed within 12&#x2009;hours of recanalization (n=11). qEEG features showed significant correlations with National Institutes of Health Stroke Scale score and infarct volume. After matching, 13 patients with END and 26 controls were included in the diagnostic analysis. Relative alpha power demonstrated the highest diagnostic accuracy for the affected and unaffected hemispheres. The optimal electrode positions were FC3/4 in the unaffected hemisphere, and F7/8 and C3/4 in the affected hemisphere. No device-related adverse events were reported.<br><br>CONCLUSION: The qEEG system exhibits a high diagnostic accuracy for END and may be a promising tool for monitoring neurological function. The identification of optimal electrode positions may enhance device convenience.<br><br>CLINICAL TRIAL REGISTRATION: ChiCTR 2300070829.}, }
@article {pmid39053485, year = {2024}, author = {Eder, M and Xu, J and Grosse-Wentrup, M}, title = {Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6793}, pmid = {39053485}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Benchmarking/methods ; *Neural Networks, Computer ; Humans ; *Electroencephalography/methods ; Algorithms ; Evoked Potentials, Visual/physiology ; Imagination/physiology ; }, abstract = {Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.}, }
@article {pmid39053465, year = {2024}, author = {Zhao, Q and Li, X and Wen, J and He, Y and Zheng, N and Li, W and Cardona, A and Gong, Z}, title = {A two-layer neural circuit controls fast forward locomotion in Drosophila.}, journal = {Current biology : CB}, volume = {34}, number = {15}, pages = {3439-3453.e5}, doi = {10.1016/j.cub.2024.06.060}, pmid = {39053465}, issn = {1879-0445}, mesh = {Animals ; *Locomotion/physiology ; *Drosophila melanogaster/physiology ; Larva/physiology ; Cholinergic Neurons/physiology ; Interneurons/physiology ; Drosophila/physiology ; }, abstract = {Fast forward locomotion is critical for animal hunting and escaping behaviors. However, how the underlying neural circuit is wired at synaptic resolution to decide locomotion direction and speed remains poorly understood. Here, we identified in the ventral nerve cord (VNC) a set of ascending cholinergic neurons (AcNs) to be command neurons capable of initiating fast forward peristaltic locomotion in Drosophila larvae. Targeted manipulations revealed that AcNs are necessary and sufficient for fast forward locomotion. AcNs can activate their postsynaptic partners, A01j and A02j; both are interneurons with locomotory rhythmicity. Activated A01j neurons form a posterior-anteriorly descendent gradient in output activity along the VNC to launch forward locomotion from the tail. Activated A02j neurons exhibit quicker intersegmental transmission in activity that enables fast propagation of motor waves. Our work revealed a global neural mechanism that coordinately controls the launch direction and propagation speed of Drosophila locomotion, furthering the understanding of the strategy for locomotion control.}, }
@article {pmid39053352, year = {2024}, author = {Soriano-Segura, P and Ortiz, M and Iáñez, E and Azorín, JM}, title = {Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential.}, journal = {Computer methods and programs in biomedicine}, volume = {255}, number = {}, pages = {108332}, doi = {10.1016/j.cmpb.2024.108332}, pmid = {39053352}, issn = {1872-7565}, mesh = {Humans ; *Brain-Computer Interfaces ; *Exoskeleton Device ; *Lower Extremity/physiology ; Male ; *Gait/physiology ; Adult ; Electroencephalography ; Young Adult ; Female ; }, abstract = {BACKGROUND AND OBJECTIVE: Brain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP.<br><br>METHODS: The initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts.<br><br>RESULTS: The ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % &#xb1; 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types.<br><br>CONCLUSIONS: The results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.}, }
@article {pmid39052465, year = {2025}, author = {Zhong, XC and Wang, Q and Liu, D and Chen, Z and Liao, JX and Sun, J and Zhang, Y and Fan, FL}, title = {EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {4}, pages = {2484-2495}, doi = {10.1109/JBHI.2024.3431230}, pmid = {39052465}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods/classification ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; *Imagination/physiology ; Adult ; }, abstract = {Motorimagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the test data during training. This is unrealistic and impractical in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments conducted on a simulative dataset, BCI competition IV 2a, 2b, and OpenBMI datasets, demonstrate the superiority and competitive performance of our proposed framework over other state-of-the-art methods. Specifically, EEG-DG achieves average classification accuracies of 81.79% and 87.12% on datasets IV-2a and IV-2b, respectively, and 78.37% and 76.94% for inter-session and inter-subject evaluations on dataset OpenBMI, which even outperforms some domain adaptation methods.}, }
@article {pmid39050380, year = {2024}, author = {Porcaro, C and Diciotti, S and Madan, CR and Marzi, C}, title = {Editorial: Methods and application in fractal analysis of neuroimaging data.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1453284}, pmid = {39050380}, issn = {1662-5161}, }
@article {pmid39049815, year = {2024}, author = {M, B and E, GMK and Raimond, K and George S, T}, title = {An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification.}, journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine}, volume = {238}, number = {7}, pages = {837-847}, doi = {10.1177/09544119241260553}, pmid = {39049815}, issn = {2041-3033}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Automation ; Brain-Computer Interfaces ; Machine Learning ; }, abstract = {Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.}, }
@article {pmid39048655, year = {2024}, author = {Liu, B and Gao, H and Jiang, Y and Wu, J}, title = {Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {17043}, pmid = {39048655}, issn = {2045-2322}, abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.}, }
@article {pmid39046259, year = {2024}, author = {Mansuri, A and Vora, P and Feuerbach, T and Winck, J and Vermeer, AWP and Hoheisel, W and Kierfeld, J and Thommes, M}, title = {A Monte Carlo simulation of tracer diffusion in amorphous polymers.}, journal = {Soft matter}, volume = {20}, number = {31}, pages = {6204-6214}, doi = {10.1039/d4sm00782d}, pmid = {39046259}, issn = {1744-6848}, abstract = {Tracer diffusion in amorphous polymers is a sought-after quantity for a range of technological applications. In this regard, a quantitative description of the so-called decoupling from the reverse proportionality between viscosity and diffusion coefficient into a fractional one remains a challenge requiring a deeper insight. This work employs a Monte Carlo simulation framework in 3 dimensions to investigate the consequences of different scenarios for estimating this fractional exponent on the diffusion coefficient of tracers in polymers near glass transition. To this end, we adopted a continuous-time random walk model for tracer diffusion in the supercooled liquid state. The waiting time distribution of the diffusants was computed based on the rotational correlation times of the polymer. This proposed procedure is of particular interest because it brings the quantity of waiting time (and its statistics) in connection with the measurable observable of rotational times. In the framework of our simulations the aforementioned fractional exponent appears in the relation between the diffusant's waiting time and the rotational time of the diffusion medium. A limited comparison with experimental diffusivities from the literature revealed a reasonable agreement with a fractional exponent on the basis of the molar volumes of the diffusant and the monomeric unit. Finally, an analysis of time-averaged mean squared displacement pointed to normal Brownian dynamics for tracer diffusion in polymers above the glass transition temperature.}, }
@article {pmid39045509, year = {2024}, author = {Gall, R and Mcdonald, N and Huang, X and Wears, A and Price, RB and Ostadabbas, S and Akcakaya, M and Woody, ML}, title = {AttentionCARE: replicability of a BCI for the clinical application of augmented reality-guided EEG-based attention modification for adolescents at high risk for depression.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1360218}, pmid = {39045509}, issn = {1662-5161}, support = {K23 MH119225/MH/NIMH NIH HHS/United States ; }, abstract = {Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away from goal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents.}, }
@article {pmid39045104, year = {2024}, author = {Liang, F and Song, Y and Huang, X and Ren, T and Ji, Q and Guo, Y and Li, X and Sui, Y and Xie, X and Han, L and Li, Y and Ren, Y and Xu, Z}, title = {Assessing breast disease with deep learning model using bimodal bi-view ultrasound images and clinical information.}, journal = {iScience}, volume = {27}, number = {7}, pages = {110279}, pmid = {39045104}, issn = {2589-0042}, abstract = {Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists' scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer.}, }
@article {pmid39043844, year = {2025}, author = {Lin, S and Fan, CY and Wang, HR and Li, XF and Zeng, JL and Lan, PX and Li, HX and Zhang, B and Hu, C and Xu, J and Luo, JH}, title = {Correction: Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice.}, journal = {Molecular psychiatry}, volume = {30}, number = {1}, pages = {365-366}, doi = {10.1038/s41380-024-02658-7}, pmid = {39043844}, issn = {1476-5578}, }
@article {pmid39043690, year = {2024}, author = {Hofmann, UG and Stieglitz, T}, title = {Why some BCI should still be called BMI.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {6207}, pmid = {39043690}, issn = {2041-1723}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; }, abstract = {Neurotechnology is becoming an integral part of clinical practice. In this Comment, the authors advocate for more scrutiny and attention when describing new neurotechnologies with particular attention to the surgical risks involved and the invasiveness.}, }
@article {pmid39038627, year = {2024}, author = {Hou, Y and Liu, W and He, T and Chen, A}, title = {Association between the performance of executive function and the remission of depressive state after clinical treatment in patients with depression.}, journal = {Journal of affective disorders}, volume = {364}, number = {}, pages = {28-36}, doi = {10.1016/j.jad.2024.07.137}, pmid = {39038627}, issn = {1573-2517}, mesh = {Humans ; *Executive Function/physiology ; Male ; Female ; *Memory, Short-Term ; Adult ; Middle Aged ; Neuropsychological Tests ; Depression/psychology/therapy ; Reaction Time ; Remission Induction ; Follow-Up Studies ; Cognitive Dysfunction ; }, abstract = {BACKGROUND: Previous studies have reported that patients with depression have significant cognitive impairment. The aim of this study is to comprehensively evaluate the impairment of executive functions in patients with depression and whether the cognitive behavior performance of executive function is association with remission of depressive state after clinical treatment.<br><br>METHODS: We used cognitive-behavioral test to evaluate the performance of executive functions of 95 inpatients with depression before hospitalization and conducted two follow-up evaluations of their depression status on the 15th day of hospitalization and approximately 9&#xa0;months after discharge.<br><br>RESULTS: The performance of executive function except the accuracy of inhibition control in patients with depression were significantly worse than that of healthy controls. Multivariate linear regression analysis found that the reaction time of working memory not only had a significant linear relationship with the baseline depression scores of patients with depression, but also had a significant linear relationship with the reduced depression scores after two follow-up visits.<br><br>LIMITATIONS: We only used cognitive-behavioral data as indicators to evaluate the cognitive performances of participants and only measured three components of executive function.<br><br>CONCLUSIONS: The reaction time of working memory was a stable and effective predictor of symptom relief in patients with depression after clinical treatment. These results provide initial evidence for working memory to predict the clinical prognosis of inpatients with depression prospectively, which could be further leveraged to improve intervention approaches and analyze the heterogeneity of depression.}, }
@article {pmid39038520, year = {2024}, author = {Campos-Arteaga, G and Flores-Torres, J and Rojas-Thomas, F and Morales-Torres, R and Poyser, D and Sitaram, R and Rodríguez, E and Ruiz, S}, title = {EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process.}, journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology}, volume = {203}, number = {}, pages = {112406}, doi = {10.1016/j.ijpsycho.2024.112406}, pmid = {39038520}, issn = {1872-7697}, mesh = {Humans ; *Neurofeedback/physiology/methods ; *Memory Consolidation/physiology ; Male ; Female ; Young Adult ; Adult ; *Electroencephalography ; Adolescent ; }, abstract = {The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories. The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB. Memory performance was evaluated 24 h and seven days later using an 'old-new' recognition task, where participants distinguished between 'old' and 'new' images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training. In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.}, }
@article {pmid39038519, year = {2024}, author = {Kapgate, DD}, title = {Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment.}, journal = {Behavioural brain research}, volume = {472}, number = {}, pages = {115154}, doi = {10.1016/j.bbr.2024.115154}, pmid = {39038519}, issn = {1872-7549}, mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; *Avatar ; *Brain-Computer Interfaces ; *Electroencephalography ; *Event-Related Potentials, P300/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; *Video Games ; }, abstract = {INTRODUCTION: This research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex.<br><br>METHODS: A BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar's movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement.<br><br>RESULTS AND CONCLUSIONS: Conventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.}, }
@article {pmid39037771, year = {2024}, author = {Stout, JJ and George, AE and Kim, S and Hallock, HL and Griffin, AL}, title = {Using synchronized brain rhythms to bias memory-guided decisions.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {39037771}, issn = {2050-084X}, support = {R21 MH117687/MH/NIMH NIH HHS/United States ; }, mesh = {*Prefrontal Cortex/physiology ; *Decision Making/physiology ; *Theta Rhythm/physiology ; *Hippocampus/physiology ; Animals ; Male ; Memory/physiology ; Brain-Computer Interfaces ; Humans ; Thalamus/physiology ; Optogenetics ; }, abstract = {Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.}, }
@article {pmid39037698, year = {2024}, author = {Ruan, K and Bai, G and Fang, Y and Li, D and Li, T and Liu, X and Lu, B and Lu, Q and Songyang, Z and Sun, S and Wang, Z and Zhang, X and Zhou, W and Zhang, H}, title = {Biomolecular condensates and disease pathogenesis.}, journal = {Science China. Life sciences}, volume = {67}, number = {9}, pages = {1792-1832}, pmid = {39037698}, issn = {1869-1889}, mesh = {Humans ; *Neurodegenerative Diseases/metabolism ; *Neoplasms/metabolism ; *Biomolecular Condensates/metabolism ; Organelles/metabolism ; Animals ; Phase Transition ; Hearing Loss/metabolism ; Immune System Diseases/metabolism ; }, abstract = {Biomolecular condensates or membraneless organelles (MLOs) formed by liquid-liquid phase separation (LLPS) divide intracellular spaces into discrete compartments for specific functions. Dysregulation of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration, tumorigenesis, and many other pathological processes. Herein, we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase separation. We then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders, hearing loss, cancers, and immunological diseases. Finally, we describe the emerging discovery of chemical modulators of phase separation.}, }
@article {pmid39037186, year = {2024}, author = {Liu, H and Bai, Y and Zheng, Q and Liu, J and Zhu, J and Ni, G}, title = {Electrophysiological correlation of auditory selective spatial attention in the "cocktail party" situation.}, journal = {Human brain mapping}, volume = {45}, number = {11}, pages = {e26793}, pmid = {39037186}, issn = {1097-0193}, support = {2023YFF1203500//National Key Research and Development Program of China/ ; 2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 81971698//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; *Attention/physiology ; Female ; Young Adult ; *Electroencephalography ; *Space Perception/physiology ; *Evoked Potentials, Auditory/physiology ; Adult ; *Auditory Perception/physiology ; Acoustic Stimulation ; Brain Mapping ; }, abstract = {The auditory system can selectively attend to the target source in complex environments, the phenomenon known as the "cocktail party" effect. However, the spatiotemporal dynamics of electrophysiological activity associated with auditory selective spatial attention (ASSA) remain largely unexplored. In this study, single-source and multiple-source paradigms were designed to simulate different auditory environments, and microstate analysis was introduced to reveal the electrophysiological correlates of ASSA. Furthermore, cortical source analysis was employed to reveal the neural activity regions of these microstates. The results showed that five microstates could explain the spatiotemporal dynamics of ASSA, ranging from MS1 to MS5. Notably, MS2 and MS3 showed significantly lower partial properties in multiple-source situations than in single-source situations, whereas MS4 had shorter durations and MS5 longer durations in multiple-source situations than in single-source situations. MS1 had insignificant differences between the two situations. Cortical source analysis showed that the activation regions of these microstates initially transferred from the right temporal cortex to the temporal-parietal cortex, and subsequently to the dorsofrontal cortex. Moreover, the neural activity of the single-source situations was greater than that of the multiple-source situations in MS2 and MS3, correlating with the N1 and P2 components, with the greatest differences observed in the superior temporal gyrus and inferior parietal lobule. These findings suggest that these specific microstates and their associated activation regions may serve as promising substrates for decoding ASSA in complex environments.}, }
@article {pmid39036878, year = {2024}, author = {Ye, Y and Deng, Q and Wu, J and Zhong, C and Ma, H and Shi, Y and Li, D and Tang, R and Tang, Y and Jian, J and Zhu, B and Lin, H and Li, L}, title = {Electrostatic Force-Assisted Transfer of Flexible Silicon Photodetector Focal Plane Arrays for Image Sensors.}, journal = {ACS applied materials &amp; interfaces}, volume = {16}, number = {30}, pages = {39572-39579}, doi = {10.1021/acsami.4c05890}, pmid = {39036878}, issn = {1944-8252}, abstract = {Flexible photodetectors are pivotal in contemporary optoelectronic technology applications, such as data reception and image sensing, yet their performance and yield are often hindered by the challenge of heterogeneous integration between photoactive materials and flexible substrates. Here, we showcase the potential of an electrostatic force-assisted transfer printing technique for integrating Si PIN photodiodes onto flexible substrates. This clean and dry process eliminates the need for chemical etchants, making it a highly desirable method for manufacturing high-performance flexible photodetector arrays, expanding their widespread applications in electronic eyes, robotics, and human-machine interaction. As a demonstration, a 5 × 5 flexible Si photodetector focal plane array is constructed for imaging sensors and shaped into a convex semicylindrical form to achieve a π field of view with long-term mechanical and thermal stability. Such an approach provides a high yield rate and consistent performance, with the single photodetector demonstrating exceptional characteristics, including a responsivity of 0.61 A/W, a response speed of 39.77 MHz, a linear dynamic range of 108.53 dB, and a specific detectivity of 2.75 × 10[12] Jones at an applied voltage of -3 V at 940 nm.}, }
@article {pmid39034991, year = {2024}, author = {Ye, Y and Xia, L and Yang, S and Luo, Y and Tang, Z and Li, Y and Han, L and Xie, H and Ren, Y and Na, N}, title = {Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.}, journal = {Frontiers in immunology}, volume = {15}, number = {}, pages = {1438247}, pmid = {39034991}, issn = {1664-3224}, mesh = {Humans ; *Deep Learning ; *Kidney Transplantation/adverse effects ; *Graft Rejection/pathology/immunology/diagnosis ; Biopsy ; Male ; Female ; Allografts/pathology ; Adult ; Middle Aged ; Kidney/pathology/immunology ; Reproducibility of Results ; }, abstract = {BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection.<br><br>METHOD: We collected H&amp;E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations.<br><br>RESULTS: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively.<br><br>CONCLUSION: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.}, }
@article {pmid39033788, year = {2024}, author = {Chen, Y and Liu, S and Hao, Y and Zhao, Q and Ren, J and Piao, Y and Wang, L and Yang, Y and Jin, C and Wang, H and Zhou, X and Gao, JH and Zhang, X and Wei, Z}, title = {Higher emotional synchronization is modulated by relationship quality in romantic relationships and not in close friendships.}, journal = {NeuroImage}, volume = {297}, number = {}, pages = {120733}, doi = {10.1016/j.neuroimage.2024.120733}, pmid = {39033788}, issn = {1095-9572}, mesh = {Humans ; Male ; *Friends/psychology ; *Emotions/physiology ; *Interpersonal Relations ; Female ; Young Adult ; Adult ; *Electroencephalography ; Prefrontal Cortex/physiology ; Social Interaction ; }, abstract = {Emotions are fundamental to social interaction and deeply intertwined with interpersonal dynamics, especially in romantic relationships. Although the neural basis of interaction processes in romance has been widely explored, the underlying emotions and the connection between relationship quality and neural synchronization remain less understood. Our study employed EEG hyperscanning during a non-interactive video-watching paradigm to compare the emotional coordination between romantic couples and close friends. Couples showed significantly greater behavioral and prefrontal alpha synchronization than friends. Notably, couples with low relationship quality required heightened neural synchronization to maintain robust behavioral synchronization. Further support vector machine analysis underscores the crucial role of prefrontal activity in differentiating couples from friends. In summary, our research addresses gaps concerning how intrinsic emotions linked to relationship quality influence neural and behavioral synchronization by investigating a natural non-interactive context, thereby advancing our understanding of the neural mechanisms underlying emotional coordination in romantic relationships.}, }
@article {pmid39032522, year = {2024}, author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V}, title = {Lightweight attention mechanisms for EEG emotion recognition for brain computer interface.}, journal = {Journal of neuroscience methods}, volume = {410}, number = {}, pages = {110223}, doi = {10.1016/j.jneumeth.2024.110223}, pmid = {39032522}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Attention/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: In the realm of brain-computer interfaces (BCI), identifying emotions from electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the intricacy of the signals, and the several channels that make up the signals.<br><br>NEW METHODS: Using dual-stream structure scaling and multiple attention mechanisms (LDMGEEG), a lightweight network is provided to maximize the accuracy and performance of EEG-based emotion identification. Reducing the number of computational parameters while maintaining the current level of classification accuracy is the aim. This network employs a symmetric dual-stream architecture to assess separately time-domain and frequency-domain spatio-temporal maps constructed using differential entropy features of EEG signals as inputs.<br><br>RESULT: The experimental results show that after significantly lowering the number of parameters, the model achieved the best possible performance in the field, with a 95.18&#x202f;% accuracy on the SEED dataset.<br><br>Moreover, it reduced the number of parameters by 98&#x202f;% when compared to existing models.<br><br>CONCLUSION: The proposed method distinct channel-time/frequency-space multiple attention and post-attention methods enhance the model's ability to aggregate features and result in lightweight performance.}, }
@article {pmid39030206, year = {2024}, author = {Kueper, N and Kim, SK and Kirchner, EA}, title = {Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {16690}, pmid = {39030206}, issn = {2045-2322}, support = {01IW20003//Bundesministerium f&#xfc;r Bildung und Forschung/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Exoskeleton Device ; Calibration ; *Intention ; *Movement/physiology ; Adult ; Machine Learning ; Female ; Young Adult ; }, abstract = {Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.}, }
@article {pmid39030159, year = {2024}, author = {Li, D and Yu, J and Zhu, C and Du, Y and Ma, Y and Dong, Y and Wu, Z}, title = {Identifying extracerebellar characteristics in a large cohort of 319 Chinese patients with spinocerebellar ataxia type 3.}, journal = {Chinese medical journal}, volume = {137}, number = {17}, pages = {2131-2133}, pmid = {39030159}, issn = {2542-5641}, mesh = {Adolescent ; Adult ; Aged ; Child ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Cohort Studies ; East Asian People ; *Machado-Joseph Disease/diagnosis/pathology ; }, }
@article {pmid39029500, year = {2024}, author = {Rajpura, P and Cecotti, H and Kumar Meena, Y}, title = {Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6593}, pmid = {39029500}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Artificial Intelligence/trends ; Electroencephalography/methods ; }, abstract = {Objective.This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. Trust in these models can be established by incorporating reasoning or causal relationships from domain experts. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding, often used interchangeably in this context, and formulate a comprehensive framework.Approach.To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology-preferred reporting items for systematic reviews and meta-analyses to review (n = 1246) and analyse (n = 84) studies published in 2015 and onwards for key insights.Main results.The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualise and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.Significance.This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.}, }
@article {pmid39029497, year = {2024}, author = {Habashi, AG and Azab, AM and Eldawlatly, S and Aly, GM}, title = {Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6598}, pmid = {39029497}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Neural Networks, Computer ; Calibration ; Male ; Adult ; Female ; Movement/physiology ; Young Adult ; Deep Learning ; }, abstract = {Objective.Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology.Approach.This study focuses on enhancing cross-subject (CS) MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications.Main results.To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in CS accuracy outperforming state-of-the-art methods.Significance.This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.}, }
@article {pmid39029496, year = {2024}, author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X}, title = {A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6595}, pmid = {39029496}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Brain/physiology ; Models, Neurological ; Movement/physiology ; }, abstract = {Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.}, }
@article {pmid39029493, year = {2024}, author = {Zhou, Q and Zhang, Q and Wang, B and Yang, Y and Yuan, Z and Li, S and Zhao, Y and Zhu, Y and Gao, Z and Zhou, J and Wang, C}, title = {RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad658e}, pmid = {39029493}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Attention/physiology ; Female ; Young Adult ; *Electroencephalography/methods ; Adult ; *Photic Stimulation/methods ; Evoked Potentials/physiology ; Pattern Recognition, Visual/physiology ; }, abstract = {Objective.While brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is widely used in target detection, patterns of event-related potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded 'BCI-blind' users are still lacking.Approach.A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants' attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening.Main results.Statistical analysis showed that the conditions of target concealment and size significantly influenced ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, exhibited lower amplitudes and longer latencies. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets.Significance.The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.}, }
@article {pmid39028609, year = {2024}, author = {Su, J and Wang, J and Wang, W and Wang, Y and Bunterngchit, C and Zhang, P and Hou, ZG}, title = {An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2950-2960}, doi = {10.1109/TNSRE.2024.3431025}, pmid = {39028609}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Hand ; Male ; *Electroencephalography/methods ; Female ; *Evoked Potentials, Visual/physiology ; Middle Aged ; Adult ; Algorithms ; Imagination/physiology ; Stroke/physiopathology ; Gestures ; Aged ; Healthy Volunteers ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; Reproducibility of Results ; }, abstract = {Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77 % and 79.38 ± 6.26 %, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.}, }
@article {pmid39028484, year = {2024}, author = {Blanco-Diaz, CF and Guerrero-Mendez, CD and de Andrade, RM and Badue, C and De Souza, AF and Delisle-Rodriguez, D and Bastos-Filho, T}, title = {Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {12}, pages = {3763-3779}, pmid = {39028484}, issn = {1741-0444}, support = {285/2021//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa e Inova&#xe7;&#xe3;o do Esp&#xed;rito Santo/ ; }, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Biomechanical Phenomena ; Male ; *Lower Extremity/physiology ; Adult ; Female ; Brain-Computer Interfaces ; Bicycling/physiology ; Neural Networks, Computer ; Young Adult ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.}, }
@article {pmid39028220, year = {2025}, author = {Pitt, KM and McKelvey, M and Weissling, K and Thiessen, A}, title = {Brain-computer interface for augmentative and alternative communication access: The initial training needs and learning preferences of speech-language pathologists.}, journal = {International journal of speech-language pathology}, volume = {27}, number = {1}, pages = {14-22}, doi = {10.1080/17549507.2024.2363939}, pmid = {39028220}, issn = {1754-9515}, mesh = {Humans ; *Speech-Language Pathology/education ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Learning ; Male ; Female ; Adult ; }, abstract = {PURPOSE: To enable the codesign of a training framework for brain-computer interfaces for augmentative and alternative communications access (BCI-AAC), the aim of this study is to evaluate the initial BCI-AAC training needs and preferred learning strategies of speech-language pathologists (SLPs) with AAC experience.<br><br>METHOD: Eleven SLPs employed across a broad range of settings completed a semi-structured interview. A grounded theory approach alongside peer debriefing and review, member checking, and triangulation procedures were utilised for thematic analysis to help ensure data reliability and credibility.<br><br>RESULT: Regarding critical training needs, SLPs identified the subthemes of (a) personalisation of intervention, (b) technical aspects, (c) BCI-AAC system types and access, and (d) how to support stakeholders in BCI-AAC implementation. Regarding learning strategy preferences, participants discussed (a) expert guidance and demonstrations, (b) hands-on experience, alongside (c) media and presentations.<br><br>CONCLUSION: Findings present a continuum of critical training needs ranging from more foundational information to more personalised assessment and intervention consideration. These thematic results present a first step in developing a basic framework for SLP training in BCI-AAC to utilise and build from as technology development continues, and provides an important initial starting point for the codesign of clinically focused BCI-AAC trainings.}, }
@article {pmid39026645, year = {2024}, author = {Pang, M and Xu, R and Xi, R and Yao, H and Bao, K and Peng, R and Zhi, H and Zhang, K and He, R and Su, Y and Liu, X and Ming, D}, title = {Molecular understanding of the therapeutic potential of melanin inhibiting natural products.}, journal = {RSC medicinal chemistry}, volume = {15}, number = {7}, pages = {2226-2253}, pmid = {39026645}, issn = {2632-8682}, abstract = {With the development of society and the improvement of people's living standards, there is an increasing demand for melanin-inhibiting products that prioritize health, safety, and efficacy. Therefore, the development of natural products that can safely and efficiently inhibit melanin synthesis is of great social significance and has significant market potential. In this paper, by reviewing the literature reported in recent years, we summarized the natural products with inhibition of melanin synthesis effects that have been put into or not yet put into the market, and classified them according to the chemical groups of their compounds or the extraction methods of the natural products. Through the summary analysis, we found that these compounds mainly include terpenoids, phenylpropanoids, flavonoids and so on, while the natural product extracts mainly include methanol extracts, ethanol extracts, and aqueous extracts. Their main inhibition of melanin synthesis mechanisms include: (1) direct inhibition of tyrosinase activity; (2) down-regulation of the α-MSH-MC1R, Wnt, NO, PI3K/Akt and MAPK pathways through the expression of MITF and its downstream genes TYR, TRP-1, and TRP-2; (3) antioxidant; (4) inhibition of melanocyte growth through cytotoxicity; (5) inhibition of melanosome production and transport. This paper provides an in-depth discussion on the research progress of whitening natural products and their market value. The aim is to offer guidance for future research and development of natural skin whitening products.}, }
@article {pmid39024960, year = {2024}, author = {Li, R and Cao, M and Fu, D and Wei, W and Wang, D and Yuan, Z and Hu, R and Deng, W}, title = {Deciphering language disturbances in schizophrenia: A study using fine-tuned language models.}, journal = {Schizophrenia research}, volume = {271}, number = {}, pages = {120-128}, doi = {10.1016/j.schres.2024.07.016}, pmid = {39024960}, issn = {1573-2509}, support = {2023C03081 AND 2024SSYS007//Key R &amp; D Program of Zhejiang/ ; }, mesh = {Humans ; *Schizophrenia/physiopathology/complications ; Male ; Female ; Adult ; *Language Disorders/etiology/diagnosis/physiopathology ; Middle Aged ; Young Adult ; Schizophrenic Psychology ; }, abstract = {This research presents two stable language metrics, namely Successful Prediction Rate (SPR) and Disfluency (DF), to objectively quantify the linguistic disturbances associated with schizophrenia. These novel language metrics can capture both off-topic responses and incoherence in patients' speech by modeling speech information and fine-tuning techniques. Additionally, these metrics exhibit cultural sensitivity while providing a more comprehensive evaluation of linguistic abnormalities in schizophrenia. This research fine-tuned the ELECTRA Pretrained Language Model on a 750 MB text corpus obtained from major Chinese mental health forums. The effectiveness of the fine-tuned language model is verified on a group comprising 38 individuals diagnosed with schizophrenia and 25 meticulously matched healthy controls. The study explores the association between the fine-tuned language model and the Positive and Negative Syndrome Scale (PANSS) items. The results demonstrate that SPR is higher in healthy controls, indicating better language understanding by the pre-trained language model. Conversely, DF is higher in individuals with schizophrenia, indicating more inconsistent language structure. The relationship between linguistic features and P2 (conceptual disorganization) reveals that patients with positive P2 exhibit lower SPR and higher DF. Binary logistic regression using the combined SPR and DF features achieves 84.5 % accuracy in classifying P2, exceeding the performance of traditional features by 20.5 %. Moreover, the proposed linguistic features outperform traditional linguistic features in discriminating FTD (formal thought disorder), as demonstrated by multivariate linear regression analysis.}, }
@article {pmid39024158, year = {2024}, author = {Han, Y and Du, L and Huang, Q and Cui, D and Li, Y}, title = {Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {7}, pages = {}, doi = {10.1093/cercor/bhae288}, pmid = {39024158}, issn = {1460-2199}, support = {2018B030339001//Key R&amp;D Program of Guangdong Province, China/ ; 2024A1515011690//Guangdong Natural Science Foundation General Program/ ; YG2016ZD06//Shanghai Jiao Tong University Medical Engineering Key Foundation/ ; 2022ZD0208900//STI 2030-Major Projects/ ; }, mesh = {Humans ; *Meditation ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; *Electroencephalography ; Adult ; Middle Aged ; Machine Learning ; }, abstract = {Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.}, }
@article {pmid39022552, year = {2024}, author = {Xi, Y and Schriver, KE and Roe, AW and Zhang, X}, title = {Quantifying tissue temperature changes induced by infrared neural stimulation: numerical simulation and MR thermometry.}, journal = {Biomedical optics express}, volume = {15}, number = {7}, pages = {4111-4131}, pmid = {39022552}, issn = {2156-7085}, abstract = {Infrared neural stimulation (INS) delivered via short pulse trains is an innovative tool that has potential for us use for studying brain function and circuitry, brain machine interface, and clinical use. The prevailing mechanism for INS involves the conversion of light energy into thermal transients, leading to neuronal membrane depolarization. Due to the potential risks of thermal damage, it is crucial to ensure that the resulting local temperature increases are within non-damaging limits for brain tissues. Previous studies have estimated damage thresholds using histological methods and have modeled thermal effects based on peripheral nerves. However, additional quantitative measurements and modeling studies are needed for the central nervous system. Here, we performed 7 T MRI thermometry on ex vivo rat brains following the delivery of infrared pulse trains at five different intensities from 0.1-1.0 J/cm[2] (each pulse train 1,875 nm, 25 us/pulse, 200 Hz, 0.5 s duration, delivered through 200 µm fiber). Additionally, we utilized the General BioHeat Transfer Model (GBHTM) to simulate local temperature changes in perfused brain tissues while delivering these laser energies to tissue (with optical parameters of human skin) via three different sizes of optical fibers at five energy intensities. The simulation results clearly demonstrate that a 0.5 second INS pulse train induces an increase followed by an immediate drop in temperature at stimulation offset. The delivery of multiple pulse trains with 2.5 s interstimulus interval (ISI) leads to rising temperatures that plateau. Both thermometry and modeling results show that, using parameters that are commonly used in biological applications (200 µm diameter fiber, 0.1-1.0 J/cm[2]), the final temperature increase at the end of the 60 sec stimuli duration does not exceed 1°C with stimulation values of 0.1-0.5 J/cm[2] and does not exceed 2°C with stimulation values of up to 1.0 J/cm[2]. Thus, the maximum temperature rise is consistent with the thermal damage threshold reported in previous studies. This study provides a quantitative evaluation of the temperature changes induced by INS, suggesting that existing practices pose minimal major safety concerns for biological tissues.}, }
@article {pmid39021269, year = {2025}, author = {Wang, J and Huang, Y and Wu, L and Sun, Y and Zhang, X and Cao, F}, title = {Sleep-specific repetitive negative thinking processes and prenatal insomnia symptoms: A naturalistic follow-up study from mid- to late-pregnancy.}, journal = {Journal of sleep research}, volume = {34}, number = {1}, pages = {e14272}, doi = {10.1111/jsr.14272}, pmid = {39021269}, issn = {1365-2869}, support = {32071084//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Female ; *Sleep Initiation and Maintenance Disorders/psychology ; Adult ; Pregnancy ; Follow-Up Studies ; *Pregnancy Complications/psychology ; Surveys and Questionnaires ; *Anxiety/psychology ; China ; *Pessimism/psychology ; *Rumination, Cognitive/physiology ; }, abstract = {Insomnia symptoms are highly prevalent during pregnancy; therefore, identifying modifiable risk markers is important for risk prediction and early intervention. This study aimed to examine the role of sleep-specific rumination and sleep-specific worry in prenatal insomnia symptoms. A total of 859 married pregnant women without history of psychiatric illnesses (mean [standard deviation] age, 30.15 [3.86] years; 593 [69.0%] with a bachelor's degree or above) were enrolled from the obstetrical outpatient departments of two tertiary comprehensive hospitals in Shandong, China, who completed assessments of sleep-specific rumination, sleep-specific worry, and insomnia symptoms at baseline (mid-pregnancy) and follow-up (late-pregnancy). Measures included Daytime Insomnia Symptom Response Scale, Anxiety and Preoccupation about Sleep Questionnaire, and Insomnia Severity Index. Our results showed that after controlling for covariates, both sleep-specific rumination and sleep-specific worry showed significant concurrent and prospective associations with insomnia symptoms, and the increases in scores of sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of insomnia symptoms at follow-up. Moreover, the increases in sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of reporting newly developed insomnia symptoms rather than persistent normal sleep. However, the changes in sleep-specific rumination and sleep-specific worry were not significantly associated with the likelihood of reporting persistent or remitted insomnia symptoms rather than persistent normal sleep. In conclusion, sleep-specific rumination and sleep-specific worry were significantly associated with concurrent or subsequent insomnia symptoms; thus, they may be promising cognitive risk markers and intervention targets.}, }
@article {pmid39019065, year = {2024}, author = {Mohammadi, Z and Denman, DJ and Klug, A and Lei, TC}, title = {A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {39019065}, issn = {1741-2552}, support = {R00 EY028612/EY/NEI NIH HHS/United States ; R41 NS132700/NS/NINDS NIH HHS/United States ; }, mesh = {*Algorithms ; *Action Potentials/physiology ; Animals ; *Neurons/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.}, }
@article {pmid39019042, year = {2024}, author = {Zhu, Z and Miao, L and Li, K and Ma, Q and Pan, L and Shen, C and Ge, Q and Du, Y and Yin, L and Yang, H and Xu, X and Zeng, LH and Liu, Y and Xu, H and Li, XM and Sun, L and Yu, YQ and Duan, S}, title = {A hypothalamic-amygdala circuit underlying sexually dimorphic aggression.}, journal = {Neuron}, volume = {112}, number = {18}, pages = {3176-3191.e7}, doi = {10.1016/j.neuron.2024.06.022}, pmid = {39019042}, issn = {1097-4199}, mesh = {Animals ; *Aggression/physiology ; Male ; Female ; *Sex Characteristics ; Mice ; *Hypothalamus/physiology ; *Neural Pathways/physiology ; *Amygdala/physiology ; Mice, Inbred C57BL ; Ventromedial Hypothalamic Nucleus/physiology ; }, abstract = {Male animals often display higher levels of aggression than females. However, the neural circuitry mechanisms underlying this sexually dimorphic aggression remain elusive. Here, we identify a hypothalamic-amygdala circuit that mediates male-biased aggression in mice. Specifically, the ventrolateral part of the ventromedial hypothalamus (VMHvl), a sexually dimorphic region associated with eliciting male-biased aggression, projects densely to the posterior substantia innominata (pSI), an area that promotes similar levels of attack in both sexes of mice. Although the VMHvl innervates the pSI unidirectionally through both excitatory and inhibitory connections, it is the excitatory VMHvl-pSI projections that are strengthened in males to promote aggression, whereas the inhibitory connections that reduce aggressive behavior are strengthened in females. Consequently, the convergent hypothalamic input onto the pSI leads to heightened pSI activity in males, resulting in male-biased aggression. Our findings reveal a sexually distinct excitation-inhibition balance of a hypothalamic-amygdala circuit that underlies sexually dimorphic aggression.}, }
@article {pmid39015823, year = {2024}, author = {Herbert, C and Northoff, G}, title = {Editorial: Analyzing and computing humans - the role of language, culture, brain and health.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1439729}, pmid = {39015823}, issn = {1662-5161}, }
@article {pmid39015472, year = {2024}, author = {He, Q and Yang, C and Xu, Y and Niu, H and Wu, H and Huang, H and Chai, X and Cao, T and Wang, N and Wong, P and He, J and Yang, Y and Zhao, J}, title = {Anatomical-related factors and outcome of percutaneous short-term spinal cord stimulation electrode shift in patients with disorders of consciousness: a retrospective study.}, journal = {Frontiers in aging neuroscience}, volume = {16}, number = {}, pages = {1403156}, pmid = {39015472}, issn = {1663-4365}, abstract = {BACKGROUND: Disorders of consciousness (DoC) represent a spectrum of neurological conditions that pose significant treatment challenges. Percutaneous short-term spinal cord stimulation (SCS) has emerged as a promising experimental diagnostic treatment to assess and potentially improve consciousness levels. However, the effectiveness of this intervention is frequently compromised by the shift of electrodes, particularly in the cervical region, which can negatively affect therapeutic outcomes.<br><br>METHODS: This retrospective study aimed to study if electrodes shift in percutaneous short-term SCS in patients with DoC would affect the outcome. We analyzed the relationship between electrode shift length and patient outcome, as well as the correlation with various anatomical parameters, including the actual length of the cervical spine, linear length, spinal canal transverse diameter, spinal canal diameter, and C2 cone height, in a cohort of patients undergoing the procedure.<br><br>RESULTS: Our findings revealed that in patients with better outcome, there are significant less patient with electrode shift (p&#x2009;=&#x2009;0.019). Further, a linear correlation was found between the length of electrode shift and patients' outcome (Rho&#x2009;=&#x2009;0.583, p&#x2009;=&#x2009;0.002), with longer shift lengths associated with poorer outcomes. Contrary to our expectations, there was no significant association between the measured anatomical parameters and the extent of electrode shift. However, a trend was found between the actual length of the cervical spine and the shift of the electrode (p&#x2009;=&#x2009;0.098). Notably, the shorter spinal canal transverse diameter was found to be significantly associated with better outcome in patients with DoC receiving percutaneous short-term SCS (p&#x2009;=&#x2009;0.033).<br><br>CONCLUSION: These results highlight the clinical importance of electrode stability in the cervical region during SCS treatment for patients with DoC. Ensuring secure placement of electrodes may play a crucial role in enhancing patients' outcome and minimize postoperative complications. Given the lack of association with expected anatomical parameters, future research should investigate other factors that could impact electrode stability to optimize this therapeutic intervention.}, }
@article {pmid39012561, year = {2024}, author = {Lyreskog, DM and Zohny, H and Mann, SP and Singh, I and Savulescu, J}, title = {Decentralising the Self - Ethical Considerations in Utilizing Decentralised Web Technology for Direct Brain Interfaces.}, journal = {Science and engineering ethics}, volume = {30}, number = {4}, pages = {28}, pmid = {39012561}, issn = {1471-5546}, support = {/WT_/Wellcome Trust/United Kingdom ; WT203132/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; IS-BRC-1215-20005//NIHR Oxford Biomedical Research Centre/ ; }, mesh = {Humans ; *Internet ; *Privacy ; *Brain-Computer Interfaces/ethics ; *Personal Autonomy ; Social Responsibility ; Blockchain/ethics ; Computer Security/ethics ; Ownership/ethics ; Politics ; Cognition ; Safety ; Technology/ethics ; }, abstract = {The rapidly advancing field of brain-computer (BCI) and brain-to-brain interfaces (BBI) is stimulating interest across various sectors including medicine, entertainment, research, and military. The developers of large-scale brain-computer networks, sometimes dubbed 'Mindplexes' or 'Cloudminds', aim to enhance cognitive functions by distributing them across expansive networks. A key technical challenge is the efficient transmission and storage of information. One proposed solution is employing blockchain technology over Web 3.0 to create decentralised cognitive entities. This paper explores the potential of a decentralised web for coordinating large brain-computer constellations, and its associated benefits, focusing in particular on the conceptual and ethical challenges this innovation may pose pertaining to (1) Identity, (2) Sovereignty (encompassing Autonomy, Authenticity, and Ownership), (3) Responsibility and Accountability, and (4) Privacy, Safety, and Security. We suggest that while a decentralised web can address some concerns and mitigate certain risks, underlying ethical issues persist. Fundamental questions about entity definition within these networks, the distinctions between individuals and collectives, and responsibility distribution within and between networks, demand further exploration.}, }
@article {pmid39011811, year = {2024}, author = {Blau, R and Russman, SM and Qie, Y and Shipley, W and Lim, A and Chen, AX and Nyayachavadi, A and Ah, L and Abdal, A and Esparza, GL and Edmunds, SJ and Vatsyayan, R and Dunfield, SP and Halder, M and Jokerst, JV and Fenning, DP and Tao, AR and Dayeh, SA and Lipomi, DJ}, title = {Surface-Grafted Biocompatible Polymer Conductors for Stable and Compliant Electrodes for Brain Interfaces.}, journal = {Advanced healthcare materials}, volume = {13}, number = {29}, pages = {e2402215}, pmid = {39011811}, issn = {2192-2659}, support = {DP2-EB029757/EB/NIBIB NIH HHS/United States ; //Alfred P. Sloan Foundation/ ; ECCS-1542148//National Science Foundation/ ; 898571//H2020 Marie Sk&#x142;odowska-Curie Actions/ ; //UC Irvine Materials Research Institute/ ; //UC President's Dissertation Year Fellowship/ ; CHE-1338173//National Science Foundation Major Research Instrumentation Program/ ; 1845683//National Science Foundation/ ; FA9550-19-1-0278//Air Force Office of Scientific Research/ ; UG3NS123723-01//BRAIN Initiative/ ; DMR-2011967//National Science Foundation/ ; CBET-2223566//National Science Foundation Disability and Rehabilitation Engineering/ ; //National Institutes of Health's Brain Research/ ; //Natural Sciences and Engineering Research Council of Canada/ ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; //Kuwait Foundation for the Advancement of Sciences/ ; R01NS123655-01//BRAIN Initiative/ ; DMR-2011924//National Science Foundation/ ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; }, mesh = {*Polymers/chemistry ; *Biocompatible Materials/chemistry ; Surface Properties ; Electrodes ; Electric Conductivity ; Brain/physiology ; Brain-Computer Interfaces ; Animals ; Polyethylene Glycols/chemistry ; Gold/chemistry ; }, abstract = {Durable and conductive interfaces that enable chronic and high-resolution recording of neural activity are essential for understanding and treating neurodegenerative disorders. These chronic implants require long-term stability and small contact areas. Consequently, they are often coated with a blend of conductive polymers and are crosslinked to enhance durability despite the potentially deleterious effect of crosslinking on the mechanical and electrical properties. Here the grafting of the poly(3,4 ethylenedioxythiophene) scaffold, poly(styrenesulfonate)-b-poly(poly(ethylene glycol) methyl ether methacrylate block copolymer brush to gold, in a controlled and tunable manner, by surface-initiated atom-transfer radical polymerization (SI-ATRP) is described. This "block-brush" provides high volumetric capacitance (120 F cm[─3]), strong adhesion to the metal (4 h ultrasonication), improved surface hydrophilicity, and stability against 10 000 charge-discharge voltage sweeps on a multiarray neural electrode. In addition, the block-brush film showed 33% improved stability against current pulsing. This approach can open numerous avenues for exploring specialized polymer brushes for bioelectronics research and application.}, }
@article {pmid39010893, year = {2024}, author = {Song, J and Zhai, Q and Wang, C and Liu, J}, title = {EEGGAN-Net: enhancing EEG signal classification through data augmentation.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1430086}, pmid = {39010893}, issn = {1662-5161}, abstract = {BACKGROUND: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications.<br><br>METHODS: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks.<br><br>RESULTS: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models.<br><br>CONCLUSIONS: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.}, }
@article {pmid39009108, year = {2024}, author = {Liu, ML and Liu, YP and Guo, XX and Wu, ZY and Zhang, XT and Roe, AW and Hu, JM}, title = {Orientation selectivity mapping in the visual cortex.}, journal = {Progress in neurobiology}, volume = {240}, number = {}, pages = {102656}, doi = {10.1016/j.pneurobio.2024.102656}, pmid = {39009108}, issn = {1873-5118}, mesh = {Male ; Female ; Animals ; Macaca ; *Visual Cortex/diagnostic imaging/physiology ; *Neuroimaging/methods ; Photic Stimulation/methods ; *Orientation/physiology ; Visual Perception/physiology ; Deep Learning ; Image Enhancement/methods ; *Optical Imaging/methods ; *Visual Pathways/diagnostic imaging/physiology ; Spatial Processing/physiology ; }, abstract = {The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.}, }
@article {pmid39008625, year = {2024}, author = {Liu, H and Ding, S and Lin, X and Wang, S and Wang, Y and Feng, Z and Song, J}, title = {Bone Fracture Healing under the Intervention of a Stretchable Ultrasound Array.}, journal = {ACS nano}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsnano.4c02426}, pmid = {39008625}, issn = {1936-086X}, abstract = {Ultrasound treatment has been recognized as an effective and noninvasive approach to promote fracture healing. However, traditional rigid ultrasound probe is bulky, requiring cumbersome manual operations and inducing unfavorable side effects when functioning, which precludes the wide application of ultrasound in bone fracture healing. Here, we report a stretchable ultrasound array for bone fracture healing, which features high-performance 1-3 piezoelectric composites as transducers, stretchable multilayered serpentine metal films in a bridge-island pattern as electrical interconnects, soft elastomeric membranes as encapsulations, and polydimethylsiloxane (PDMS) with low curing agent ratio as adhesive layers. The resulting ultrasound array offers the benefits of large stretchability for easy skin integration and effective affecting region for simple skin alignment with good electromechanical performance. Experimental investigations of the stretchable ultrasound array on the delayed union model in femoral shafts of rats show that the callus growth is more active in the second week of treatment and the fracture site is completely osseous healed in the sixth week of treatment. Various bone quality indicators (e.g., bone modulus, bone mineral density, bone tissue/total tissue volume, and trabecular bone thickness) could be enhanced with the intervention of a stretchable ultrasound array. Histological and immunohistochemical examinations indicate that ultrasound promotes osteoblast differentiation, bone formation, and remodeling by promoting the expression of osteopontin (OPN) and runt-related transcription factor 2 (RUNX2). This work provides an effective tool for bone fracture healing in a simple and convenient manner and creates engineering opportunities for applying ultrasound in medical applications.}, }
@article {pmid39006480, year = {2024}, author = {Wang, R and Wang, X and Platt, ML and Sheng, F}, title = {Decomposing loss aversion from a single neural signal.}, journal = {iScience}, volume = {27}, number = {7}, pages = {110153}, pmid = {39006480}, issn = {2589-0042}, abstract = {People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.}, }
@article {pmid39006157, year = {2024}, author = {van Stuijvenberg, OC and Samlal, DPS and Vansteensel, MJ and Broekman, MLD and Jongsma, KR}, title = {The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1420334}, pmid = {39006157}, issn = {1662-5161}, abstract = {AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.}, }
@article {pmid39004179, year = {2024}, author = {White, AJ and Kelly-Hedrick, M and Miranda, SP and Abdelbarr, MM and Lázaro-Muñoz, G and Pouratian, N and Shen, F and Nahed, BV and Williamson, T}, title = {Bioethics and Neurosurgery: An Overview of Existing and Emerging Topics for the Practicing Neurosurgeon.}, journal = {World neurosurgery}, volume = {190}, number = {}, pages = {181-186}, doi = {10.1016/j.wneu.2024.07.051}, pmid = {39004179}, issn = {1878-8769}, mesh = {Humans ; *Neurosurgery/ethics ; *Neurosurgeons/ethics ; *Bioethics ; Informed Consent/ethics ; Neurosurgical Procedures/ethics/methods ; Bioethical Issues ; }, abstract = {Neurosurgery is a field with complex ethical issues. In this article, we aim to provide an overview of key and emerging ethical issues in neurosurgery with a focus on issues relevant to practicing neurosurgeons. These issues include those of informed consent, capacity, clinical trials, emerging neurotechnology, innovation, equity and justice, and emerging bioethics areas including community engagement and organizational ethics. We argue that bioethics can help neurosurgeons think about and address these issues, and, in turn, the field of bioethics can benefit from engagement by neurosurgeons. Several ideas for increasing engagement in bioethics are proposed.}, }
@article {pmid38997424, year = {2024}, author = {Guo, J and Zhou, YL and Yang, Y and Guo, S and You, E and Xie, X and Jiang, Y and Mao, C and Xu, HE and Zhang, Y}, title = {Structural basis of tethered agonism and G protein coupling of protease-activated receptors.}, journal = {Cell research}, volume = {34}, number = {10}, pages = {725-734}, pmid = {38997424}, issn = {1748-7838}, support = {32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82121005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32130022//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82273985//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371249//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100959//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82322070//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171187//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019YFA050880//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 2023YFC2306800//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; }, mesh = {Humans ; *Cryoelectron Microscopy ; *Receptor, PAR-1/metabolism/chemistry ; Protein Binding ; HEK293 Cells ; Models, Molecular ; Signal Transduction ; GTP-Binding Proteins/metabolism/chemistry ; GTP-Binding Protein alpha Subunits, Gq-G11/metabolism/chemistry ; }, abstract = {Protease-activated receptors (PARs) are a unique group within the G protein-coupled receptor superfamily, orchestrating cellular responses to extracellular proteases via enzymatic cleavage, which triggers intracellular signaling pathways. Protease-activated receptor 1 (PAR1) is a key member of this family and is recognized as a critical pharmacological target for managing thrombotic disorders. In this study, we present cryo-electron microscopy structures of PAR1 in its activated state, induced by its natural tethered agonist (TA), in complex with two distinct downstream proteins, the Gq and Gi heterotrimers, respectively. The TA peptide is positioned within a surface pocket, prompting PAR1 activation through notable conformational shifts. Contrary to the typical receptor activation that involves the outward movement of transmembrane helix 6 (TM6), PAR1 activation is characterized by the simultaneous downward shift of TM6 and TM7, coupled with the rotation of a group of aromatic residues. This results in the displacement of an intracellular anion, creating space for downstream G protein binding. Our findings delineate the TA recognition pattern and highlight a distinct role of the second extracellular loop in forming β-sheets with TA within the PAR family, a feature not observed in other TA-activated receptors. Moreover, the nuanced differences in the interactions between intracellular loops 2/3 and the Gα subunit of different G proteins are crucial for determining the specificity of G protein coupling. These insights contribute to our understanding of the ligand binding and activation mechanisms of PARs, illuminating the basis for PAR1's versatility in G protein coupling.}, }
@article {pmid38996764, year = {2024}, author = {Montero, AS and Aliouat, I and Ribon, M and Canney, M and Goldwirt, L and Mourah, S and Berriat, F and Lobsiger, CS and Pradat, PF and Salachas, F and Bruneteau, G and Carpentier, A and Boillée, S}, title = {Effect of ultrasound-mediated blood-spinal cord barrier opening on survival and motor function in females in an amyotrophic lateral sclerosis mouse model.}, journal = {EBioMedicine}, volume = {106}, number = {}, pages = {105235}, pmid = {38996764}, issn = {2352-3964}, mesh = {Animals ; *Amyotrophic Lateral Sclerosis/metabolism/therapy ; Female ; *Disease Models, Animal ; Mice ; *Spinal Cord/metabolism ; *Blood-Brain Barrier/metabolism ; *Insulin-Like Growth Factor I/metabolism ; Mice, Transgenic ; Humans ; Motor Neurons/metabolism ; Ultrasonic Waves ; }, abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive loss of motor neurons. The limited efficacy of recent therapies in clinical development may be linked to lack of drug penetration to the affected motor neurons due to the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB).<br><br>METHODS: In this work, the safety and efficacy of repeated short transient opening of the BSCB by low intensity pulsed ultrasound (US, sonication) was studied in females of an ALS mouse model (B6.Cg-Tg(SOD1&#x2217;G93A)1Gur/J). The BSCB was disrupted using a 1&#xa0;MHz ultrasound transducer coupled to the spinal cord, with and without injection of insulin-like growth factor 1 (IGF1), a neurotrophic factor that has previously shown efficacy in ALS models.<br><br>FINDINGS: Results in wild-type (WT) animals demonstrated that the BSCB can be safely disrupted and IGF1 concentrations significantly enhanced after a single session of transient BSCB disruption (176&#xa0;&#xb1;&#xa0;32&#xa0;&#x3bc;g/g vs. 0.16&#xa0;&#xb1;&#xa0;0.008&#xa0;&#x3bc;g/g, p&#xa0;<&#xa0;0.0001). Five repeated weekly US sessions performed in female ALS mice demonstrated a survival advantage in mice treated with IGF1 and US (US IGF1) compared to treatment with IGF1 alone (176 vs. 166 days, p&#xa0;=&#xa0;0.0038). Surprisingly, this survival advantage was also present in mice treated with US alone vs. untreated mice (178.5 vs. 166.5 days, p&#xa0;=&#xa0;0.0061). Muscle strength did not show difference among the groups. Analysis of glial cell immunoreactivity and microglial transcriptome showing reduced cell proliferation pathways, in addition to lymphocyte infiltration, suggested that the beneficial effect of US or US IGF1 could act through immune cell modulation.<br><br>INTERPRETATION: These results show the first step towards a possible beneficial impact of transient BSCB opening for ALS therapy and suggest implication of immune cells.<br><br>FUNDING: Fondation pour la Recherche M&#xe9;dicale (FRM). Investissements d'avenirANR-10-IAIHU-06, Soci&#xe9;t&#xe9; Fran&#xe7;aise de Neurochirurgie (SFNC), Fond d'&#xe9;tude et de Recherche du Corps Medical (FERCM), Aide &#xe0; la Recherche des Maladies du Cerveau (ARMC), SLA Fondation Recherche (SLAFR), French Ministry for High Education and Research (MENR), Carthera, Laboratoire de Recherche en Technologies Chirurgicales Avanc&#xe9;es (LRTCA).}, }
@article {pmid38996409, year = {2024}, author = {Venot, T and Desbois, A and Corsi, MC and Hugueville, L and Saint-Bauzel, L and De Vico Fallani, F}, title = {Intentional binding for noninvasive BCI control.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad628c}, pmid = {38996409}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; *Imagination/physiology ; Female ; Robotics/methods ; Hand Strength/physiology ; Young Adult ; Intention ; Psychomotor Performance/physiology ; }, abstract = {Objective. Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation.Approach. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm.Main results. Showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.Significance. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.}, }
@article {pmid38993375, year = {2024}, author = {Aljuhani, W and Sayyad, Y}, title = {Orthopedic Research Funding: Assessing the Relationship between Investments and Breakthroughs.}, journal = {Orthopedic reviews}, volume = {16}, number = {}, pages = {120368}, pmid = {38993375}, issn = {2035-8164}, abstract = {Orthopedic research plays a crucial role in improving patient outcomes for musculoskeletal disorders. This narrative review explores the intricate interplay between funding patterns and the trajectory of breakthroughs achieved in this dynamic field. A meticulous search strategy identified studies illuminating the diverse sources of orthopedic research funding, including public funding (government agencies), philanthropic organizations, private sector investment, and international funding bodies. The review further delved into the spectrum of breakthroughs, encompassing fundamental scientific discoveries, technological advancements, and personalized medicine approaches. Public funding emerged as a significant pillar, supporting foundational research that lays the groundwork for future advancements. Philanthropic organizations addressed specific musculoskeletal disorders, often focusing on patient-centric applications. International funding bodies played a role in supporting research in low- and middle-income countries. Breakthroughs extended beyond cutting-edge prosthetics and minimally invasive surgeries, encompassing fundamental discoveries in areas like gene therapy and biomaterials science. Technological advancements included brain-computer interface prosthetics and 3D-printed implants. Personalized medicine offered the potential for tailored treatments based on individual needs and genetic profiles. This review underscores the complex interplay between funding patterns and breakthroughs in orthopedic research. A multifaceted approach is essential for continued progress. Fostering collaboration, optimizing funding models, and prioritizing both foundational and translational research hold the key to unlocking the true potential of orthopedic research and transforming the lives of patients suffering from musculoskeletal disorders.}, }
@article {pmid38993329, year = {2024}, author = {Yan, T and Su, C and Xue, W and Hu, Y and Zhou, H}, title = {Mobile phone short video use negatively impacts attention functions: an EEG study.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1383913}, pmid = {38993329}, issn = {1662-5161}, abstract = {The pervasive nature of short-form video platforms has seamlessly integrated into daily routines, yet it is important to recognize their potential adverse effects on both physical and mental health. Prior research has identified a detrimental impact of excessive short-form video consumption on attentional behavior, but the underlying neural mechanisms remain unexplored. In the current study, we aimed to investigate the effect of short-form video use on attentional functions, measured through the attention network test (ANT). A total of 48 participants, consisting of 35 females and 13 males, with a mean age of 21.8 years, were recruited. The mobile phone short video addiction tendency questionnaire (MPSVATQ) and self-control scale (SCS) were conducted to assess the short video usage behavior and self-control ability. Electroencephalogram (EEG) data were recorded during the completion of the ANT task. The correlation analysis showed a significant negative relationship between MPSVATQ and theta power index reflecting the executive control in the prefrontal region (r = -0.395, p = 0.007), this result was not observed by using theta power index of the resting-state EEG data. Furthermore, a significant negative correlation was identified between MPSVATQ and SCS outcomes (r = -0.320, p = 0.026). These results suggest that an increased tendency toward mobile phone short video addiction could negatively impact self-control and diminish executive control within the realm of attentional functions. This study sheds light on the adverse consequences stemming from short video consumption and underscores the importance of developing interventions to mitigate short video addiction.}, }
@article {pmid38991197, year = {2024}, author = {van Putten, MJAM and Ruijter, BJ and Horn, J and van Rootselaar, AF and Tromp, SC and van Kranen-Mastenbroek, V and Gaspard, N and Hofmeijer, J and , }, title = {Quantitative Characterization of Rhythmic and Periodic EEG Patterns in Patients in a Coma After Cardiac Arrest and Association With Outcome.}, journal = {Neurology}, volume = {103}, number = {3}, pages = {e209608}, doi = {10.1212/WNL.0000000000209608}, pmid = {38991197}, issn = {1526-632X}, mesh = {Humans ; *Coma/physiopathology/etiology ; *Electroencephalography/methods ; Male ; Female ; *Heart Arrest/complications/physiopathology ; Middle Aged ; Aged ; }, abstract = {OBJECTIVES: Rhythmic and periodic patterns (RPPs) on EEG in patients in a coma after cardiac arrest are associated with a poor neurologic outcome. We characterize RPPs using qEEG in relation to outcomes.<br><br>METHODS: Post hoc analysis was conducted on 172 patients in a coma after cardiac arrest from the TELSTAR trial, all with RPPs. Quantitative EEG included corrected background continuity index (BCI*), relative discharge power (RDP), discharge frequency, and shape similarity. Neurologic outcomes at 3 months after arrest were categorized as poor (CPC = 3-5) or good (CPC = 1-2).<br><br>RESULTS: A total of 16 patients (9.3%) had a good outcome. Patients with good outcomes showed later RPP onset (28.5 vs 20.1 hours after arrest, p < 0.05) and higher background continuity at RPP onset (BCI[*] = 0.83 vs BCI[*] = 0.59, p < 0.05). BCI* <0.45 at RPP onset, maximum BCI* <0.76, RDP >0.47, or shape similarity >0.75 were consistently associated with poor outcomes, identifying 36%, 22%, 40%, or 24% of patients with poor outcomes, respectively. In patients meeting both BCI* >0.44 at RPP onset and BCI* >0.75 within 72 hours, the probability of good outcomes doubled to 18%.<br><br>DISCUSSION: Sufficient EEG background continuity before and during RPPs is crucial for meaningful recovery. Background continuity, discharge power, and shape similarity can help select patients with relevant chances of recovery and may guide treatment.<br><br>February 4, 2014, ClinicalTrial.gov, NCT02056236.}, }
@article {pmid38989015, year = {2024}, author = {Barrio-Pujante, A and Bleriot, I and Blasco, L and Fernández-Garcia, L and Pacios, O and Ortiz-Cartagena, C and Cuenca, FF and Oteo-Iglesias, J and Tomás, M}, title = {Regulation of anti-phage defense mechanisms by using cinnamaldehyde as a quorum sensing inhibitor.}, journal = {Frontiers in microbiology}, volume = {15}, number = {}, pages = {1416628}, pmid = {38989015}, issn = {1664-302X}, abstract = {BACKGROUND: Multidrug-resistant bacteria and the shortage of new antibiotics constitute a serious health problem. This problem has led to increased interest in the use of bacteriophages, which have great potential as antimicrobial agents but also carry the risk of inducing resistance. The objective of the present study was to minimize the development of phage resistance in Klebsiella pneumoniae strains by inhibiting quorum sensing (QS) and thus demonstrate the role of QS in regulating defense mechanisms.<br><br>RESULTS: Cinnamaldehyde (CAD) was added to K. pneumoniae cultures to inhibit QS and thus demonstrate the role of the signaling system in regulating the anti-phage defense mechanism. The QS inhibitory activity of CAD in K. pneumoniae was confirmed by a reduction in the quantitative expression of the lsrB gene (AI-2 pathway) and by proteomic analysis. The infection assays showed that the phage was able to infect a previously resistant K. pneumoniae strain in the cultures to which CAD was added. The results were confirmed using proteomic analysis. Thus, anti-phage defense-related proteins from different systems, such as cyclic oligonucleotide-based bacterial anti-phage signaling systems (CBASS), restriction-modification (R-M) systems, clustered regularly interspaced short palindromic repeat-Cas (CRISPR-Cas) system, and bacteriophage control infection (BCI), were present in the cultures with phage but not in the cultures with phage and CAD. When the QS and anti-phage defense systems were inhibited by the combined treatment, proteins related to phage infection and proliferation, such as the tail fiber protein, the cell division protein DamX, and the outer membrane channel protein TolC, were detected.<br><br>CONCLUSION: Inhibition of QS reduces phage resistance in K. pneumoniae, resulting in the infection of a previously resistant strain by phage, with a significant increase in phage proliferation and a significant reduction in bacterial growth. QS inhibitors could be considered for therapeutic application by including them in phage cocktails or in phage-antibiotic combinations to enhance synergistic effects and reduce the emergence of antimicrobial resistance.}, }
@article {pmid38988769, year = {2024}, author = {Liang, J and Wang, Z and Han, J and Zhang, L}, title = {EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1421010}, pmid = {38988769}, issn = {1662-4548}, abstract = {Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.}, }
@article {pmid38988764, year = {2024}, author = {Hong, Y and Ryun, S and Chung, CK}, title = {Evoking artificial speech perception through invasive brain stimulation for brain-computer interfaces: current challenges and future perspectives.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1428256}, pmid = {38988764}, issn = {1662-4548}, abstract = {Encoding artificial perceptions through brain stimulation, especially that of higher cognitive functions such as speech perception, is one of the most formidable challenges in brain-computer interfaces (BCI). Brain stimulation has been used for functional mapping in clinical practices for the last 70 years to treat various disorders affecting the nervous system, including epilepsy, Parkinson's disease, essential tremors, and dystonia. Recently, direct electrical stimulation has been used to evoke various forms of perception in humans, ranging from sensorimotor, auditory, and visual to speech cognition. Successfully evoking and fine-tuning artificial perceptions could revolutionize communication for individuals with speech disorders and significantly enhance the capabilities of brain-computer interface technologies. However, despite the extensive literature on encoding various perceptions and the rising popularity of speech BCIs, inducing artificial speech perception is still largely unexplored, and its potential has yet to be determined. In this paper, we examine the various stimulation techniques used to evoke complex percepts and the target brain areas for the input of speech-like information. Finally, we discuss strategies to address the challenges of speech encoding and discuss the prospects of these approaches.}, }
@article {pmid38986469, year = {2024}, author = {Park, H and Jun, SC}, title = {Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6187}, pmid = {38986469}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; Female ; Adult ; Rest/physiology ; Young Adult ; }, abstract = {Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.}, }
@article {pmid38986468, year = {2025}, author = {He, X and Li, H and Yu, P and Wu, H and Chen, B}, title = {DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.}, journal = {Journal of neural engineering}, volume = {22}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad618a}, pmid = {38986468}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Fatigue/diagnosis/physiopathology ; *Neural Networks, Computer ; Male ; Adult ; Female ; Young Adult ; }, abstract = {Objective. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in the training datasets, owing to bio-individual differences and noisy labels. This study aims to develop an effective framework for cross-subject fatigue detection by addressing these challenges.Approach. In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a domain-adversarial neural network-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples.Main results. Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14%and 97.41%, respectively. These promising results demonstrate our model's effectiveness and excellent generalization capability.Significance. This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the practical application of brain-computer interfaces for fatigue detection.}, }
@article {pmid38986465, year = {2024}, author = {Lo, YT and Jiang, L and Woodington, B and Middya, S and Braendlein, M and Lam, JLW and Lim, MJR and Ng, VYP and Rao, JP and Chan, DWS and Ang, BT}, title = {Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad618c}, pmid = {38986465}, issn = {1741-2552}, mesh = {Animals ; Rats ; *Rats, Wistar ; *Electrocorticography/methods/instrumentation ; *Motor Cortex/physiology ; *Electrodes, Implanted ; Male ; Microelectrodes ; Action Potentials/physiology ; Equipment Design/methods ; Spatial Navigation/physiology ; Brain-Computer Interfaces ; Equipment Failure Analysis/methods ; }, abstract = {Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.}, }
@article {pmid38986460, year = {2024}, author = {Ploesser, M and Abraham, ME and Broekman, MLD and Zincke, MT and Beach, CA and Urban, NB and Ben-Haim, S}, title = {Electrical and Magnetic Neuromodulation Technologies and Brain-Computer Interfaces: Ethical Considerations for Enhancement of Brain Function in Healthy People - A Systematic Scoping Review.}, journal = {Stereotactic and functional neurosurgery}, volume = {102}, number = {5}, pages = {308-324}, pmid = {38986460}, issn = {1423-0372}, mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Brain/physiology ; Transcranial Magnetic Stimulation/methods/ethics ; Deep Brain Stimulation/ethics/methods ; }, abstract = {INTRODUCTION: This scoping review aimed to synthesize the fragmented evidence on ethical concerns related to the use of electrical and magnetic neuromodulation technologies, as well as brain-computer interfaces for enhancing brain function in healthy individuals, addressing the gaps in understanding spurred by rapid technological advancements and ongoing ethical debates.<br><br>METHODS: The following databases and interfaces were queried: MEDLINE (via PubMed), Web of Science, PhilPapers, and Google Scholar. Additional references were identified via bibliographies of included citations. References included experimental studies, reviews, opinion papers, and letters to editors published in peer-reviewed journals that explored the ethical implications of electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy adult or pediatric populations.<br><br>RESULTS: A total of 23 articles were included in the review, of which the majority explored expert opinions in the form of qualitative studies or surveys as well as reviews. Two studies explored the view of laypersons on the topic. The majority of evidence pointed to ethical concerns relating to a lack of sufficient efficacy and safety data for these new technologies, with the risks of invasive procedures potentially outweighing the benefits. Additionally, concerns about potential socioeconomic consequences were raised that could further exacerbate existing socioeconomic inequalities, as well as the risk of changes to person and environment.<br><br>CONCLUSION: This scoping review highlights a critical shortage of ethical research on electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy individuals, with key concerns regarding the safety, efficacy, and socioeconomic impacts of neuromodulation technologies. It underscores the urgent need for integrating ethical considerations into neuroscientific research to address significant gaps and ensure equitable access and outcomes.}, }
@article {pmid38986452, year = {2024}, author = {Jochumsen, M and Poulsen, KB and Sørensen, SL and Sulkjær, CS and Corydon, FK and Strauss, LS and Roos, JB}, title = {Single-trial movement intention detection estimation in patients with Parkinson's disease: a movement-related cortical potential study.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad6189}, pmid = {38986452}, issn = {1741-2552}, mesh = {Humans ; *Parkinson Disease/physiopathology/diagnosis ; Male ; Female ; Aged ; *Movement/physiology ; Middle Aged ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Intention ; Electromyography/methods ; }, abstract = {Objectives. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.Approach. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).Main results. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.Significance. The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.}, }
@article {pmid38986186, year = {2024}, author = {Zhang, D and Li, H and Xie, J}, title = {Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {179}, number = {}, pages = {106497}, doi = {10.1016/j.neunet.2024.106497}, pmid = {38986186}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Unsupervised Machine Learning ; Supervised Machine Learning ; Algorithms ; Brain/physiology ; }, abstract = {The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.}, }
@article {pmid38985934, year = {2024}, author = {Cheng, Y and Yan, L and Shoukat, MU and She, J and Liu, W and Shi, C and Wu, Y and Yan, F}, title = {An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control.}, journal = {Journal of neurophysiology}, volume = {132}, number = {3}, pages = {809-821}, doi = {10.1152/jn.00029.2024}, pmid = {38985934}, issn = {1522-1598}, support = {61876137//MOST | National Natural Science Foundation of China (NSFC)/ ; S202102GG001//Science and Technology Planning Project of the third Division of Xingjiang Production and Construction Corps/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; Electroencephalography/methods ; Photic Stimulation/methods ; Aircraft ; }, abstract = {Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW &amp; NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.}, }
@article {pmid38984886, year = {2024}, author = {Zhu, F and Cai, J and Zheng, H and Liang, Z and Zhang, Y}, title = {Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.}, journal = {The Review of scientific instruments}, volume = {95}, number = {7}, pages = {}, doi = {10.1063/5.0208524}, pmid = {38984886}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; *Algorithms ; Humans ; Imagination/physiology ; }, abstract = {The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.}, }
@article {pmid38984151, year = {2024}, author = {Darvishi, S and Datta Gupta, A and Hamilton-Bruce, A and Koblar, S and Baumert, M and Abbott, D}, title = {Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.}, journal = {PNAS nexus}, volume = {3}, number = {7}, pages = {pgae240}, pmid = {38984151}, issn = {2752-6542}, abstract = {This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.}, }
@article {pmid38982461, year = {2024}, author = {Daigle, L and Khalid, H and Gagnon, CA and Arsenault, J and Bienzle, D and Bisson, SK and Blais, MC and Denis-Robichaud, J and Forest, C and Grenier St-Sauveur, V and Koszegi, M and MacNicol, J and Nantel-Fortier, N and Nury, C and Prystajecky, N and Fraser, E and Carabin, H and Aenishaenslin, C}, title = {High prevalence of SARS-CoV-2 antibodies and low prevalence of SARS-CoV-2 RNA in cats recently exposed to human cases.}, journal = {BMC veterinary research}, volume = {20}, number = {1}, pages = {304}, pmid = {38982461}, issn = {1746-6148}, mesh = {Cats ; Animals ; *SARS-CoV-2/immunology ; *Cat Diseases/virology/epidemiology ; *RNA, Viral ; *Antibodies, Viral/blood ; *COVID-19/veterinary/epidemiology/diagnosis/virology ; Cross-Sectional Studies ; Humans ; Female ; Male ; Prevalence ; }, abstract = {BACKGROUND: The primary objective of this cross-sectional study, conducted in Québec and Bristish Columbia (Canada) between February 2021 and January 2022, was to measure the prevalence of viral RNA in oronasal and rectal swabs and serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amongst cats living in households with at least one confirmed human case. Secondary objectives included a description of potential risk factors for the presence of SARS-CoV-2 antibodies and an estimation of the association between the presence of viral RNA in swabs as well as SARS-CoV-2 antibodies and clinical signs. Oronasal and rectal swabs and sera were collected from 55 cats from 40 households at most 15 days after a human case confirmation, and at up to two follow-up visits. A RT-qPCR assay and an ELISA were used to detect SARS-CoV-2 RNA in swabs and serum SARS-CoV-2 IgG antibodies, respectively. Prevalence and 95% Bayesian credibility intervals (BCI) were calculated, and associations were evaluated using prevalence ratio and 95% BCI obtained from Bayesian mixed log-binomial models.<br><br>RESULTS: Nine (0.16; 95% BCI&#x2009;=&#x2009;0.08-0.28) and 38 (0.69; 95% BCI&#x2009;=&#x2009;0.56-0.80) cats had at least one positive RT-qPCR and at least one positive serological test result, respectively. No risk factor was associated with the prevalence of SARS-CoV-2 serum antibodies. The prevalence of clinical signs suggestive of COVID-19 in cats, mainly sneezing, was 2.12 (95% BCI&#x2009;=&#x2009;1.03-3.98) times higher amongst cats with detectable viral RNA compared to those without.<br><br>CONCLUSIONS: We showed that cats develop antibodies to SARS-CoV-2 when exposed to recent human cases, but detection of viral RNA on swabs is rare, even when sampling occurs soon after confirmation of a human case. Moreover, cats with detectable levels of virus showed clinical signs more often than cats without signs, which can be useful for the management of such cases.}, }
@article {pmid38982198, year = {2024}, author = {Mejia, LA}, title = {Real control in virtual rats.}, journal = {Nature neuroscience}, volume = {27}, number = {7}, pages = {1214}, doi = {10.1038/s41593-024-01708-1}, pmid = {38982198}, issn = {1546-1726}, mesh = {Animals ; Rats ; *Virtual Reality ; Brain-Computer Interfaces ; }, }
@article {pmid38980788, year = {2024}, author = {Li, R and Bai, D and Li, Z and Yang, S and Liu, W and Zhang, Y and Zhou, J and Luo, J and Wang, W}, title = {The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2564-2578}, doi = {10.1109/TNSRE.2024.3425636}, pmid = {38980788}, issn = {1558-0210}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Electroencephalography/methods ; Male ; *Robotics/methods ; *Hand Strength/physiology ; *Brain-Computer Interfaces ; Young Adult ; Female ; Adult ; *Neural Networks, Computer ; Healthy Volunteers ; }, abstract = {In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70 %, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18 % for the online experiment. All the experimental results demonstrated the effectiveness of the brain-computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.}, }
@article {pmid38980648, year = {2024}, author = {Pan, G and Zhao, B and Zhang, M and Guo, Y and Yan, Y and Dai, D and Zhang, X and Yang, H and Ni, J and Huang, Z and Li, X and Duan, S}, title = {Nucleus Accumbens Corticotropin-Releasing Hormone Neurons Projecting to the Bed Nucleus of the Stria Terminalis Promote Wakefulness and Positive Affective State.}, journal = {Neuroscience bulletin}, volume = {40}, number = {11}, pages = {1602-1620}, pmid = {38980648}, issn = {1995-8218}, mesh = {Animals ; *Septal Nuclei/physiology/metabolism ; *Nucleus Accumbens/metabolism/physiology ; *Corticotropin-Releasing Hormone/metabolism ; *Wakefulness/physiology ; *Neurons/physiology/metabolism ; Male ; Mice ; *Mice, Inbred C57BL ; Neural Pathways/physiology ; Anxiety/metabolism/physiopathology ; Reward ; }, abstract = {The nucleus accumbens (NAc) plays an important role in various emotional and motivational behaviors that rely on heightened wakefulness. However, the neural mechanisms underlying the relationship between arousal and emotion regulation in NAc remain unclear. Here, we investigated the roles of a specific subset of inhibitory corticotropin-releasing hormone neurons in the NAc (NAc[CRH]) in regulating arousal and emotional behaviors in mice. We found an increased activity of NAc[CRH] neurons during wakefulness and rewarding stimulation. Activation of NAc[CRH] neurons converts NREM or REM sleep to wakefulness, while inhibition of these neurons attenuates wakefulness. Remarkably, activation of NAc[CRH] neurons induces a place preference response (PPR) and decreased basal anxiety level, whereas their inactivation induces a place aversion response and anxious state. NAc[CRH] neurons are identified as the major NAc projection neurons to the bed nucleus of the stria terminalis (BNST). Furthermore, activation of the NAc[CRH]-BNST pathway similarly induced wakefulness and positive emotional behaviors. Taken together, we identified a basal forebrain CRH pathway that promotes the arousal associated with positive affective states.}, }
@article {pmid38977483, year = {2024}, author = {Ha, J and Park, MK and Park, SN and Cho, HH and Choi, JY and Lee, CK and Lee, IW and Moon, IJ and Jung, JY and Jung, J and Lee, KY and Oh, JH and Park, HJ and Seo, JH and Song, JJ and Kim, H and Jang, JH and Choung, YH}, title = {Tinnitus reduction after active bone-conduction implantation in patients with single-sided deafness: a prospective multicenter study.}, journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery}, volume = {281}, number = {11}, pages = {5677-5686}, pmid = {38977483}, issn = {1434-4726}, mesh = {Humans ; Prospective Studies ; Male ; *Tinnitus/surgery/physiopathology ; Female ; Middle Aged ; *Bone Conduction ; *Hearing Loss, Unilateral/rehabilitation/surgery/physiopathology ; Adult ; Aged ; Treatment Outcome ; Hearing Aids ; Surveys and Questionnaires ; }, abstract = {PURPOSE: Single-sided deafness (SSD) presents significant challenges for patients, including compromised sound localization, reduced speech recognition, and often, tinnitus. These issues are typically addressed using interventions such as cochlear implantation (CI) and bone conduction implant (BCI). However, evidence regarding the efficacy of BCI in reducing tinnitus in SSD patients remains limited. This study explored the ability of a novel active transcutaneous BCI (Bonebridge BCI602) to alleviate tinnitus in SSD patients.<br><br>STUDY DESIGN: Prospective cohort multicenter study.<br><br>SETTING: Tertiary referral hospitals.<br><br>METHODS: A prospective multicenter study of 30 SSD patients was conducted. The patients were divided into two groups: those with (n&#x2009;=&#x2009;19) and without (n&#x2009;=&#x2009;11) tinnitus. Audiometric assessments, subjective questionnaires including the Abbreviated Profile of Hearing Aid Benefit (APHAB) and the Bern Benefit in Single-Sided Deafness (BBSS), and tinnitus evaluations with the Tinnitus Handicap Inventory (THI) and tinnitogram were conducted before and after BCI surgery.<br><br>RESULTS: THI scores after surgery were significantly reduced in SSD patients with tinnitus. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups; however, the former group exhibited a significantly greater improvement in the APHAB questionnaire score. According to tinnitograms, the loudness of tinnitus decreased, particularly in patients with ipsilateral tinnitus. Patients with residual hearing had greater reductions in their THI scores. However, three patients without residual hearing had a relative worsening of tinnitus after surgery.<br><br>CONCLUSION: The Bonebridge BCI602 effectively reduced tinnitus in SSD patients, particularly in those with residual hearing. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups. These findings demonstrate the therapeutic potential of BCI for managing SSD and associated tinnitus.}, }
@article {pmid38977410, year = {2024}, author = {M, AL and R, R}, title = {Rehabilitation Based on BCI: An Innovative Enhancement for Sensorimotor Cortex Rhythms Systemization.}, journal = {Advanced biology}, volume = {8}, number = {9}, pages = {e2400004}, doi = {10.1002/adbi.202400004}, pmid = {38977410}, issn = {2701-0198}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Sensorimotor Cortex/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {The research proposes a novel strategy for categorizing electroencephalograms (EEG) in real-time brain-computer interfaces that have rehabilitation applications. The methodology utilizes Five Cross-Common Spatial Patterns (FCCSP) to develop a motor movement/imagery systemization model that extracts multi-domain characteristics with excellent performance. The goal is to eliminate the impact caused by EEG's nonstationarity. The article highlights the findings of a real-time technique that is incorporated into a comprehensive prediction system, and it offers an innovative method to boost accuracy in real-time Sensory-Motor cortex Rhythms (SMR). The accuracy increased from 57.14% using raw EEG to 85.71% after preprocessing, and from 58.08% to 97.94% in public domain SMR. The proposed Butterworth bandpass filter is optimized using the FCCSP to determine the ideal bandwidth that incorporates the whole EEG features in beta waves. The Hybrid Systemization of the Correlated Feature Removal classifier is then integrated with the FCCSP method to create improved predictive models. As a consequence, while applied to real-time and PhysioNet datasets, the outcome system achieved outstanding accuracy values of 85.71% and 97.94%, respectively. This demonstrates the robustness of the strategy to increase SMR prediction efficiency.}, }
@article {pmid38976678, year = {2024}, author = {Zhu, H and Beierholm, U and Shams, L}, title = {BCI Toolbox: An open-source python package for the Bayesian causal inference model.}, journal = {PLoS computational biology}, volume = {20}, number = {7}, pages = {e1011791}, pmid = {38976678}, issn = {1553-7358}, mesh = {*Bayes Theorem ; Humans ; *Algorithms ; Computational Biology/methods ; Software ; Brain-Computer Interfaces ; }, abstract = {Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.}, }
@article {pmid38976469, year = {2024}, author = {Wang, Z and Shen, L and Yang, Y and Ma, Y and Man Wong, C and Liu, Z and Lin, C and Tin Hon, C and Qian, T and Wan, F}, title = {A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2470-2481}, doi = {10.1109/TNSRE.2024.3424410}, pmid = {38976469}, issn = {1558-0210}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; *Machine Learning ; Least-Squares Analysis ; Nonlinear Dynamics ; Reproducibility of Results ; Male ; }, abstract = {The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.}, }
@article {pmid38975469, year = {2024}, author = {Kalani, M and Anjankar, A}, title = {Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment.}, journal = {Cureus}, volume = {16}, number = {6}, pages = {e61706}, pmid = {38975469}, issn = {2168-8184}, abstract = {Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.}, }
@article {pmid38975192, year = {2024}, author = {Li, Y and Nie, Y and Quan, Z and Zhang, H and Song, R and Feng, H and Cheng, X and Liu, W and Geng, X and Sun, X and Fu, Y and Wang, S}, title = {Brain-machine interactive neuromodulation research tool with edge AI computing.}, journal = {Heliyon}, volume = {10}, number = {12}, pages = {e32609}, pmid = {38975192}, issn = {2405-8440}, abstract = {Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.}, }
@article {pmid38974661, year = {2024}, author = {Liu, Y and Gao, X and Zhang, Y and Zeng, M and Liu, Y and Wu, Y and Hu, W and Lai, Y and Liao, J}, title = {Geographical variation in dementia prevalence across China: a geospatial analysis.}, journal = {The Lancet regional health. Western Pacific}, volume = {47}, number = {}, pages = {101117}, pmid = {38974661}, issn = {2666-6065}, abstract = {BACKGROUND: Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces.<br><br>METHODS: In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the China&#xa0;Health and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates.<br><br>FINDINGS: The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation.<br><br>INTERPRETATION: Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment.<br><br>FUNDING: National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).}, }
@article {pmid38974471, year = {2024}, author = {Zhang, J and Zhang, Y and Zhang, X and Xu, B and Zhao, H and Sun, T and Wang, J and Lu, S and Shen, X}, title = {A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.}, journal = {iScience}, volume = {27}, number = {6}, pages = {110164}, pmid = {38974471}, issn = {2589-0042}, abstract = {This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.}, }
@article {pmid38973051, year = {2024}, author = {Zou, X and Chen, B and Li, Y}, title = {[Research status and progress of bilateral cochlear implantation].}, journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery}, volume = {38}, number = {7}, pages = {666-670}, pmid = {38973051}, issn = {2096-7993}, mesh = {Humans ; *Cochlear Implantation/methods ; *Cochlear Implants ; Speech Perception ; Hearing Loss/surgery ; }, abstract = {With the development of social economic and technology, Cochlear Implantation has became an effective therapy for patients who suffered from severe or profound hearing impairment. In the meantime, patients' demands for sound and auditory quality are also increasing. In terms of speech recognition, localization, and auditory quality, bilateral hearing is closer to the auditory experience of normal individuals, so bilateral cochlear implantation（BCI） emerged as the times require. In this article, we will introduce the status and progress of bimodal regarding to the following aspects: the brief history, the advantages of BCI, different methods for BCI, and the problems encountered in BCI.}, }
@article {pmid38971641, year = {2024}, author = {Casasanta, N and Patel, R and Raymond, S and Kier, MW and Blanter, J and Sohval, S and Hovstadius, M and Wu, C and Zimmerman, B and Cascetta, K and Bagiella, E and Tiersten, A}, title = {Correlating Predicted Adjuvant Therapy Benefit and Risk of Recurrence Between Breast Cancer Index (BCI) and the 21-Gene Oncotype DX Recurrence Score (RS).}, journal = {Clinical breast cancer}, volume = {24}, number = {7}, pages = {585-596}, doi = {10.1016/j.clbc.2024.06.005}, pmid = {38971641}, issn = {1938-0666}, mesh = {Humans ; Female ; *Breast Neoplasms/genetics/pathology/therapy ; Middle Aged ; *Neoplasm Recurrence, Local/genetics/pathology ; Retrospective Studies ; Adult ; Chemotherapy, Adjuvant ; Gene Expression Profiling ; Aged ; Prognosis ; Biomarkers, Tumor/genetics/metabolism ; Antineoplastic Agents, Hormonal/therapeutic use ; Receptors, Estrogen/metabolism ; Risk Assessment/methods ; }, abstract = {INTRODUCTION: Breast Cancer Index (BCI) is a genomic assay that evaluates the benefit of extending endocrine therapy (ET) from 5 to 10 years and predicts recurrence risk (RR). We evaluated the association between BCI and Oncotype DX (ODX).<br><br>PATIENTS: Women with hormone receptor (HR)-positive early-stage breast cancer (EBC) who had BCI and ODX performed were included.<br><br>METHODS: We performed a retrospective review of women with HR-positive EBC. BCI was categorized as predictive of extended ET versus not and ODX recurrence score (RS) as low (0-10), intermediate (11-25), and high (26-100). Univariate and multivariable logistic and linear regression models assessed the relationship between BCI and ODX, factors associated with each, and discordance between scores.<br><br>RESULTS: We identified 153 women, 22% were premenopausal and 18% were lymph node positive. The univariate logistic and linear models revealed an association between BCI predictive score and ODX RS (OR 7.84, CI, 2.63-23.36, P < .001) and log of BCI RR (Beta 0.04, CI, 0.02-0.06, P < .001). Seventy-four percent of BCI predictive scores were concordant with ODX RS and 83% of BCI RR was concordant with ODX RR. In a univariate logistic regression model, BCI predictive of ET benefit was associated with discordance (OR 28.00, CI, 10.58-74.02, P < .001). Higher ODX RR was associated with discordance (OR 1.92, CI, 1.42-2.59, P < .001).<br><br>CONCLUSION: We found a significant association between ODX and BCI predictive and prognostic scores. BCI predictive of extended ET benefit was associated with discordance with ODX RS. Higher predicted RR on ODX was associated with discordance with BCI predicted RR.}, }
@article {pmid38969758, year = {2024}, author = {de Borman, A and Wittevrongel, B and Dauwe, I and Carrette, E and Meurs, A and Van Roost, D and Boon, P and Van Hulle, MM}, title = {Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {818}, pmid = {38969758}, issn = {2399-3642}, support = {11K2324N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4321N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0C1522N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4118N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4321N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0C1522N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; PDM/19/176//KU Leuven (Katholieke Universiteit Leuven)/ ; C24/18/098//KU Leuven (Katholieke Universiteit Leuven)/ ; AKUL 043//Hercules Foundation/ ; 857375//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; }, mesh = {Humans ; *Electrocorticography/methods ; *Speech/physiology ; Male ; Female ; Adult ; *Brain-Computer Interfaces ; *Imagination/physiology ; Young Adult ; Motor Cortex/physiology ; }, abstract = {Speech brain-computer interfaces aim to support communication-impaired patients by translating neural signals into speech. While impressive progress was achieved in decoding performed, perceived and attempted speech, imagined speech remains elusive, mainly due to the absence of behavioral output. Nevertheless, imagined speech is advantageous since it does not depend on any articulator movements that might become impaired or even lost throughout the stages of a neurodegenerative disease. In this study, we analyzed electrocortigraphy data recorded from 16 participants in response to 3 speech modes: performed, perceived (listening), and imagined speech. We used a linear model to detect speech events and examined the contributions of each frequency band, from delta to high gamma, given the speech mode and electrode location. For imagined speech detection, we observed a strong contribution of gamma bands in the motor cortex, whereas lower frequencies were more prominent in the temporal lobe, in particular of the left hemisphere. Based on the similarities in frequency patterns, we were able to transfer models between speech modes and participants with similar electrode locations.}, }
@article {pmid38969345, year = {2024}, author = {Kitta, T and Wada, N and Shinohara, S and Hayashi, N and Yamamura, H and Yamamoto, T and Takagi, H and Hatakeyama, T and Nagabuchi, M and Morishita, S and Tsunekawa, R and Ohtani, M and Kobayashi, S and Hori, JI and Kakizaki, H}, title = {Validation of the area under the Watts factor curve during the voiding cycle as a novel parameter for diagnosing detrusor underactivity in females.}, journal = {International journal of urology : official journal of the Japanese Urological Association}, volume = {31}, number = {10}, pages = {1121-1127}, doi = {10.1111/iju.15531}, pmid = {38969345}, issn = {1442-2042}, mesh = {Humans ; Female ; Middle Aged ; *Urodynamics ; Aged ; Adult ; *Lower Urinary Tract Symptoms/diagnosis/physiopathology/etiology ; *Urination/physiology ; *Urinary Bladder, Underactive/diagnosis/physiopathology ; *Urinary Bladder/physiopathology ; *Area Under Curve ; ROC Curve ; Muscle Contraction/physiology ; Aged, 80 and over ; Urinary Bladder Neck Obstruction/diagnosis/physiopathology/complications ; }, abstract = {OBJECTIVE: Detrusor underactivity (DU) is a common cause of lower urinary tract symptoms (LUTS). To date, no consensus has been reached on the urodynamic criteria for defining DU. We previously proposed the area under the curve of the Watts factor (WF-AUC) as a new parameter for diagnosing DU. By comparing previously reported five criteria for DU and WF-AUC, we analyzed whether the WF-AUC could assess detrusor contraction in women with LUTS.<br><br>METHODS: Using urodynamic data of consecutive 77 women with LUTS, first, we classified DU based on previously reported five criteria. Second, we assessed the potential correlation between multiple parameters and WF-AUC. Third, receiver operating characteristic curve analysis was performed to determine the cutoff value of WF-AUC for diagnosing DU based on previously reported five criteria. Fourth, a linear regression analysis was conducted and compared using multiple criteria and female bladder outlet obstruction index (BOOIf).<br><br>RESULTS: WF-AUC was positively correlated with the maximum values of WF, bladder contractility index (BCI), and projected isovolumetric pressure 1 (PIP1) with correlation coefficients of 0.63, 0.57, and 0.34, respectively. AUC for diagnosing DU based on previously reported five criteria ranging from 0.773 to 0.896 with different cutoff values of AUC-WF. The Spearman's correlation test revealed that BOOIf was significantly correlated with BCI, but not Wmax, PIP1 and WF-AUC.<br><br>CONCLUSIONS: This study demonstrated the non-inferiority of the WF-AUC compared to previously reported criteria for defining DU. Depending on the cutoff value, the WF-AUC could appropriately evaluate women with DU, regardless of the presence of BOO.}, }
@article {pmid38968976, year = {2024}, author = {Kumari, A and Edla, DR and Reddy, RR and Jannu, S and Vidyarthi, A and Alkhayyat, A and de Marin, MSG}, title = {EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.}, journal = {Journal of neuroscience methods}, volume = {409}, number = {}, pages = {110215}, doi = {10.1016/j.jneumeth.2024.110215}, pmid = {38968976}, issn = {1872-678X}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments.}, }
@article {pmid38968936, year = {2024}, author = {Wu, H and Xie, Q and Yu, Z and Zhang, J and Liu, S and Long, J}, title = {Unsupervised heterogeneous domain adaptation for EEG classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5fbd}, pmid = {38968936}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; Unsupervised Machine Learning ; Algorithms ; Neural Networks, Computer ; }, abstract = {Objective.Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification.Approach.In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features.Main results.Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.Significance.This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.}, }
@article {pmid38966123, year = {2024}, author = {Qi, G and Liu, R and Guan, W and Huang, A}, title = {Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {5}, number = {}, pages = {0130}, pmid = {38966123}, issn = {2692-7632}, abstract = {In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.}, }
@article {pmid38966087, year = {2024}, author = {Sun, J and Li, C}, title = {Editorial: Advanced neurotechnology in stroke rehabilitation.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1440752}, pmid = {38966087}, issn = {1664-2295}, }
@article {pmid38965248, year = {2024}, author = {Xu, M and Zhou, W and Shen, X and Qiu, J and Li, D}, title = {Temporal-spatial cross attention network for recognizing imagined characters.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {15432}, pmid = {38965248}, issn = {2045-2322}, support = {2023C01143//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang/ ; 19ZDA348//National Social Science Fund of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Attention/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features of the signals in a sequential, parallel, or single-feature manner. However, there has been limited research on the cross-relationships between temporal and spatial features, despite the inherent association between channels and sampling points in Brain-Computer Interface (BCI) signal acquisition, which holds significant information about brain activity. To address the limited research on the relationships between temporal and spatial features, we proposed a Temporal-Spatial Cross-Attention Network model, named TSCA-Net. The TSCA-Net is comprised of four modules: the Temporal Feature (TF), the Spatial Feature (SF), the Temporal-Spatial Cross (TSCross), and the Classifier. The TF combines LSTM and Transformer to extract temporal features from BCI signals, while the SF captures spatial features. The TSCross is introduced to learn the correlations between the temporal and spatial features. The Classifier predicts the label of BCI data based on its characteristics. We validated the TSCA-Net model using publicly available datasets of handwritten characters, which recorded the spiking activity from two micro-electrode arrays (MEAs). The results showed that our proposed TSCA-Net outperformed other comparison models (EEG-Net, EEG-TCNet, S3T, GRU, LSTM, R-Transformer, and ViT) in terms of accuracy, precision, recall, and F1 score, achieving 92.66 % , 92.77 % , 92.70 % , and 92.58 % , respectively. The TSCA-Net model demonstrated a 3.65 % to 7.49 % improvement in accuracy over the comparison models.}, }
@article {pmid38963812, year = {2024}, author = {Tian, Y and Zheng, J and Zhu, X and Liu, X and Li, H and Wang, J and Yang, Q and Zeng, LH and Shi, Z and Gong, M and Hu, Y and Xu, H}, title = {A prefrontal-habenular circuitry regulates social fear behaviour.}, journal = {Brain : a journal of neurology}, volume = {147}, number = {12}, pages = {4185-4199}, doi = {10.1093/brain/awae209}, pmid = {38963812}, issn = {1460-2156}, support = {32071005//National Natural Science Foundation of China/ ; 2021YFA1101701//National Key Research and Development Program of China/ ; 010904008//Nanhu Brain-Computer Interface Institute/ ; SHSMU-ZDCX20211102//Innovative Research Team of High-level Local Universities in Shanghai/ ; //Fundamental Research Funds for the Central Universities/ ; //MOE Frontiers Science Center for Brain Science/ ; //and/ ; //Brain-Machine Integration of Zhejiang University/ ; }, mesh = {Animals ; *Fear/physiology/psychology ; *Prefrontal Cortex/physiology ; Mice ; Male ; *Habenula/physiology ; Humans ; Mice, Inbred C57BL ; Social Behavior ; Neural Pathways/physiology ; Female ; Optogenetics ; Adult ; Conditioning, Classical/physiology ; }, abstract = {The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments, including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified. Using a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are activated synchronously during social fear expression. Moreover, optogenetic inhibition of the mPFC-LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal-habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear. These results unravel a crucial role of the prefrontal-habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptoms often observed in many psychiatric disorders.}, }
@article {pmid38963559, year = {2024}, author = {Kukkar, KK and Rao, N and Huynh, D and Shah, S and Contreras-Vidal, JL and Parikh, PJ}, title = {Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors.}, journal = {Experimental brain research}, volume = {242}, number = {9}, pages = {2093-2112}, pmid = {38963559}, issn = {1432-1106}, support = {P2C HD086844/HD/NICHD NIH HHS/United States ; R25 HD106896/HD/NICHD NIH HHS/United States ; R25HD106896//National Institute of Child Health and Human Development/ ; P2CHDo86844//National Center for Medical Rehabilitation Research/ ; }, mesh = {Humans ; *Postural Balance/physiology ; Male ; Female ; *Stroke/physiopathology ; Middle Aged ; Aged ; *Electromyography ; *Muscle, Skeletal/physiopathology/physiology ; Chronic Disease ; Survivors ; Electroencephalography/methods ; Adult ; }, abstract = {Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.}, }
@article {pmid38963179, year = {2024}, author = {Martinez-Peon, D and Garcia-Hernandez, NV and Benavides-Bravo, FG and Parra-Vega, V}, title = {Characterization and classification of kinesthetic motor imagery levels.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5f27}, pmid = {38963179}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; Adult ; Female ; *Kinesthesis/physiology ; Young Adult ; *Brain-Computer Interfaces ; Muscle Contraction/physiology ; Motor Cortex/physiology ; Electromyography/methods ; Algorithms ; Movement/physiology ; Reproducibility of Results ; Support Vector Machine ; }, abstract = {Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.}, }
@article {pmid38962279, year = {2024}, author = {Singh, AK and Bianchi, L}, title = {Encoding temporal information in deep convolution neural network.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1287794}, pmid = {38962279}, issn = {2673-6195}, abstract = {A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets-physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.}, }
@article {pmid38962177, year = {2024}, author = {Khazaei, S and Parshi, S and Alam, S and Amin, MR and Faghih, RT}, title = {A multimodal dataset for investigating working memory in presence of music: a pilot study.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1406814}, pmid = {38962177}, issn = {1662-4548}, abstract = {INTRODUCTION: Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.<br><br>METHODS: Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.<br><br>RESULTS: A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.<br><br>DISCUSSION: Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.}, }
@article {pmid38961531, year = {2024}, author = {Sands, I and Demarco, R and Thurber, L and Esteban-Linares, A and Song, D and Meng, E and Chen, Y}, title = {Interface-Mediated Neurogenic Signaling: The Impact of Surface Geometry and Chemistry on Neural Cell Behavior for Regenerative and Brain-Machine Interfacing Applications.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {36}, number = {33}, pages = {e2401750}, pmid = {38961531}, issn = {1521-4095}, support = {1905785//National Science Foundation/ ; 2234570//National Science Foundation/ ; R01 AR072027/AR/NIAMS NIH HHS/United States ; R21 AR079153/AR/NIAMS NIH HHS/United States ; 1R21AR079153-01A1/NH/NIH HHS/United States ; //University of Connecticut/ ; 2025362//National Science Foundation/ ; U24 NS113647/NS/NINDS NIH HHS/United States ; 7R01AR072027/NH/NIH HHS/United States ; 80JSC022CA006/NASA/NASA/United States ; U01 NS126046/NS/NINDS NIH HHS/United States ; W81XWH2110274/NASA/NASA/United States ; }, mesh = {Animals ; Humans ; *Brain-Computer Interfaces ; *Signal Transduction ; *Neurons/cytology/metabolism ; *Surface Properties ; Neurogenesis ; Nanostructures/chemistry ; Tissue Scaffolds/chemistry ; Brain/metabolism/cytology/physiology ; Nerve Regeneration ; Biocompatible Materials/chemistry ; }, abstract = {Nanomaterial advancements have driven progress in central and peripheral nervous system applications such as tissue regeneration and brain-machine interfacing. Ideally, neural interfaces with native tissue shall seamlessly integrate, a process that is often mediated by the interfacial material properties. Surface topography and material chemistry are significant extracellular stimuli that can influence neural cell behavior to facilitate tissue integration and augment therapeutic outcomes. This review characterizes topographical modifications, including micropillars, microchannels, surface roughness, and porosity, implemented on regenerative scaffolding and brain-machine interfaces. Their impact on neural cell response is summarized through neurogenic outcome and mechanistic analysis. The effects of surface chemistry on neural cell signaling with common interfacing compounds like carbon-based nanomaterials, conductive polymers, and biologically inspired matrices are also reviewed. Finally, the impact of these extracellular mediated neural cues on intracellular signaling cascades is discussed to provide perspective on the manipulation of neuron and neuroglia cell microenvironments to drive therapeutic outcomes.}, }
@article {pmid38959876, year = {2024}, author = {Van Den Kerchove, A and Si-Mohammed, H and Van Hulle, MM and Cabestaing, F}, title = {Correcting for ERP latency jitter improves gaze-independent BCI decoding.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5ec0}, pmid = {38959876}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Fixation, Ocular/physiology ; *Attention/physiology ; *Electroencephalography/methods ; Young Adult ; Photic Stimulation/methods ; Reaction Time/physiology ; Evoked Potentials, Visual/physiology ; }, abstract = {Objective.Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze.Approach.ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment.Main results.WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session.Significance. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.}, }
@article {pmid38957693, year = {2024}, author = {Eidel, M and Pfeiffer, M and Ziebell, P and Kübler, A}, title = {Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1371631}, pmid = {38957693}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.}, }
@article {pmid38956454, year = {2024}, author = {Xu, T and Ji, Z and Xu, X and Wang, L}, title = {Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.}, journal = {BMC bioinformatics}, volume = {25}, number = {1}, pages = {227}, pmid = {38956454}, issn = {1471-2105}, mesh = {*Brain-Computer Interfaces ; *Algorithms ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.<br><br>RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.<br><br>CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.}, }
@article {pmid38956006, year = {2024}, author = {Huang, Q and Ding, J and Wang, X}, title = {A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.}, journal = {Neuroscience bulletin}, volume = {40}, number = {12}, pages = {1915-1930}, pmid = {38956006}, issn = {1995-8218}, mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Adult ; Male ; Female ; Young Adult ; Brain/physiopathology ; Signal Processing, Computer-Assisted ; }, abstract = {Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.}, }
@article {pmid38954651, year = {2024}, author = {Köhler, RM and Binns, TS and Merk, T and Zhu, G and Yin, Z and Zhao, B and Chikermane, M and Vanhoecke, J and Busch, JL and Habets, JGV and Faust, K and Schneider, GH and Cavallo, A and Haufe, S and Zhang, J and Kühn, AA and Haynes, JD and Neumann, WJ}, title = {Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.}, journal = {Brain : a journal of neurology}, volume = {147}, number = {10}, pages = {3358-3369}, pmid = {38954651}, issn = {1460-2156}, support = {//Deutsche Forschungsgemeinschaft/ ; }, mesh = {Humans ; *Parkinson Disease/therapy/physiopathology ; *Deep Brain Stimulation/methods ; Female ; Male ; Middle Aged ; Aged ; *Subthalamic Nucleus/physiopathology ; *Dopamine/metabolism ; Volition ; Electrocorticography/methods ; Electromyography ; Movement/physiology ; Sensorimotor Cortex/physiopathology ; }, abstract = {The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.}, }
@article {pmid38953082, year = {2024}, author = {Mulpuri, RP and Konda, N and Gadde, ST and Amalakanti, S and Valiveti, SC}, title = {Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.}, journal = {Cureus}, volume = {16}, number = {5}, pages = {e61400}, pmid = {38953082}, issn = {2168-8184}, abstract = {Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.}, }
@article {pmid38952818, year = {2024}, author = {Vaccari, FE and Diomedi, S and De Vitis, M and Filippini, M and Fattori, P}, title = {Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.}, journal = {Network neuroscience (Cambridge, Mass.)}, volume = {8}, number = {2}, pages = {486-516}, pmid = {38952818}, issn = {2472-1751}, abstract = {Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.}, }
@article {pmid38952644, year = {2024}, author = {Liu, W and Guo, Y and Xie, J and Wu, Y and Zhao, D and Xing, Z and Fu, X and Zhou, S and Zhang, H and Wang, X}, title = {Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1387471}, pmid = {38952644}, issn = {1662-5161}, abstract = {OBJECTIVE: This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors.<br><br>METHODS: This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n&#x2009;=&#x2009;107) and verification set (n&#x2009;=&#x2009;27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model.<br><br>RESULT: The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets.<br><br>CONCLUSION: This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.}, }
@article {pmid38951635, year = {2024}, author = {Song, H and Hsieh, TH and Yeon, SH and Shu, T and Nawrot, M and Landis, CF and Friedman, GN and Israel, EA and Gutierrez-Arango, S and Carty, MJ and Freed, LE and Herr, HM}, title = {Continuous neural control of a bionic limb restores biomimetic gait after amputation.}, journal = {Nature medicine}, volume = {30}, number = {7}, pages = {2010-2019}, pmid = {38951635}, issn = {1546-170X}, support = {R01 HD097135/HD/NICHD NIH HHS/United States ; R25 NS065743/NS/NINDS NIH HHS/United States ; R01HD097135//U.S. Department of Health &amp; Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)/ ; }, mesh = {Humans ; *Artificial Limbs ; *Gait/physiology ; *Amputation, Surgical ; *Amputees ; *Bionics ; *Biomimetics/methods ; Male ; Middle Aged ; Adult ; Female ; Muscle, Skeletal/innervation ; Walking ; Leg/surgery ; }, abstract = {For centuries scientists and technologists have sought artificial leg replacements that fully capture the versatility of their intact biological counterparts. However, biological gait requires coordinated volitional and reflexive motor control by complex afferent and efferent neural interplay, making its neuroprosthetic emulation challenging after limb amputation. Here we hypothesize that continuous neural control of a bionic limb can restore biomimetic gait after below-knee amputation when residual muscle afferents are augmented. To test this hypothesis, we present a neuroprosthetic interface consisting of surgically connected, agonist-antagonist muscles including muscle-sensing electrodes. In a cohort of seven leg amputees, the interface is shown to augment residual muscle afferents by 18% of biologically intact values. Compared with a matched amputee cohort without the afferent augmentation, the maximum neuroprosthetic walking speed is increased by 41%, enabling equivalent peak speeds to persons without leg amputation. Further, this level of afferent augmentation enables biomimetic adaptation to various walking speeds and real-world environments, including slopes, stairs and obstructed pathways. Our results suggest that even a small augmentation of residual muscle afferents restores biomimetic gait under continuous neuromodulation in individuals with leg amputation.}, }
@article {pmid38951525, year = {2024}, author = {Li, F and Gallego, J and Tirko, NN and Greaser, J and Bashe, D and Patel, R and Shaker, E and Van Valkenburg, GE and Alsubhi, AS and Wellman, S and Singh, V and Padilla, CG and Gheres, KW and Broussard, JI and Bagwell, R and Mulvihill, M and Kozai, TDY}, title = {Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {5512}, pmid = {38951525}, issn = {2041-1723}, support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R01NS129632//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R21 EB028055/EB/NIBIB NIH HHS/United States ; R01NS115707//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; CAREER 1943906//NSF | ENG/OAD | Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET)/ ; R01 NS094396/NS/NINDS NIH HHS/United States ; R21EB028055//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R44 MH131514/MH/NIMH NIH HHS/United States ; R44MH131514//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01NS094396//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS105691//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS115707/NS/NINDS NIH HHS/United States ; }, mesh = {*Microglia/radiation effects/metabolism ; Animals ; *Microelectrodes ; *Electrodes, Implanted ; *Ultrasonic Waves ; Male ; Foreign-Body Reaction/prevention &amp; control/etiology ; Mice ; Cerebral Cortex/radiation effects/cytology ; Brain-Computer Interfaces ; Cell Movement/radiation effects ; Rats ; }, abstract = {Microglia are important players in surveillance and repair of the brain. Implanting an electrode into the cortex activates microglia, produces an inflammatory cascade, triggers the foreign body response, and opens the blood-brain barrier. These changes can impede intracortical brain-computer interfaces performance. Using two-photon imaging of implanted microelectrodes, we test the hypothesis that low-intensity pulsed ultrasound stimulation can reduce microglia-mediated neuroinflammation following the implantation of microelectrodes. In the first week of treatment, we found that low-intensity pulsed ultrasound stimulation increased microglia migration speed by 128%, enhanced microglia expansion area by 109%, and a reduction in microglial activation by 17%, indicating improved tissue healing and surveillance. Microglial coverage of the microelectrode was reduced by 50% and astrocytic scarring by 36% resulting in an increase in recording performance at chronic time. The data indicate that low-intensity pulsed ultrasound stimulation helps reduce the foreign body response around chronic intracortical microelectrodes.}, }
@article {pmid38949928, year = {2024}, author = {Wu, X and Metcalfe, B and He, S and Tan, H and Zhang, D}, title = {A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2408-2431}, doi = {10.1109/TNSRE.2024.3421551}, pmid = {38949928}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electrocorticography/instrumentation/methods ; *Movement/physiology ; *Electrodes, Implanted ; Deep Brain Stimulation/instrumentation ; Biomechanical Phenomena ; Electroencephalography/methods/instrumentation ; Electrodes ; Motor Cortex/physiology ; Hand/physiology ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.}, }
@article {pmid38949927, year = {2024}, author = {Flores, C and Contreras, M and Macedo, I and Andreu-Perez, J}, title = {Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3794-3803}, doi = {10.1109/TNSRE.2024.3420960}, pmid = {38949927}, issn = {1558-0210}, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Deep Learning ; *Electroencephalography ; *Event-Related Potentials, P300/physiology ; *Machine Learning ; *Neural Networks, Computer ; }, abstract = {Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-world diversity. Deep learning models aim to enhance BCI classification accuracy, and transfer learning has been suggested to adapt models to individual neural patterns using a base model trained on others' data. This approach promises better generalizability and reduced overfitting, yet challenges remain in handling diverse and imbalanced datasets from different equipment, subjects, multiple centres in different countries, and both healthy and patient populations for effective model transfer and tuning. In a setting characterized by maximal heterogeneity, we proposed P300 wave detection in BCIs employing a convolutional neural network fitted with adaptive transfer learning based on Poison Sampling Disk (PDS) called Active Sampling (AS), which flexibly adjusts the transition from source data to the target domain. Our results reported for subject adaptive with 40% of adaptive fine-tuning that the averaged classification accuracy improved by 5.36% and standard deviation reduced by 12.22% using two distinct, internationally replicated datasets. These results outperformed in classification accuracy, computational time, and training efficiency, mainly due to the proposed Active Sampling (AS) method for transfer learning.}, }
@article {pmid38946233, year = {2024}, author = {Lü, C and Wang, T and Xi, X and Wang, M and Wang, J and Zhilenko, A and Li, L}, title = {A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2024.2371036}, pmid = {38946233}, issn = {1476-8259}, abstract = {Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.}, }
@article {pmid38945115, year = {2024}, author = {Wang, X and Yang, W and Qi, W and Wang, Y and Ma, X and Wang, W}, title = {STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {178}, number = {}, pages = {106471}, doi = {10.1016/j.neunet.2024.106471}, pmid = {38945115}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Electroencephalography/methods ; Brain/physiology ; Movement/physiology ; }, abstract = {Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.}, }
@article {pmid38944964, year = {2024}, author = {Wu, X and Gao, R and Tian, X and Hou, J and Wang, Y and Wang, Q and Tang, DKH and Yao, Y and Zhang, X and Wang, B and Yang, G and Li, H and Li, R}, title = {Co-composting of dewatered sludge and wheat straw with newly isolated Xenophilus azovorans: Carbon dynamics, humification, and driving pathways.}, journal = {Journal of environmental management}, volume = {365}, number = {}, pages = {121613}, doi = {10.1016/j.jenvman.2024.121613}, pmid = {38944964}, issn = {1095-8630}, mesh = {*Triticum ; *Sewage/microbiology ; *Composting ; *Carbon/metabolism ; Humic Substances ; Charcoal ; }, abstract = {Composting is a biological reaction caused by microorganisms. Composting efficiency can be adequately increased by adding biochar and/or by inoculating with exogenous microorganisms. In this study, we looked at four methods for dewatered sludge waste (DSW) and wheat straw (WS) aerobic co-composting: T1 (no additive), T2 (5% biochar), T3 (5% of a newly isolated strain, Xenophilus azovorans (XPA)), and T4 (5% of biochar-immobilized XPA (BCI-XPA)). Throughout the course of the 42-day composting period, we looked into the carbon dynamics, humification, microbial community succession, and modifications to the driving pathways. Compared to T1 and T2, the addition of XPA (T3) and BCI-XPA (T4) extended the thermophilic phase of composting without negatively affecting compost maturation. Notably, T4 exhibited a higher seed germination index (132.14%). Different from T1 and T2 treatments, T3 and T4 treatments increased CO2 and CH4 emissions in the composting process, in which the cumulative CO2 emissions increased by 18.61-47.16%, and T3 and T4 treatments also promoted the formation of humic acid. Moreover, T4 treatment with BCI-XPA addition showed relatively higher activities of urease, polyphenol oxidase, and laccase, as well as a higher diversity of microorganisms compared to other processes. The Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis showed that microorganisms involved in the carbon cycle dominated the entire composting process in all treatments, with chemoheterotrophy and aerobic chemoheterotrophy being the main pathways of organic materials degradation. Moreover, the presence of XPA accelerated the breakdown of organic materials by catabolism of aromatic compounds and intracellular parasite pathways. On the other hand, the xylanolysis pathway was aided in the conversion of organic materials to dissolved organics by the addition of BCI-XPA. These findings indicate that XPA and BCI-XPA have potential as additives to improve the efficiency of dewatered sludge and wheat straw co-composting.}, }
@article {pmid38943984, year = {2024}, author = {Kim, JS and Kim, H and Chung, CK and Kim, JS}, title = {Dual model transfer learning to compensate for individual variability in brain-computer interface.}, journal = {Computer methods and programs in biomedicine}, volume = {254}, number = {}, pages = {108294}, doi = {10.1016/j.cmpb.2024.108294}, pmid = {38943984}, issn = {1872-7565}, mesh = {*Brain-Computer Interfaces ; Humans ; Neural Networks, Computer ; Electroencephalography ; Movement/physiology ; Algorithms ; Brain/physiology ; Machine Learning ; Male ; Adult ; }, abstract = {Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.}, }
@article {pmid38943861, year = {2024}, author = {Sun, H and Ding, Y and Bao, J and Qin, K and Tong, C and Jin, J and Guan, C}, title = {Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {178}, number = {}, pages = {106470}, doi = {10.1016/j.neunet.2024.106470}, pmid = {38943861}, issn = {1879-2782}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Algorithms ; Imagination/physiology ; Deep Learning ; Machine Learning ; Adult ; Male ; Time Factors ; }, abstract = {Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.}, }
@article {pmid38941986, year = {2024}, author = {Ahsan Awais, M and Ward, T and Redmond, P and Healy, G}, title = {From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5d17}, pmid = {38941986}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; *Photic Stimulation/methods ; *Artifacts ; Visual Perception/physiology ; Machine Learning ; Movement/physiology ; }, abstract = {Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.}, }
@article {pmid38941792, year = {2024}, author = {Huang, Z and Sun, Y and Liu, S and Chen, X and Ping, J and Fei, P and Gong, Z and Zheng, N}, title = {A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.}, journal = {Biochemical and biophysical research communications}, volume = {727}, number = {}, pages = {150290}, doi = {10.1016/j.bbrc.2024.150290}, pmid = {38941792}, issn = {1090-2104}, mesh = {Animals ; *Larva/physiology ; *Machine Learning ; *Neurons/physiology ; *Posture/physiology ; Microscopy, Fluorescence/methods ; Drosophila melanogaster/physiology ; Drosophila/physiology ; Movement/physiology ; Behavior, Animal/physiology ; }, abstract = {To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidic chip to simultaneously capture neural activity and body movement in small freely behaving Drosophila larva. We develop a transfer learning based method to simultaneously track the continuously changing body posture and activity of neurons that move together using a sub-region tracking network with a precise landmark estimation network for the inference of target landmark trajectory. Based on the tracking of each labelled neuron, the activity of the neuron indicated by fluorescent intensity is calculated. For each video, annotation of only 20 frames in a video is sufficient to yield human-level accuracy for all other frames. The validity of this method is further confirmed by reproducing the activity pattern of PMSIs (period-positive median segmental interneurons) and larval movement as previously reported. Using this method, we disclosed the correlation between larval movement and left-right asymmetry in activity of a group of unidentified neurons labelled by R52H01-Gal4 and further confirmed the roles of these neurons in bilateral balance of body contraction during larval crawling by genetic inhibition of these neurons. Our method provides a new tool for accurate extraction of neural activities and movement of freely behaving small-size transparent animals.}, }
@article {pmid38941048, year = {2024}, author = {Brewe, AM and Antezana, L and Carlton, CN and Gracanin, D and Richey, JA and Kim, I and White, SW}, title = {A Randomized Trial Utilizing EEG Brain Computer Interface to Improve Facial Emotion Recognition in Autistic Adults.}, journal = {Journal of autism and developmental disorders}, volume = {}, number = {}, pages = {}, pmid = {38941048}, issn = {1573-3432}, support = {R33MH100268-03/MH/NIMH NIH HHS/United States ; }, abstract = {PURPOSE: Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults.<br><br>METHODS: Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n&#x2009;=&#x2009;17) or waitlist control (n&#x2009;=&#x2009;10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint.<br><br>RESULTS: Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline.<br><br>CONCLUSION: Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.}, }
@article {pmid38938001, year = {2024}, author = {Zhang, Z and Huang, Y and Chen, X and Li, J and Yang, Y and Lv, L and Wang, J and Wang, M and Wang, Y and Wang, Z}, title = {State-specific Regulation of Electrical Stimulation in the Intralaminar Thalamus of Macaque Monkeys: Network and Transcriptional Insights into Arousal.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {33}, pages = {e2402718}, pmid = {38938001}, issn = {2198-3844}, support = {2021ZD0204000//STI-2030-Major Projects/ ; 2022ZD0205100//STI-2030-Major Projects/ ; 2021ZD0203900//STI-2030-Major Projects/ ; 2019B030335001//Key-Area Research and Development Program of Guangdong Province/ ; 82151303//National Natural Science Foundation of China/ ; 82271292//National Natural Science Foundation of China/ ; 82301445//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *Arousal/genetics/physiology ; Macaca ; Magnetic Resonance Imaging/methods ; Electroencephalography/methods ; Electric Stimulation/methods ; Deep Brain Stimulation/methods ; Male ; Thalamus/metabolism ; }, abstract = {Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging (EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.}, }
@article {pmid38937562, year = {2024}, author = {Morozova, M and Nasibullina, A and Yakovlev, L and Syrov, N and Kaplan, A and Lebedev, M}, title = {Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {14862}, pmid = {38937562}, issn = {2045-2322}, support = {21-75-30024//Russian Foundation/ ; }, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; *Evoked Potentials, Motor/physiology ; Adult ; *Imagination/physiology ; Young Adult ; *Touch/physiology ; Pyramidal Tracts/physiology ; Fingers/physiology ; Motor Cortex/physiology ; Vibration ; Brain-Computer Interfaces ; }, abstract = {Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain-computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.}, }
@article {pmid38936392, year = {2024}, author = {Savalle, E and Pillette, L and Won, K and Argelaguet, F and Lécuyer, A and J-M Macé, M}, title = {Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5cc2}, pmid = {38936392}, issn = {1741-2552}, mesh = {Humans ; *Virtual Reality ; Male ; Female ; *Electroencephalography/methods ; Adult ; Young Adult ; *Acoustic Stimulation/methods ; Event-Related Potentials, P300/physiology ; Algorithms ; Attention/physiology ; Evoked Potentials, Auditory/physiology ; }, abstract = {Objective.Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR.Approach.In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE.Main results.Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions.Significance.Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE.}, }
@article {pmid38934637, year = {2025}, author = {Tankus, A and Stern, E and Klein, G and Kaptzon, N and Nash, L and Marziano, T and Shamia, O and Gurevitch, G and Bergman, L and Goldstein, L and Fahoum, F and Strauss, I}, title = {A Speech Neuroprosthesis in the Frontal Lobe and Hippocampus: Decoding High-Frequency Activity into Phonemes.}, journal = {Neurosurgery}, volume = {96}, number = {2}, pages = {356-364}, doi = {10.1227/neu.0000000000003068}, pmid = {38934637}, issn = {1524-4040}, support = {17630//Ministry of Science and Technology, Israel/ ; }, mesh = {Humans ; Male ; Adult ; *Hippocampus/physiology ; *Speech/physiology ; *Frontal Lobe/physiology ; Electrodes, Implanted ; *Phonetics ; *Brain-Computer Interfaces ; *Neural Prostheses ; }, abstract = {BACKGROUND AND OBJECTIVES: Loss of speech due to injury or disease is devastating. Here, we report a novel speech neuroprosthesis that artificially articulates building blocks of speech based on high-frequency activity in brain areas never harnessed for a neuroprosthesis before: anterior cingulate and orbitofrontal cortices, and hippocampus.<br><br>METHODS: A 37-year-old male neurosurgical epilepsy patient with intact speech, implanted with depth electrodes for clinical reasons only, silently controlled the neuroprosthesis almost immediately and in a natural way to voluntarily produce 2 vowel sounds.<br><br>RESULTS: During the first set of trials, the participant made the neuroprosthesis produce the different vowel sounds artificially with 85% accuracy. In the following trials, performance improved consistently, which may be attributed to neuroplasticity. We show that a neuroprosthesis trained on overt speech data may be controlled silently.<br><br>CONCLUSION: This may open the way for a novel strategy of neuroprosthesis implantation at earlier disease stages (eg, amyotrophic lateral sclerosis), while speech is intact, for improved training that still allows silent control at later stages. The results demonstrate clinical feasibility of direct decoding of high-frequency activity that includes spiking activity in the aforementioned areas for silent production of phonemes that may serve as a part of a neuroprosthesis for replacing lost speech control pathways.}, }
@article {pmid38933146, year = {2024}, author = {Ren, G and Kumar, A and Mahmoud, SS and Fang, Q}, title = {A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1394107}, pmid = {38933146}, issn = {1662-5161}, abstract = {BACKGROUND: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets.<br><br>METHODS: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases.<br><br>RESULTS: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively.<br><br>CONCLUSIONS: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.}, }
@article {pmid38931814, year = {2024}, author = {Jochumsen, M and Lavesen, ER and Griem, AB and Falkenberg-Andersen, C and Jensen, SKG}, title = {The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {12}, pages = {}, pmid = {38931814}, issn = {1424-8220}, mesh = {Humans ; *Caffeine/pharmacology ; Male ; *Electroencephalography/methods ; Female ; *Movement/drug effects/physiology ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Coffee/chemistry ; }, abstract = {Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.}, }
@article {pmid38931769, year = {2024}, author = {Papadopoulou, A and Hermiz, J and Grace, C and Denes, P}, title = {A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {12}, pages = {}, pmid = {38931769}, issn = {1424-8220}, support = {F1NS107667//National Institute Of Neurological Disorders And Stroke of the National Institutes of Health/ ; }, mesh = {Animals ; *Signal Processing, Computer-Assisted ; Electrophysiology/methods/instrumentation ; Neurons/physiology ; Electrophysiological Phenomena ; Electrodes ; Equipment Design ; }, abstract = {The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.}, }
@article {pmid38931751, year = {2024}, author = {Mwata-Velu, T and Zamora, E and Vasquez-Gomez, JI and Ruiz-Pinales, J and Sossa, H}, title = {Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {12}, pages = {}, pmid = {38931751}, issn = {1424-8220}, support = {Folio SIP/1988/DI/DAI/2022, 20220002, 20230232, 20240108, 20231622, 202409//Instituto Polit&#xe9;cnico Nacional/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).}, }
@article {pmid38931540, year = {2024}, author = {Huang, W and Liu, X and Yang, W and Li, Y and Sun, Q and Kong, X}, title = {Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {12}, pages = {}, pmid = {38931540}, issn = {1424-8220}, support = {No.2021YFF1200700, No.2022YFF1202400, No.2023YFF1204200, No.2023YFF1203900//The National Key Research and Development Program of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology/diagnostic imaging ; Imagination/physiology ; }, abstract = {A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.}, }
@article {pmid38929887, year = {2024}, author = {Krüger, L and Kamp, O and Alfen, K and Theysohn, J and Dudda, M and Becker, L}, title = {Pediatric Carotid Injury after Blunt Trauma and the Necessity of CT and CTA-A Narrative Literature Review.}, journal = {Journal of clinical medicine}, volume = {13}, number = {12}, pages = {}, pmid = {38929887}, issn = {2077-0383}, abstract = {Background: Blunt carotid injury (BCI) in pediatric trauma is quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/blunt cerebrovascular injury (BCVI) on pediatric patients after blunt trauma is, what routine diagnostics looks like, if a computed tomography (CT)/computed tomography angiography (CTA) scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects. Methods: This narrative literature review includes reviews, systematic reviews, case reports and original studies in the English language between 1999 and 2020 that deal with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure for children were included in this study. For literature research, Medline (PubMed) and the Cochrane library were used. Results: Pediatric BCI/BCVI shows an overall incidence between 0.03 and 0.5% of confirmed BCI/BCVI cases due to pediatric blunt trauma. In total, 1.1-3.5% of pediatric blunt trauma patients underwent CTA to detect BCI/BCVI. Only 0.17-1.2% of all CTA scans show a positive diagnosis for BCI/BCVI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligrays (mGy), whereas a computed tomography angiography needs at least 138 mGy. A cumulative dose of about 50 mGy almost triples the risk of leukemia, and a cumulative dose of about 60 mGy triples the risk of brain cancer. Conclusions: Given that a BCI/BCVI could have extensive neurological consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. CT and CTA are mostly used in routine BCI/BCVI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are established BCI/BCVI screening options, as well as duplex ultrasound.}, }
@article {pmid38927850, year = {2024}, author = {Li, X and Yang, S and Fei, N and Wang, J and Huang, W and Hu, Y}, title = {A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {6}, pages = {}, pmid = {38927850}, issn = {2306-5354}, support = {GJHZ20220913143408015//Shenzhen Science and Technology Program/ ; 2022A703-3//Zhanjiang Competitive Allocation of Special Funds for Scientific and Technological Development/ ; JCYJ20230807113007015//Shenzhen Science and Technology Program/ ; SZSM202211004//Sanming Project of Medicine in Shenzhen/ ; SZXK2020084//Shenzhen Key Medical Discipline Construction Fund/ ; B2024036//Health Commission of Guangdong Province/ ; }, abstract = {The application of wearable electroencephalogram (EEG) devices is growing in brain-computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum-convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.}, }
@article {pmid38925110, year = {2024}, author = {Wyse-Sookoo, K and Luo, S and Candrea, D and Schippers, A and Tippett, DC and Wester, B and Fifer, M and Vansteensel, MJ and Ramsey, NF and Crone, NE}, title = {Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {38925110}, issn = {1741-2552}, support = {T32 EB003383/EB/NIBIB NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Longitudinal Studies ; *Electrocorticography/methods ; *Speech/physiology ; Male ; Gamma Rhythm/physiology ; Middle Aged ; Female ; Electrodes, Implanted ; }, abstract = {Objective.Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period.Approach.ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode.Main Results.We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation.Significance.Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis.Clinical Trial Information.ClinicalTrials.gov, registration number NCT03567213.}, }
@article {pmid38925086, year = {2024}, author = {Augenstein, TE and Nagalla, D and Mohacey, A and Cubillos, LH and Lee, MH and Ranganathan, R and Krishnan, C}, title = {A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.}, journal = {Computers in biology and medicine}, volume = {178}, number = {}, pages = {108778}, doi = {10.1016/j.compbiomed.2024.108778}, pmid = {38925086}, issn = {1879-0534}, mesh = {Humans ; *Robotics ; Adult ; Male ; *Self-Help Devices ; Female ; User-Computer Interface ; Brain-Computer Interfaces ; }, abstract = {Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user's residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.}, }
@article {pmid38923489, year = {2024}, author = {Huang, J and Lv, Y and Zhang, ZQ and Xiong, B and Wang, Q and Wan, B and Yang, P}, title = {Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2376-2387}, doi = {10.1109/TNSRE.2024.3419013}, pmid = {38923489}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Electroencephalography/methods ; Calibration ; Male ; Adult ; Female ; Young Adult ; Reproducibility of Results ; Photic Stimulation/methods ; Healthy Volunteers ; }, abstract = {Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.}, }
@article {pmid38923184, year = {2024}, author = {Iwama, S and Tsuchimoto, S and Mizuguchi, N and Ushiba, J}, title = {EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.}, journal = {Human brain mapping}, volume = {45}, number = {9}, pages = {e26767}, pmid = {38923184}, issn = {1097-0193}, support = {JPMJPR23I1//Japan Science and Technology Agency/ ; 20H05923//Japan Society for the Promotion of Science/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Magnetic Resonance Imaging ; Adult ; *Brain-Computer Interfaces ; Male ; *Neurofeedback/methods ; Young Adult ; *Sensorimotor Cortex/physiology/diagnostic imaging ; Female ; *Neural Networks, Computer ; }, abstract = {Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain-computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI-based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.}, }
@article {pmid38922986, year = {2024}, author = {Chen, J and Zeng, H and Cheng, Y and Yang, B}, title = {Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.}, journal = {Medical physics}, volume = {51}, number = {10}, pages = {7269-7281}, doi = {10.1002/mp.17266}, pmid = {38922986}, issn = {2473-4209}, mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/genetics ; *Neural Networks, Computer ; *Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted/methods ; Contrast Media ; Female ; Receptor, ErbB-2/metabolism/genetics ; }, abstract = {BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non-invasive system for identifying HER2 and HR in breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).<br><br>METHODS: In light of the absence of high-performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY 1) and I-SPY 2, were incorporated. I-SPY 2 was utilized for model training and validation, while I-SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction.<br><br>RESULTS: The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I-SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I-SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction.<br><br>CONCLUSION: This study proposes a non-invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre-trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic.}, }
@article {pmid38920504, year = {2024}, author = {Bardella, G and Franchini, S and Pan, L and Balzan, R and Ramawat, S and Brunamonti, E and Pani, P and Ferraina, S}, title = {Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.}, journal = {Entropy (Basel, Switzerland)}, volume = {26}, number = {6}, pages = {}, pmid = {38920504}, issn = {1099-4300}, support = {PH11715C823A9528//Sapienza University of Rome/ ; RM12117A8AD27DB1//Sapienza University of Rome/ ; EBRAINS-Italy PNRR 2023//EBRAINS-Italy PNRR 2023/ ; }, abstract = {Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.}, }
@article {pmid38920469, year = {2024}, author = {Neven, H and Zalcman, A and Read, P and Kosik, KS and van der Molen, T and Bouwmeester, D and Bodnia, E and Turin, L and Koch, C}, title = {Testing the Conjecture That Quantum Processes Create Conscious Experience.}, journal = {Entropy (Basel, Switzerland)}, volume = {26}, number = {6}, pages = {}, pmid = {38920469}, issn = {1099-4300}, support = {//Tiny Blue Dot Foundation/ ; }, abstract = {The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest-pain generates intense feelings and absorbs all our conscious awareness, yet nothing particularly intelligent is going on. In the hard sciences, this topic is frequently met with skepticism because, to date, no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here, we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human conscious experience through brain-quantum computer interfaces.}, }
@article {pmid38920119, year = {2024}, author = {Samal, P and Hashmi, MF}, title = {An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-24}, doi = {10.1080/10255842.2024.2369257}, pmid = {38920119}, issn = {1476-8259}, abstract = {Emotion recognition using EEG is a difficult study because the signals' unstable behavior, which is brought on by the brain's complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model's utility in emotional computing.}, }
@article {pmid38919909, year = {2024}, author = {Liu, Y and Liu, R and Ge, J and Wang, Y}, title = {Advancements in brain-machine interfaces for application in the metaverse.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1383319}, pmid = {38919909}, issn = {1662-4548}, abstract = {In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future.}, }
@article {pmid38919152, year = {2024}, author = {Salmanipour, S and Rezaie, A and Alipour, N and Ghahremani-Nasab, M and Zakerhamidi, MS and Akbari-Gharalari, N and Mehdipour, A and Salehi, R and Jarolmasjed, S}, title = {Development of Polyphosphate/Nanokaolin-Modified Alginate Sponge by Gas-Foaming and Plasma Glow Discharge Methods for Ultrarapid Hemostasis in Noncompressible Bleeding.}, journal = {ACS applied materials &amp; interfaces}, volume = {16}, number = {27}, pages = {34684-34704}, doi = {10.1021/acsami.4c05695}, pmid = {38919152}, issn = {1944-8252}, mesh = {Polyphosphates/chemistry ; Kaolin/chemistry ; *Metal Nanoparticles/chemistry ; Alginates/chemistry ; Time Factors ; Humans ; Hemostasis ; *Hemorrhage/therapy ; Porosity ; Cell Survival ; Cell Line ; Male ; Animals ; Rats ; }, abstract = {Effective bleeding management strategies in uncontrollable and noncompressible massive hemorrhage are becoming important in both clinical and combat situations. Here, a novel approach was developed to create a superporous and highly absorbable hemostatic sponge through a facile chemical gas-foaming method by cross-linking long-chain polyphosphate along with nanokaolin and Ca[2+] in an alginate structure to synergistically activate the coagulation pathway. Natural kaolin obtained from the Marand mine in East Azarbaijan was converted into pseudohexagonal-shaped kaolin nanoparticles (30 to 150 nm) using ball milling followed by a newly developed glow discharge plasma treatment method. The obtained ultralight sponges (>90% porosity) exhibit ultrarapid water/blood absorption capacity (∼4000%) and excellent shape memory, which effectively concentrates coagulation factors. The results of in vitro tests demonstrated that the proposed sponges exhibited enhanced blood clotting ability (BCI < 10%) and superior cohesion with red blood cells (∼100) and platelets (∼80%) compared to commercially available hemostatic products. The in vivo host response results exhibited biosafety with no systemic and significant local inflammatory response by hematological, pathological, and biochemical parameter assessments. In a rat femoral artery complete excision model, the application of alginate/k/polyp nanocomposite sponges resulted in a complete hemostasis time of 60 s by significant reduction of hemostasis time (∼6.7-8.3 fold) and blood loss (∼2-2.8-fold) compared to commercially available hemostatic agents (P < 0.001). In conclusion, distinct physical characteristics accompanied by unique chemical composition multifunctional sponges activate hemostasis synergistically by triggering the XII, XI, X, IX, V, and II factors and the contact pathway and have the ability of rapid hemostasis in noncompressible severe bleeding.}, }
@article {pmid38915599, year = {2024}, author = {Jensen, MA and Schalk, G and Ince, N and Hermes, D and Brunner, P and Miller, KJ}, title = {Feasibility of Stereo EEG Based Brain Computer Interfacing in An Adult and Pediatric Cohort.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.06.12.598257}, pmid = {38915599}, issn = {2692-8205}, abstract = {INTRODUCTION: Stereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring method which records from the brain volumetrically with depth electrodes. Implementation of sEEG in BCI has not been well-described across a diverse patient cohort.<br><br>METHODS: Across eighteen subjects, channels with high frequency broadband (HFB, 65-115Hz) power increases during hand, tongue, or foot movements during a motor screening task were provided real-time feedback based on these HFB power changes to control a cursor on a screen.<br><br>RESULTS: Seventeen subjects established successful control of the overt motor BCI, but only nine were able to control imagery BCI with &#x2265; 80% accuracy. In successful imagery BCI, HFB power in the two target conditions separated into distinct subpopulations, which appear to engage unique subnetworks of the motor cortex compared to cued movement or imagery alone.<br><br>CONCLUSION: sEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across patient ages and cortical regions with substantial differences in learning proficiency between real or imagined movement.}, }
@article {pmid38915308, year = {2024}, author = {Jin, Y and Li, J and Fan, Z and Hua, X and Wang, T and Du, S and Xi, X and Li, L}, title = {Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1404816}, pmid = {38915308}, issn = {1662-4548}, abstract = {OBJECTIVE: Nowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.<br><br>APPROACH: We proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT&#x2009;+&#x2009;FC) features.<br><br>RESULTS: The classification accuracy of our proposed methods was up to 99.75%.<br><br>SIGNIFICANCE: The proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces.}, }
@article {pmid38914073, year = {2024}, author = {Martin Del Campo Vera, R and Sundaram, S and Lee, R and Lee, Y and Leonor, A and Chung, RS and Shao, A and Cavaleri, J and Gilbert, ZD and Zhang, S and Kammen, A and Mason, X and Heck, C and Liu, CY and Kellis, S and Lee, B}, title = {Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {38914073}, issn = {1741-2552}, support = {K23 NS114190/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Hippocampus/physiology ; Female ; *Beta Rhythm/physiology ; Male ; Adult ; *Arm/physiology ; Psychomotor Performance/physiology ; Movement/physiology ; Electroencephalography/methods/classification ; Principal Component Analysis ; Young Adult ; Reproducibility of Results ; Middle Aged ; }, abstract = {Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.}, }
@article {pmid38913534, year = {2024}, author = {Li, T and Jiang, Y and Fu, X and Sun, Z and Yan, Y and Li, YF and Liu, S}, title = {Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {10}, pages = {3014-3023}, doi = {10.1109/TBME.2024.3406940}, pmid = {38913534}, issn = {1558-2531}, mesh = {*Brain-Computer Interfaces ; *Electrodes, Implanted ; Animals ; *Robotics/instrumentation ; Mice ; Equipment Design ; Electroencephalography/instrumentation/methods ; Nanotechnology/instrumentation ; Brain/physiology ; }, abstract = {Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Conventional rigid electrodes face the problem of foreign body reaction induced by mechanical mismatch to biological tissue, while flexible electrodes, though more preferential, lack controllability during implantation. Researchers have explored various strategies, from assistive shuttle to biodegradable coatings, to strike a balance between implantation rigidity and post-implantation flexibility. Yet, these approaches may introduce complications, including immune response, inflammations, and raising intracranial pressure. To this end, this paper proposes a novel nanorobot-based technique for direct implantation of flexible neural electrodes, leveraging the high controllability and repeatability of robotics to enhance the implantation quality. This approach features a dual-arm nanorobotic system equipped with stereo microscope, by which a flexible electrode is first visually aligned to the target neural tissue to establish contact and thereafter implanted into brain with well controlled insertion direction and depth. The key innovation is, through dual-arm coordination, the flexible electrode maintains straight along the implantation direction. With this approach, we implanted CNTf electrodes into cerebral cortex of mouse, and captured standard spiking neural signals.}, }
@article {pmid38913514, year = {2024}, author = {Gong, L and Chen, W and Zhang, D}, title = {An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {10}, pages = {5890-5903}, doi = {10.1109/JBHI.2024.3418010}, pmid = {38913514}, issn = {2168-2208}, mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Neural Networks, Computer ; Algorithms ; Brain-Computer Interfaces ; }, abstract = {Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46 %, 92.15/5.13 %, 97.60/1.68 %(valence), and 97.48/1.42 %(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.}, }
@article {pmid38912322, year = {2024}, author = {Wen, H and Zhong, Y and Yao, L and Wang, Y}, title = {Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm: A High-Density EEG Source Imaging Study.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {5}, number = {}, pages = {0118}, pmid = {38912322}, issn = {2692-7632}, abstract = {Complementary to brain-computer interface (BCI) based on motor imagery (MI) task, sensory imagery (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior recording. In this study, we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram (EEG) recording, and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the tasks. In the experiment, participants were instructed to perform the left and right hand tasks in MI paradigm, sensory stimulation (SS) paradigm and SI paradigm. The statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts, frontal and right temporal areas in α bands, whereas real SS and imagined SI showed a similar activation pattern. The similarity between SS and SI may provide a way to train the BCI system, while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI performance. The combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider application. BCI studies concentrate on the hybrid decoding method combining MI or SI with SS, but the underlining neurophysiological correlates between them were unclear. MI and SI differed from each other within the ipsilateral sensorimotor cortex in alpha bands. This is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.}, }
@article {pmid38909175, year = {2024}, author = {Dai, C and Lin, X and Xue, B and Xi, X and Gao, M and Liu, X and Han, T and Li, Q and Yuan, H and Sun, X}, title = {Correlation of bilateral M1 hand area excitability and overall functional recovery after spinal cord injury: protocol for a prospective cohort study.}, journal = {BMC neurology}, volume = {24}, number = {1}, pages = {213}, pmid = {38909175}, issn = {1471-2377}, support = {82072534//National Natural Science Foundation of China/ ; 82272591//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Recovery of Function/physiology ; *Hand/physiopathology ; *Transcranial Magnetic Stimulation/methods ; *Motor Cortex/physiopathology ; Prospective Studies ; Evoked Potentials, Motor/physiology ; Male ; Adult ; Female ; Cohort Studies ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; }, abstract = {BACKGROUND: After spinal cord injury (SCI), a large number of survivors suffer from severe motor dysfunction (MD). Although the injury site is in the spinal cord, excitability significantly decreases in the primary motor cortex (M1), especially in the lower extremity (LE) area. Unfortunately, M1 LE area-targeted repetitive transcranial magnetic stimulation (rTMS) has not achieved significant motor improvement in individuals with SCI. A recent study reported that the M1 hand area in individuals with SCl contains a compositional code (the movement-coding component of neural activity) that links matching movements from the upper extremities (UE) and the LE. However, the correlation between bilateral M1 hand area excitability and overall functional recovery is unknown.<br><br>OBJECTIVE: To clarify the changes in the excitability of the bilateral M1 hand area after SCI and its correlation with motor recovery, we aim to specify the therapeutic parameters of rTMS for SCI motor rehabilitation.<br><br>METHODS: This study is a 12-month prospective cohort study. The neurophysiological and overall functional status of the participants will be assessed. The primary outcomes included single-pulse and paired-pulse TMS. The second outcome included functional near-infrared spectroscopy (fNIRS) measurements. Overall functional status included total motor score, modified Ashworth scale score, ASIA Impairment Scale grade, spinal cord independence measure and modified Barthel index. The data will be recorded for individuals with SCI at disease durations of 1 month, 2 months, 4 months, 6 months and 12 months. The matched healthy controls will be measured during the same period of time after recruitment.<br><br>DISCUSSION: The present study is the first to analyze the role of bilateral M1 hand area excitability changes in the evaluation and prediction of overall functional recovery (including motor function and activities of daily living) after SCI, which will further expand the traditional theory of the predominant role of M1, optimize the current rTMS treatment, and explore the brain-computer interface design for individuals with SCI.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR2300068831.}, }
@article {pmid38909069, year = {2024}, author = {Ngo, TD and Kieu, HD and Nguyen, MH and Nguyen, TH and Can, VM and Nguyen, BH and Le, TH}, title = {An EEG &amp; eye-tracking dataset of ALS patients &amp; healthy people during eye-tracking-based spelling system usage.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {664}, pmid = {38909069}, issn = {2052-4463}, mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Eye-Tracking Technology ; }, abstract = {This research presents a dataset consisting of electroencephalogram and eye tracking recordings obtained from six patients with amyotrophic lateral sclerosis (ALS) in a locked-in state and one hundred seventy healthy individuals. The ALS patients exhibited varying degrees of disease progression, ranging from partial mobility and weakened speech to complete paralysis and loss of speech. Despite these physical impairments, the ALS patients retained good eye function, which allowed them to use a virtual keyboard for communication. Data from ALS patients was recorded multiple times at their homes, while data from healthy individuals was recorded once in a laboratory setting. For each data recording, the experimental design involved nine recording sessions per participant, each corresponding to a common human action or demand. This dataset can serve as a valuable benchmark for several applications, such as improving spelling systems with brain-computer interfaces, investigating motor imagination, exploring motor cortex function, monitoring motor impairment progress in patients undergoing rehabilitation, and studying the effects of ALS on cognitive and motor processes.}, }
@article {pmid38907638, year = {2024}, author = {Selvaraj, V and Alagarsamy, M and Datchanamoorthy, K and Manickam, G}, title = {Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2024.2356633}, pmid = {38907638}, issn = {1476-8259}, abstract = {The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy; 14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.}, }
@article {pmid38905990, year = {2024}, author = {Zhang, Y and Zhang, H and Xu, T and Liu, J and Mu, J and Chen, R and Yang, J and Wang, P and Jian, X}, title = {A simulation study of transcranial magnetoacoustic stimulation of the basal ganglia thalamic neural network to improve pathological beta oscillations in Parkinson's disease.}, journal = {Computer methods and programs in biomedicine}, volume = {254}, number = {}, pages = {108297}, doi = {10.1016/j.cmpb.2024.108297}, pmid = {38905990}, issn = {1872-7565}, mesh = {*Parkinson Disease/physiopathology/therapy ; Humans ; *Basal Ganglia/physiopathology/diagnostic imaging ; *Thalamus/diagnostic imaging ; *Beta Rhythm ; Computer Simulation ; Transcranial Magnetic Stimulation/methods ; Nerve Net/physiopathology/diagnostic imaging ; Models, Neurological ; }, abstract = {BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively.<br><br>OBJECTIVE: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage.<br><br>METHODS: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network.<br><br>RESULTS: When the stimulating effect of ultrasound is ignored, TMAS-STN simultaneously inhibits the beta-band amplitude of the GPi nucleus, whereas TMAS-GPi fails to simultaneously have an inhibitory effect on the STN. TMAS-STN&amp;GPi can reduce the beta band amplitude. TMAS-STN&amp;GPi&amp;GPe suppressed the PD pathologic beta band amplitude of each nucleus to a greater extent. When considering the stimulatory effect of ultrasound, lower sound pressures of ultrasound do not affect the neuronal firing state, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents.<br><br>CONCLUSIONS: At 9 T static magnetic field, 0.5-1.5 MPa and 1.5-2.0 MPa ultrasound had synergistic effects on individual STN and GPi neurons. TMAS multinuclear stimulation with appropriate ultrasound intensity was the most effective in suppressing the amplitude of pathological beta oscillations in PD and may be clinically useful.}, }
@article {pmid38905761, year = {2024}, author = {Qiao, MX and Yu, H and Li, T}, title = {Non-invasive neurostimulation to improve sleep quality and depressive symptoms in patients with major depressive disorder: A meta-analysis of randomized controlled trials.}, journal = {Journal of psychiatric research}, volume = {176}, number = {}, pages = {282-292}, doi = {10.1016/j.jpsychires.2024.06.023}, pmid = {38905761}, issn = {1879-1379}, mesh = {Humans ; *Depressive Disorder, Major/therapy ; *Randomized Controlled Trials as Topic ; *Transcranial Direct Current Stimulation ; *Transcranial Magnetic Stimulation ; *Phototherapy/methods ; Sleep Quality ; Sleep Wake Disorders/therapy/etiology ; Outcome Assessment, Health Care ; }, abstract = {BACKGROUND: Non-invasive neurostimulation, including bright light therapy (BLT), repetitive transcranial magnetic (rTMS) and transcranial direct current stimulation (tDCS), has been shown to alleviate depressive symptoms in major depressive disorder (MDD). However, the efficacy of these interventions in addressing sleep disturbances in MDD patients remains a subject of debate.<br><br>OBJECTIVE: We aimed to conduct a meta-analysis of available randomized controlled trials (RCTs) to assess the effectiveness of non-invasive neurostimulation in improving sleep disturbances and depressive symptoms in MDD patients.<br><br>METHODS: Systematic searches for relevant RCTs were conducted in the databases PubMed, Cochrane Library, Web of Science, EMBASE, Wanfang and China National Knowledge Infrastructure up to January 2024. Data on outcomes comparable across the studies were meta-analyzed using Review Manager 5.3 and Stata 14. The pooled results were reported as standardized mean differences (SMD) with their respective 95% confidence intervals (CI).<br><br>RESULTS: Our analysis encompassed 15 RCTs involving 1348 patients. Compared to sham or no stimulation, non-invasive neurostimulation significantly improved sleep quality (SMD -0.74, 95%CI -1.15 to -0.33, p&#xa0;=&#xa0;0.0004) and sleep efficiency (SMD 0.35, 95%CI 0.10 to 0.60, p&#xa0;=&#xa0;0.006). It also significantly reduced severity of depressive symptoms (SMD -0.62, 95%CI -0.90 to -0.35, p&#xa0;<&#xa0;0.00001). Subgroup analysis further demonstrated that patients experiencing sleep improvements due to neurostimulation showed a marked decrease in depressive symptoms compared to the control group (SMD&#xa0;=&#xa0;-0.90, 95% CI [-1.26, -0.54], p&#xa0;<&#xa0;0.0001).<br><br>CONCLUSION: Current evidence from RCTs suggests that neurostimulation can enhance sleep quality and efficiency in individuals with MDD, which in turn may be associated with mitigation of depressive symptoms.<br><br>PROSPERO REGISTRATION: CRD42023423844.}, }
@article {pmid38903906, year = {2024}, author = {Klein, F}, title = {Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1286586}, pmid = {38903906}, issn = {2673-6195}, abstract = {The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.}, }
@article {pmid38903410, year = {2024}, author = {Li, K and Fu, C and Xie, Z and Zhang, J and Zhang, C and Li, R and Gao, C and Wang, J and Xue, C and Zhang, Y and Deng, W}, title = {The impact of physical therapy on dysphagia in neurological diseases: a review.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1404398}, pmid = {38903410}, issn = {1662-5161}, abstract = {A neurogenic dysphagia is dysphagia caused by problems with the central and peripheral nervous systems, is particularly prevalent in conditions such as Parkinson's disease and stroke. It significantly impacts the quality of life for affected individuals and causes additional burdens, such as malnutrition, aspiration pneumonia, asphyxia, or even death from choking due to improper eating. Physical therapy offers a non-invasive treatment with high efficacy and low cost. Evidence supporting the use of physical therapy in dysphagia treatment is increasing, including techniques such as neuromuscular electrical stimulation, sensory stimulation, transcranial direct current stimulation, and repetitive transcranial magnetic stimulation. While initial studies have shown promising results, the effectiveness of specific treatment regimens still requires further validation. At present, there is a lack of scientific evidence to guide patient selection, develop appropriate treatment regimens, and accurately evaluate treatment outcomes. Therefore, the primary objectives of this review are to review the results of existing research, summarize the application of physical therapy in dysphagia management, we also discussed the mechanisms and treatments of physical therapy for neurogenic dysphagia.}, }
@article {pmid38903409, year = {2024}, author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Fu, Y and Gong, A}, title = {Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1429130}, pmid = {38903409}, issn = {1662-5161}, abstract = {Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications.}, }
@article {pmid38901399, year = {2024}, author = {Wang, L and Gu, Y}, title = {Fuse mitochondria to win peer competition.}, journal = {Neuron}, volume = {112}, number = {12}, pages = {1897-1899}, doi = {10.1016/j.neuron.2024.05.005}, pmid = {38901399}, issn = {1097-4199}, mesh = {Animals ; *Neuronal Plasticity/physiology ; *Mitochondria/metabolism ; *Neurons/physiology ; Mitochondrial Dynamics/physiology ; Humans ; }, abstract = {In this issue of Neuron, Kochan et al.[1] report that enhanced mitochondrial fusion is essential for the heightened synaptic plasticity in adult-born neurons during the critical period, thus supporting their competition with neurons of similar age for survival.}, }
@article {pmid38901186, year = {2024}, author = {Ferrante, M and Boccato, T and Bargione, S and Toschi, N}, title = {Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models.}, journal = {Computers in biology and medicine}, volume = {178}, number = {}, pages = {108701}, doi = {10.1016/j.compbiomed.2024.108701}, pmid = {38901186}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Male ; Image Processing, Computer-Assisted/methods ; Signal Processing, Computer-Assisted ; Female ; Adult ; }, abstract = {Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6 participants for the ImageNet dataset and 10 for the THINGS-EEG 2 dataset, exposed to images spanning unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 87%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images that had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for, e.g., swift, individualized feedback experiments.}, }
@article {pmid38900612, year = {2024}, author = {Galvan, CM and Spies, RD and Milone, DH and Peterson, V}, title = {Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2346-2355}, doi = {10.1109/TNSRE.2024.3417311}, pmid = {38900612}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Computer Simulation ; *Algorithms ; Deep Learning ; }, abstract = {Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.}, }
@article {pmid38897536, year = {2024}, author = {Kapgate, DD}, title = {Effect of inverted faces as visual stimuli on the performance of the hybrid SSVEP + P300 brain computer interface.}, journal = {Brain research}, volume = {1841}, number = {}, pages = {149092}, doi = {10.1016/j.brainres.2024.149092}, pmid = {38897536}, issn = {1872-6240}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Male ; Female ; *Photic Stimulation/methods ; *Electroencephalography/methods ; Young Adult ; Adult ; Face ; Pattern Recognition, Visual/physiology ; Brain/physiology ; }, abstract = {INTRODUCTION: This study proposes a hybrid brain-computer interface (BCI) system that simultaneously evokes steady-state visual evoked potentials (SSVEP) and event-related potentials (P300). The goal of this study was to improve the performance of the current hybrid SSVEP + P300 BCI systems by incorporating inverted faces into visual stimuli.<br><br>METHODS: In this study, upright and inverted faces were added to visual stimulus to elicit stronger cortical responses in a hybrid SSVEP&#xa0;+&#xa0;P300 BCI. We also considered triggering the P300 signals with facial stimuli and the SSVEP signals with non-facial stimuli. We have tested four paradigms: the upright face paradigm (UF), the inverted face paradigm (IF), the upright face and flicker paradigm (UFF), and the inverted face and flicker paradigm (IFF).<br><br>RESULTS AND CONCLUSIONS: The results showed that the IFF paradigm evoked more robust cortical responses, which led to enhanced system accuracy and ITR. The IFF paradigm had an average accuracy of 96.6% and a system communication rate of 26.45 bits per second. The UFF paradigm is the best candidate for BCI applications among other paradigms because it provides maximum comfort while maintaining a reasonable ITR.}, }
@article {pmid38897235, year = {2024}, author = {Wang, R and Chen, ZS}, title = {Large-scale foundation models and generative AI for BigData neuroscience.}, journal = {Neuroscience research}, volume = {}, number = {}, pages = {}, pmid = {38897235}, issn = {1872-8111}, support = {UG3 NS135170/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; }, abstract = {Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.}, }
@article {pmid38897146, year = {2024}, author = {Lian, S and Li, Z}, title = {An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.}, journal = {Computers in biology and medicine}, volume = {178}, number = {}, pages = {108727}, doi = {10.1016/j.compbiomed.2024.108727}, pmid = {38897146}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; }, abstract = {Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.}, }
@article {pmid38896527, year = {2025}, author = {Yang, C and Zhang, Z and Zhang, L and Zhang, Y and Li, Z and Luo, Y and Pan, G and Zhao, B}, title = {Neural Dielet 2.0: A 128-Channel 2mm2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission Through Multi-Carrier Orthogonal Backscatter.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {19}, number = {1}, pages = {226-237}, doi = {10.1109/TBCAS.2024.3416728}, pmid = {38896527}, issn = {1940-9990}, mesh = {*Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Equipment Design ; *Signal Processing, Computer-Assisted/instrumentation ; Electric Power Supplies ; Humans ; *Neurons/physiology ; }, abstract = {Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm2mm die, and then the all-digital implementation achieves 0.02mm area and 9.87W power per channel.}, }
@article {pmid38896526, year = {2024}, author = {Xu, W and Tang, J and Qi, H}, title = {Using the Cocktail Party Effect to Add the Coding Dimension of Auditory Event Related Potential Brain-Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {10}, pages = {5953-5961}, doi = {10.1109/JBHI.2024.3416488}, pmid = {38896526}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; *Evoked Potentials, Auditory/physiology ; Adult ; Voice/physiology ; }, abstract = {OBJECTIVE: The auditory event-related potential based brain-computer interface (aERP-BCI) is a classical paradigm of brain-computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect.<br><br>METHODS: The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed.<br><br>CONCLUSION: The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC.<br><br>SIGNIFICANCE: The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.}, }
@article {pmid38895473, year = {2024}, author = {Alcolea, P and Ma, X and Bodkin, K and Miller, LE and Danziger, ZC}, title = {Less is more: selection from a small set of options improves BCI velocity control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38895473}, issn = {2692-8205}, support = {R01 NS109257/NS/NINDS NIH HHS/United States ; }, abstract = {We designed the discrete direction selection (DDS) decoder for intracortical brain computer interface (iBCI) cursor control and showed that it outperformed currently used decoders in a human-operated real-time iBCI simulator and in monkey iBCI use. Unlike virtually all existing decoders that map between neural activity and continuous velocity commands, DDS uses neural activity to select among a small menu of preset cursor velocities. We compared closed-loop cursor control across four visits by each of 48 naïve, able-bodied human subjects using either DDS or one of three common continuous velocity decoders: direct regression with assist (an affine map from neural activity to cursor velocity), ReFIT, and the velocity Kalman Filter. DDS outperformed all three by a substantial margin. Subsequently, a monkey using an iBCI also had substantially better performance with DDS than with the Wiener filter decoder (direct regression decoder that includes time history). Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of simplifying online iBCI control.}, }
@article {pmid38895388, year = {2024}, author = {Liu, J and Younk, R and Drahos, LM and Nagrale, SS and Yadav, S and Widge, AS and Shoaran, M}, title = {Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38895388}, issn = {2692-8205}, support = {R01 MH123634/MH/NIMH NIH HHS/United States ; }, abstract = {OBJECTIVE: Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.<br><br>APPROACH: We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.<br><br>MAIN RESULTS: Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.<br><br>SIGNIFICANCE: Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.}, }
@article {pmid38895167, year = {2024}, author = {Yao, Y and Hasan, WZW and Jiao, W and Dong, X and Ramli, HR and Norsahperi, NMH and Wen, D}, title = {ChatGPT and BCI-VR: a new integrated diagnostic and therapeutic perspective for the accurate diagnosis and personalized treatment of mild cognitive impairment.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1426055}, pmid = {38895167}, issn = {1662-5161}, }
@article {pmid38894334, year = {2024}, author = {Kim, D and Kim, Y and Park, J and Choi, H and Ryu, H and Loeser, M and Seo, K}, title = {Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {11}, pages = {}, pmid = {38894334}, issn = {1424-8220}, support = {This study was financially supported by Seoul National University of Science and Technology.//This study was financially supported by Seoul National University of Science and Technology./ ; }, mesh = {Humans ; *Cognitive Dysfunction/diagnosis/physiopathology ; *Electroencephalography/methods ; Aged ; Male ; Female ; *Evoked Potentials, Visual/physiology ; *Virtual Reality ; Alzheimer Disease/physiopathology ; Neuropsychological Tests ; Activities of Daily Living ; Middle Aged ; }, abstract = {Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.}, }
@article {pmid38894311, year = {2024}, author = {Sun, Q and Zhang, S and Dong, G and Pei, W and Gao, X and Wang, Y}, title = {High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {11}, pages = {}, pmid = {38894311}, issn = {1424-8220}, support = {62071447//National Natural Science Foundation of China/ ; 2022YFF1202303//National Key R&amp;D Program of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; *Algorithms ; Female ; Young Adult ; Photic Stimulation ; Electrodes ; Signal Processing, Computer-Assisted ; }, abstract = {In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.}, }
@article {pmid38894107, year = {2024}, author = {Ron-Angevin, R and Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Lespinet-Najib, V and André, JM}, title = {Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain-Computer Interface Speller under Rapid Serial Visual Presentation.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {11}, pages = {}, pmid = {38894107}, issn = {1424-8220}, support = {PID2021-127261OB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2021-127261OB-I00//Agencia Estatal de Investigaci&#xf3;n/ ; PID2021-127261OB-I00//European Regional Development Fund/ ; PID2021-127261OB-I00//Universidad de M&#xe1;laga/ ; PID2021-127261OB-I00//Instituto Universitario de Investigaci&#xf3;n en Telecomunicaci&#xf3;n de la Universidad de M&#xe1;laga/ ; PID2021-127261OB-I00//Institut Polytechnique de Bordeaux/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Electroencephalography/methods ; Young Adult ; *Photic Stimulation ; *Evoked Potentials/physiology ; Eye Movements/physiology ; }, abstract = {Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces-particularly those tinted red-have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems.}, }
@article {pmid38891906, year = {2024}, author = {Venetis, K and Pescia, C and Cursano, G and Frascarelli, C and Mane, E and De Camilli, E and Munzone, E and Dellapasqua, S and Criscitiello, C and Curigliano, G and Guerini Rocco, E and Fusco, N}, title = {The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy.}, journal = {International journal of molecular sciences}, volume = {25}, number = {11}, pages = {}, pmid = {38891906}, issn = {1422-0067}, mesh = {Humans ; *Breast Neoplasms/genetics/diagnosis/therapy ; Female ; Prognosis ; *Genomics/methods ; Biomarkers, Tumor/genetics ; Genetic Testing/methods ; }, abstract = {Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score[®], MammaPrint, Prosigna[®], EndoPredict[®], and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict[®] have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.}, }
@article {pmid38891733, year = {2024}, author = {Balčiauskas, L and Balčiauskienė, L}, title = {Insight into Body Condition Variability in Small Mammals.}, journal = {Animals : an open access journal from MDPI}, volume = {14}, number = {11}, pages = {}, pmid = {38891733}, issn = {2076-2615}, abstract = {The body condition index (BCI) is an indicator of both reproductive success and health in small mammals and might help to understand ecological roles of species. We analyzed BCI data from 28,567 individuals trapped in Lithuania between 1980 and 2023. We compared BCIs between species and examined differences in age groups, gender, and reproductive statuses within each species. Seven out of eighteen species had sample sizes with N < 100. In terms of species, we found that seven of the eight species with the highest average BCIs are granivores or omnivores, which can consume animal-based food at least seasonally. The two contrasting (decreasing or increasing) BCI patterns observed during ontogeny can be related to diet differences among juveniles, subadults, and adult animals. Our results demonstrate that reproductive stress has a negative impact on the BCI of adult females in all analyzed species and nearly all adult males. Although the animals with extremely low BCI consisted mostly of shrews, for the first time we found 23 common and pygmy shrews exhibiting the Chitty effect, i.e., a very high body mass resulting in a BCI > 5.0. This is the first multi-species approach of body condition at middle latitudes. The results increase our understanding of how changing environmental conditions are affecting small mammals.}, }
@article {pmid38890235, year = {2024}, author = {Wang, S and Zhang, X and Zhao, Y and Lv, H and Li, P and Zhang, Z and Qiao, X}, title = {BCI Improves Alcohol-Induced Cognitive and Emotional Impairments by Restoring pERK-BDNF.}, journal = {Journal of molecular neuroscience : MN}, volume = {74}, number = {3}, pages = {59}, pmid = {38890235}, issn = {1559-1166}, support = {82101978//National Natural Science Foundation of China/ ; 212300410260//Natural Science Foundation of Henan Province/ ; }, mesh = {Animals ; *Brain-Derived Neurotrophic Factor/metabolism/genetics ; Mice ; Male ; *Mice, Inbred C57BL ; *Dual Specificity Phosphatase 1/metabolism/genetics ; *Prefrontal Cortex/metabolism/drug effects ; *Ethanol/toxicity/pharmacology ; Dual Specificity Phosphatase 6/metabolism/genetics ; Aminoacetonitrile/analogs &amp; derivatives/pharmacology/therapeutic use ; Anxiety/drug therapy/etiology ; MAP Kinase Signaling System ; }, abstract = {Binge drinking causes a range of problems especially damage to the nervous system, and the specific neural mechanism of brain loss and behavioral abnormalities caused by which is still unclear. Extracellular regulated protein kinases (ERK) maintain neuronal survival, growth, and regulation of synaptic plasticity by phosphorylating specific transcription factors to regulate expression of brain-derived neurotrophic factor (BDNF). Dual-specific phosphatase 1 (DUSP1) and DUSP6 dephosphorylate tyrosine and serine/threonine residues in ERK1/2 to inactivate them. To investigate the molecular mechanism by which alcohol affects memory and emotion, a chronic intermittent alcohol exposure (CIAE) model was established. The results demonstrated that mice in the CIAE group developed short-term recognition memory impairment and anxiety-like behavior; meanwhile, the expression of DUSP1 and DUSP66 in the mPFC was increased, while the levels of p-ERK and BDNF were decreased. Micro-injection of DUSP1/6 inhibitor BCI into the medial prefrontal cortex (mPFC) restored the dendritic morphology by reversing the activity of ERK-BDNF and ultimately improved cognitive and emotional impairment caused by CIAE. These findings indicate that CIAE inhibits ERK-BDNF by increasing DUSP1/6 in the mPFC that may be associated with cognitive and emotional deficits. Consequently, DUSP1 and DUSP6 appear to be potential targets for the treatment of alcoholic brain disorders.}, }
@article {pmid38889028, year = {2024}, author = {Wu, D and Li, S and Yang, J and Sawan, M}, title = {Neuro-BERT: Rethinking Masked Autoencoding for Self-Supervised Neurological Pretraining.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3415959}, pmid = {38889028}, issn = {2168-2208}, abstract = {Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, which is often scarce and expensive to acquire, requiring specialized infrastructure and domain expertise. To address the appetite for data in deep learning, we present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. The intuition behind our approach is simple: frequency and phase distribution of neurological signals can reveal intricate neurological activities. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information using the Fourier inversion theorem. Pre-trained models can be potentially used for various downstream tasks such as sleep stage classification and gesture recognition. Unlike contrastive-based methods, which strongly rely on carefully hand-crafted augmentations and siamese structure, our approach works reasonably well with a simple transformer encoder with no augmentation requirements. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.}, }
@article {pmid38889026, year = {2024}, author = {Amrani, H and Micucci, D and Napoletano, P}, title = {Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3416066}, pmid = {38889026}, issn = {2168-2208}, abstract = {Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, leading to uncertainty about the best approach to enhance decoding performance. Additionally, current evaluation metrics used to assess decoding effectiveness are predominantly syntactic and do not provide insights into the comprehensibility of the decoded output for human understanding. We present an end-to-end architecture for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our proposal introduces the following innovations: 1) an end-to-end deep learning architecture for open vocabulary EEG decoding, incorporating a subject-dependent representation learning module for raw EEG encoding, a BART language model, and a GPT-4 sentence refinement module; 2) a more comprehensive sentence-level evaluation metric based on the BERTScore; 3) an ablation study that analyses the contributions of each module within our proposal, providing valuable insights for future research. We evaluate our approach on two publicly available datasets, ZuCo v1.0 and v2.0, comprising EEG recordings of 30 subjects engaged in natural reading tasks. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, achieving an increment over the previous state-of-the-art by 1.40%, 2.59%, and 3.20%, respectively.}, }
@article {pmid38888742, year = {2024}, author = {Bérubé, M and Verret, M and Bourque, L and Côté, C and Guénette, L and Richard-Denis, A and Ouellet, S and Singer, LN and Gauthier, L and Gagnon, MP and Gagnon, MA and Martorella, G}, title = {Educational needs and preferences of adult patients with acute pain: a mixed-methods systematic review.}, journal = {Pain}, volume = {165}, number = {12}, pages = {e162-e183}, pmid = {38888742}, issn = {1872-6623}, mesh = {Humans ; *Acute Pain/therapy ; *Patient Preference/psychology ; *Patient Education as Topic/methods ; *Pain Management/methods ; Adult ; }, abstract = {Many patients experience acute pain, which has been associated with numerous negative consequences. Pain education has been proposed as a strategy to improve acute pain management. However, studies report limited effects with educational interventions for acute pain in adults, which can be explained by the underuse of the person-centered approach. Thus, we aimed to systematically review and synthetize current evidence from quantitative, qualitative and mixed-methods studies describing patients' needs and preferences for acute pain education in adults. We searched original studies and gray literature in 7 databases, from January 1990 to October 2023. Methodological quality was assessed with the Mixed Methods Appraisal Tool. A total of 32 studies were included (n = 1847 patients), two-thirds of which were qualitative studies of high methodological quality. Most of the studies were conducted over the last 15 years in patients with postsurgical and posttraumatic pain, identified as White, with a low level of education. Patients expressed the greatest need for education when it came to what to expect in pain intensity and duration, as well how to take the medication and its associated adverse effects. The most frequently reported educational preferences were for in-person education while involving caregivers and to obtain information first from physicians, then by other professionals. This review has highlighted the needs and preferences to be considered in pain education interventions, which should be embedded in an approach cultivating communication and partnership with patients and their caregivers. The results still need to be confirmed with different patient populations.}, }
@article {pmid38887162, year = {2024}, author = {Xu, L and Saeed, S and Ma, X and Cen, X and Sun, Y and Tian, Y and Zhang, X and Zhang, D and Tang, A and Zhou, H and Lai, J and Xia, H and Hu, S}, title = {Hippocampal mitophagy contributes to spatial memory via maintaining neurogenesis during the development of mice.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {6}, pages = {e14800}, pmid = {38887162}, issn = {1755-5949}, support = {LR22C070002//Natural Science Foundation of Zhejiang Province/ ; 32100598//National Natural Science Foundation of China/ ; 32222023//National Natural Science Foundation of China/ ; Y202353713//Scientific Research Fund of 577 Zhejiang Provincial Education Department/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2024R401199//Postgraduate Science and Technology Innovation activity and "Xinmiao" Program of Zhejiang University/ ; }, mesh = {Animals ; *Neurogenesis/physiology/drug effects ; *Mitophagy/physiology/drug effects ; *Spatial Memory/physiology ; *Hippocampus ; Male ; Mice ; Mice, Inbred C57BL ; Mitochondrial Dynamics/physiology ; Quinazolinones ; }, abstract = {BACKGROUND: Impaired mitochondrial dynamics have been identified as a significant contributing factor to reduced neurogenesis under pathological conditions. However, the relationship among mitochondrial dynamics, neurogenesis, and spatial memory during normal development remains unclear. This study aims to elucidate the role of mitophagy in spatial memory mediated by neurogenesis during development.<br><br>METHODS: Adolescent and adult male mice were used to assess spatial memory performance. Immunofluorescence staining was employed to evaluate levels of neurogenesis, and mitochondrial dynamics were assessed through western blotting and transmission electron microscopy. Pharmacological interventions further validated the causal relationship among mitophagy, neurogenesis, and behavioral performance during development.<br><br>RESULTS: The study revealed differences in spatial memory between adolescent and adult mice. Diminished neurogenesis, accompanied by reduced mitophagy, was observed in the hippocampus of adult mice compared to adolescent subjects. Pharmacological induction of mitophagy in adult mice with UMI-77 resulted in enhanced neurogenesis and prolonged spatial memory retention. Conversely, inhibition of mitophagy with Mdivi-1 in adolescent mice led to reduced hippocampal neurogenesis and impaired spatial memory.<br><br>CONCLUSION: The observed decline in spatial memory in adult mice is associated with decreased mitophagy, which affects neurogenesis in the dentate gyrus. This underscores the therapeutic potential of enhancing mitophagy to counteract age- or disease-related cognitive decline.}, }
@article {pmid38886591, year = {2024}, author = {Momeny, M and Tienhaara, M and Sharma, M and Chakroborty, D and Varjus, R and Takala, I and Merisaari, J and Padzik, A and Vogt, A and Paatero, I and Elenius, K and Laajala, TD and Kurppa, KJ and Westermarck, J}, title = {DUSP6 inhibition overcomes neuregulin/HER3-driven therapy tolerance in HER2+ breast cancer.}, journal = {EMBO molecular medicine}, volume = {16}, number = {7}, pages = {1603-1629}, pmid = {38886591}, issn = {1757-4684}, mesh = {*Dual Specificity Phosphatase 6/metabolism/genetics ; Humans ; *Breast Neoplasms/drug therapy/pathology/metabolism/genetics ; Female ; *Receptor, ErbB-2/metabolism ; Animals ; *Receptor, ErbB-3/metabolism/genetics/antagonists &amp; inhibitors ; Cell Line, Tumor ; Mice ; Drug Resistance, Neoplasm/drug effects ; Antineoplastic Agents/pharmacology/therapeutic use ; Protein Kinase Inhibitors/pharmacology ; }, abstract = {Despite clinical benefits of tyrosine kinase inhibitors (TKIs) in cancer, most tumors can reactivate proliferation under TKI therapy. Here we present transcriptional profiling of HER2+ breast cancer cells transitioning from dormant drug tolerant cells to re-proliferating cells under continuous HER2 inhibitor (HER2i) therapy. Focusing on phosphatases, expression of dual-specificity phosphatase DUSP6 was found inhibited in dormant cells, but strongly induced upon regrowth. DUSP6 expression also selectively associated with poor patient survival in HER2+ breast cancers. DUSP6 overexpression conferred apoptosis resistance, whereas its pharmacological blockade prevented therapy tolerance development under HER2i therapy. DUSP6 targeting also synergized with clinically used HER2i combination therapies. Mechanistically DUSP6 is a positive regulator of HER3 expression, and its impact on HER2i tolerance was mediated by neuregulin-HER3 axis. In vivo, genetic targeting of DUSP6 reduced tumor growth in brain metastasis model, whereas its pharmacological targeting induced synthetic lethal therapeutic effect in combination with HER2i. Collectively this work demonstrates that DUSP6 drives escape from HER2i-induced dormancy, and that DUSP6 is a druggable target to overcome HER3-driven TKI resistance.}, }
@article {pmid38885847, year = {2024}, author = {Gao, Y and Zhu, Z and Fang, F and Zhang, Y and Meng, M}, title = {EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.}, journal = {Journal of affective disorders}, volume = {361}, number = {}, pages = {356-366}, doi = {10.1016/j.jad.2024.06.042}, pmid = {38885847}, issn = {1573-2517}, mesh = {Humans ; *Electroencephalography ; *Emotions/physiology ; *Support Vector Machine ; Brain/physiology ; Adult ; Brain-Computer Interfaces ; Algorithms ; Female ; Male ; Young Adult ; Signal Processing, Computer-Assisted ; }, abstract = {Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.}, }
@article {pmid38885688, year = {2024}, author = {Wu, X and Wellington, S and Fu, Z and Zhang, D}, title = {Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad593a}, pmid = {38885688}, issn = {1741-2552}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Speech/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Epilepsy/physiopathology/diagnosis ; Stereotaxic Techniques ; Adult ; Neural Networks, Computer ; }, abstract = {Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.}, }
@article {pmid38885683, year = {2024}, author = {Jin, J and Bai, G and Xu, R and Qin, K and Sun, H and Wang, X and Cichocki, A}, title = {A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad593b}, pmid = {38885683}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Transfer, Psychology/physiology ; Support Vector Machine ; Electroencephalography/methods ; Movement/physiology ; Algorithms ; Machine Learning ; Databases, Factual ; Male ; }, abstract = {Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs.Approach.We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine.Main results.Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively.Significance.By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.}, }
@article {pmid38885679, year = {2024}, author = {Whitsitt, Q and Saxena, A and Patel, B and Evans, BM and Hunt, B and Purcell, EK}, title = {Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, pmid = {38885679}, issn = {1741-2552}, support = {R01 NS107451/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Rats ; *Motor Cortex/physiology/metabolism ; *Electrodes, Implanted ; *Transcriptome ; Male ; *Microelectrodes ; Rats, Sprague-Dawley ; Brain-Computer Interfaces ; Neurons/physiology/metabolism ; }, abstract = {Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.}, }
@article {pmid38885677, year = {2024}, author = {Chen, W and Wang, S and Bao, J and Yu, C and Jiang, Q and Song, J and Zheng, Y and Hao, Y and Xu, K}, title = {Restoration of coherent reach-grasp-pull movement via sequential intraneural peripheral nerve stimulation in rats.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5935}, pmid = {38885677}, issn = {1741-2552}, mesh = {Animals ; Rats ; *Rats, Sprague-Dawley ; *Peripheral Nerves/physiology ; *Movement/physiology ; *Hand Strength/physiology ; Muscle, Skeletal/physiology/innervation ; Male ; Electric Stimulation Therapy/methods ; Electrodes, Implanted ; Electromyography/methods ; }, abstract = {Objective.Peripheral nerve stimulation (PNS) has been demonstrated as an effective way to selectively activate muscles and to produce fine hand movements. However, sequential multi-joint upper limb movements, which are critical for paralysis rehabilitation, has not been tested with PNS. Here, we aimed to restore multiple upper limb joint movements through an intraneural interface with a single electrode, achieving coherent reach-grasp-pull movement tasks through sequential stimulation.Approach.A transverse intrafascicular multichannel electrode was implanted under the axilla of the rat's upper limb, traversing the musculocutaneous, radial, median, and ulnar nerves. Intramuscular electrodes were implanted into the biceps brachii (BB), triceps brachii (TB), flexor carpi radialis (FCR), and extensor carpi radialis (ECR) muscles to record electromyographic (EMG) activity and video recordings were used to capture the kinematics of elbow, wrist, and digit joints. Charge-balanced biphasic pulses were applied to different channels to recruit distinct upper limb muscles, with concurrent recording of EMG signals and joint kinematics to assess the efficacy of the stimulation. Finally, a sequential stimulation protocol was employed by generating coordinated pulses in different channels.Main results.BB, TB, FCR and ECR muscles were selectively activated and various upper limb movements, including elbow flexion, elbow extension, wrist flexion, wrist extension, digit flexion, and digit extension, were reliably generated. The modulation effects of stimulation parameters, including pulse width, amplitude, and frequency, on induced joint movements were investigated and reach-grasp-pull movement was elicited by sequential stimulation.Significance.Our results demonstrated the feasibility of sequential intraneural stimulation for functional multi-joint movement restoration, providing a new approach for clinical rehabilitation in paralyzed patients.}, }
@article {pmid38885099, year = {2024}, author = {Wang, H and Wang, Z and Sun, Y and Yuan, Z and Xu, T and Li, J}, title = {A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2270-2280}, doi = {10.1109/TNSRE.2024.3415474}, pmid = {38885099}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Neural Networks, Computer ; Photic Stimulation ; Communication Devices for People with Disabilities ; Reproducibility of Results ; Male ; }, abstract = {Visual-based brain-computer interface (BCI) enables people to communicate with others by spelling words from the brain and helps professionals recognize targets in large numbers of images. P300 signals evoked by different types of stimuli, such as words or images, may vary significantly in terms of both amplitude and latency. A unified approach is required to detect variable P300 signals, which facilitates BCI applications, as well as deepens the understanding of the P300 generation mechanism. In this study, our proposed approach involves a cascade network structure that combines xDAWN and classical EEGNet techniques. This network is designed to classify target and non-target stimuli in both P300 speller and rapid serial visual presentation (RSVP) paradigms. The proposed approach is capable of recognizing more symbols with fewer repetitions (up to 5 rounds) compared to other models while possessing a better information transfer rate (ITR) as demonstrated on Dataset II (17.22 bits/min in the second repetition round) of BCI Competition III. Additionally, our approach has the highest unweighted average recall (UAR) performance for both 5 Hz (0.8134±0.0259) and 20 Hz (0.6527±0.0321) RSVP. The results show that the cascade network structure has better performance between both the P300 Speller and RSVP paradigms, manifesting that such a cascade structure is robust enough for dealing with P300-related signals (source code is available at https://github.com/embneural/Cascade-xDAWN-EEGNet-for-ERP-Detection).}, }
@article {pmid38885097, year = {2024}, author = {Shao, Y and Zhou, Y and Gong, P and Sun, Q and Zhang, D}, title = {A Dual-Adversarial Model for Cross-Time and Cross-Subject Cognitive Workload Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2324-2335}, doi = {10.1109/TNSRE.2024.3415364}, pmid = {38885097}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography ; *Cognition/physiology ; *Workload ; *Algorithms ; *Brain-Computer Interfaces ; Reproducibility of Results ; }, abstract = {Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.}, }
@article {pmid38881541, year = {2024}, author = {Rocha, SV and Alves Dos Santos, C and Conceição, AF and Palotino-Ferreira, BM and Morais, DB and Chavane, FS and Chaves Dias, CR and Lacerda Bachi, AL and Mendes, R and Brito-Costa, S and Silva, S and Furtado, GE}, title = {Implementing regular physical activity for older individuals in the family strategy program using the RE-AIM framework to ensure feasibility and sustainability: EISI study protocol.}, journal = {Contemporary clinical trials communications}, volume = {39}, number = {}, pages = {101311}, pmid = {38881541}, issn = {2451-8654}, abstract = {The EISI study protocol aims to address the low participation rate in physical exercise programs among older individuals, emphasizing its significance as a non-pharmacological therapeutic approach for overall health and increased physical activity. The objectives include implementing physical activity (PA) and educational health programs in Jequié, Bahia, Brazil, targeting the Family Health Strategy population to enhance local physical activity levels among older individuals. The study also seeks to evaluate the program's feasibility, safety, and sustainability for large-scale implementation, along with assessing its impact on immune and inflammatory response biomarkers to the SARS-CoV virus, as well as physical-functional and brain health. Participants, aged 60 or above, will be divided into two groups: multicomponent exercise (MCE) and behavioral change interventions (BCI). The study employs a mixed-method approach, utilizing a non-randomized controlled short-term pathway model for a 4-8 weeks of pilot study and 16-week intervention impact assessment. Data collection encompasses various aspects such as sociodemographic information, mental health, physical fitness, fall risk, functional capacity, anthropometric measurements, hemodynamic assessment, habitual physical activity, and health-related quality of life. Blood and saliva samples are collected for cytokine and antibody biomarker analysis related to SARS-CoV immunity. Pre- and post-intervention evaluations for both groups will be conducted, with the hypothesis that MCE will yield more favorable responses compared to BCI. The study's holistic approach, including the assessment of feasibility, safety, and sustainability, aims to contribute to achieving Sustainable Development Goals (SDG) 3 and SDG 9 b y promoting accessible and sustainable healthcare initiatives for older individuals. This research aligns with global efforts to enhance health and well-being, emphasizing the importance of regular exercise in the aging population.}, }
@article {pmid38880343, year = {2024}, author = {Ponrani, MA and Anand, M and Alsaadi, M and Dutta, AK and Fayaz, R and Mathew, S and Chaurasia, MA and Sunila,  and Bhende, M}, title = {Brain-computer interfaces inspired spiking neural network model for depression stage identification.}, journal = {Journal of neuroscience methods}, volume = {409}, number = {}, pages = {110203}, doi = {10.1016/j.jneumeth.2024.110203}, pmid = {38880343}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Depression/physiopathology/diagnosis ; Brain/physiopathology/physiology ; Deep Learning ; Models, Neurological ; Adult ; Action Potentials/physiology ; }, abstract = {BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Interfaces inspired deep learning-assisted diagnosis based on physiological signals holds promise for improving traditional methods lacking physiological basis and leads next generation neuro-technologies. However, traditional deep learning methods rely on immense computational power and mostly involve end-to-end network learning. These learning methods also lack physiological interpretability, limiting their clinical application in assisted diagnosis.<br><br>METHODOLOGY: A brain-like learning model for diagnosing depression using electroencephalogram (EEG) is proposed. The study collects EEG data using 128-channel electrodes, producing a 128&#xd7;128 brain adjacency matrix. Given the assumption of undirected connectivity, the upper half of the 128&#xd7;128 matrix is chosen in order to minimise the input parameter size, producing 8,128-dimensional data. After eliminating 28 components derived from irrelevant or reference electrodes, a 90&#xd7;90 matrix is produced, which can be used as an input for a single-channel brain-computer interface image.<br><br>RESULT: At the functional level, a spiking neural network is constructed to classify individuals with depression and healthy individuals, achieving an accuracy exceeding 97.5&#x202f;%.<br><br>Compared to deep convolutional methods, the spiking method reduces energy consumption.<br><br>CONCLUSION: At the structural level, complex networks are utilized to establish spatial topology of brain connections and analyse their graph features, identifying potential abnormal brain functional connections in individuals with depression.}, }
@article {pmid38878764, year = {2024}, author = {Wang, J}, title = {Can brain-computer interface promote affected upper limb movements of stroke patients?.}, journal = {Med (New York, N.Y.)}, volume = {5}, number = {6}, pages = {485-486}, doi = {10.1016/j.medj.2024.04.003}, pmid = {38878764}, issn = {2666-6340}, mesh = {Humans ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; Movement/physiology ; Stroke/physiopathology ; Recovery of Function/physiology ; }, abstract = {Brain-computer interface (BCI) systems enable the brain to control limb movement on the side with impaired nerve conduction, showing great potential for promoting functional recovery. Wang et al.[1] demonstrated that BCI combined with functional electrical stimulation improved the motor function scores of stroke patients by conducting a multicenter RCT study.}, }
@article {pmid38876320, year = {2024}, author = {Rao, Y and Zhang, L and Jing, R and Huo, J and Yan, K and He, J and Hou, X and Mu, J and Geng, W and Cui, H and Hao, Z and Zan, X and Ma, J and Chou, X}, title = {An optimized EEGNet decoder for decoding motor image of four class fingers flexion.}, journal = {Brain research}, volume = {1841}, number = {}, pages = {149085}, doi = {10.1016/j.brainres.2024.149085}, pmid = {38876320}, issn = {1872-6240}, mesh = {Humans ; *Fingers/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; Male ; *Neural Networks, Computer ; Female ; Young Adult ; Movement/physiology ; Brain/physiology ; }, abstract = {As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.}, }
@article {pmid38876042, year = {2024}, author = {Zhang, C and Jian, L and Li, X and Guo, W and Deng, W and Hu, X and Li, T}, title = {Mendelian randomization analysis of the brain, cerebrospinal fluid, and plasma proteome identifies potential drug targets for attention deficit hyperactivity disorder.}, journal = {EBioMedicine}, volume = {105}, number = {}, pages = {105197}, pmid = {38876042}, issn = {2352-3964}, mesh = {Humans ; *Attention Deficit Disorder with Hyperactivity/genetics/cerebrospinal fluid/blood ; *Mendelian Randomization Analysis ; *Proteome/metabolism ; *Brain/metabolism/pathology ; *Biomarkers/cerebrospinal fluid ; Bayes Theorem ; Blood Proteins/metabolism ; Genetic Predisposition to Disease ; }, abstract = {BACKGROUND: The need for new therapeutics for attention deficit hyperactivity disorder (ADHD) is evident. Brain, cerebrospinal fluid (CSF), and plasma protein biomarkers with causal genetic evidence could represent potential drug targets. However, a comprehensive screen of the proteome has not yet been conducted.<br><br>METHODS: We employed a three-pronged approach using Mendelian Randomization (MR) and Bayesian colocalization analysis. Firstly, we studied 608 brains, 214 CSF, and 612 plasma proteins as potential causal mediators of ADHD using MR analysis. Secondly, we analysed the consistency of the discovered biomarkers across three distinct subtypes of ADHD: childhood, persistent, and late-diagnosed ADHD. Finally, we extended our analysis to examine the correlation between identified biomarkers and Tourette syndrome and pervasive autism spectrum disorder (ASD), conditions often linked with ADHD. To validate the MR findings, we conducted sensitivity analysis. Additionally, we performed cell type analysis on the human brain to identify risk genes that are notably enriched in various brain cell types.<br><br>FINDINGS: After applying Bonferroni correction, we found that the risk of ADHD was increased by brain proteins GMPPB, NAA80, HYI, CISD2, and HYI, TIE1 in CSF and plasma. Proteins GMPPB, NAA80, ICA1L, CISD2, TIE1, and RMDN1 showed overlapped loci with ADHD risk through Bayesian colocalization. Overexpression of GMPPB protein was linked to an increase in the risk for all three ADHD subtypes. While ICA1L provided protection against both ASD and ADHD, CISD2 increased the probability of both disorders. Cell-specific studies revealed that GMPPB, NAA80, ICA1L, and CISD2 were predominantly present on the surface of excitatory-inhibitory neurons.<br><br>INTERPRETATION: Our comprehensive MR investigation of the brain, CSF, and plasma proteomes revealed seven proteins with causal connections to ADHD. Particularly, GMPPB and TIE1 emerged as intriguing targets for potential ADHD therapy.<br><br>FUNDING: This work was partly funded by the Key R &amp; D Program of Zhejiang (T.L. 2022C03096); the National Natural Science Foundation of China Project (C.Z. 82001413); Postdoctoral Foundation of West China Hospital (C.Z. 2020HXBH163).}, }
@article {pmid38875116, year = {2024}, author = {Zhou, S and Duan, S and Yang, H}, title = {Protocol for fiber photometry recording from deep brain regions in head-fixed mice.}, journal = {STAR protocols}, volume = {5}, number = {2}, pages = {103131}, pmid = {38875116}, issn = {2666-1667}, mesh = {Animals ; Mice ; *Photometry/methods ; *Brain/diagnostic imaging ; Optical Fibers ; Calcium/metabolism/analysis ; Stereotaxic Techniques ; Fiber Optic Technology/methods ; }, abstract = {To exclude the influence of motion on in vivo calcium imaging, animals usually need to be fixed. However, the whole-body restraint can cause stress in animals, affecting experimental results. In addition, some brain regions are prone to bleeding during surgery, which lowers the success rate of calcium imaging. Here, we present a protocol for calcium imaging using heparin-treated fiber in head-fixed mice. We describe steps for stereotaxic surgery, including virus injection and optic fiber implantation, fiber photometry, and data analysis. For complete details on the use and execution of this protocol, please refer to Du et al.[1].}, }
@article {pmid38874845, year = {2024}, author = {Birbaumer, N}, title = {"Your Thoughts are (were) Free!": Brain-Computer-Interfaces, Neurofeedback, Detection of Deception, and the Future of Mind-Reading.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {38874845}, issn = {1573-3270}, abstract = {This review describes the historical developement and rationale of clinically relevant research on neurophysiological "mind reading" paradims: Brain- Computer-Interfaces, detection of deception, brain stimulation and neurofeedback and the clinical applications in drug resistant epilepsy, chronic stroke, and communication with paralyzed locked-in persons. The emphasis lies on completely locked-in patients with amyotrophic lateral sclerosis using non-invasive and invasive brain computer interfaces and neurofeedback to restore verbal communication with the social environment. In the second part of the article we argue that success and failure of neurophysiological "mind reading" paradigms may be explained with a motor theory of thinking and emotion in combination with learning theory. The ethical implications of brain computer interface and neurofeedback approaches, particularly for severe chronic paralysis and loss of communication diseases and decisions on hastened death and euthanasia are discussed.}, }
@article {pmid38873653, year = {2024}, author = {Alasfour, A and Gilja, V}, title = {Consistent spectro-spatial features of human ECoG successfully decode naturalistic behavioral states.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1388267}, pmid = {38873653}, issn = {1662-5161}, abstract = {OBJECTIVE: Understanding the neural correlates of naturalistic behavior is critical for extending and confirming the results obtained from trial-based experiments and designing generalizable brain-computer interfaces that can operate outside laboratory environments. In this study, we aimed to pinpoint consistent spectro-spatial features of neural activity in humans that can discriminate between naturalistic behavioral states.<br><br>APPROACH: We analyzed data from five participants using electrocorticography (ECoG) with broad spatial coverage. Spontaneous and naturalistic behaviors such as "Talking" and "Watching TV" were labeled from manually annotated videos. Linear discriminant analysis (LDA) was used to classify the two behavioral states. The parameters learned from the LDA were then used to determine whether the neural signatures driving classification performance are consistent across the participants.<br><br>MAIN RESULTS: Spectro-spatial feature values were consistently discriminative between the two labeled behavioral states across participants. Mainly, &#x3b8;, &#x3b1;, and low and high &#x3b3; in the postcentral gyrus, precentral gyrus, and temporal lobe showed significant classification performance and feature consistency across participants. Subject-specific performance exceeded 70%. Combining neural activity from multiple cortical regions generally does not improve decoding performance, suggesting that information regarding the behavioral state is non-additive as a function of the cortical region.<br><br>SIGNIFICANCE: To the best of our knowledge, this is the first attempt to identify specific spectro-spatial neural correlates that consistently decode naturalistic and active behavioral states. The aim of this work is to serve as an initial starting point for developing brain-computer interfaces that can be generalized in a realistic setting and to further our understanding of the neural correlates of naturalistic behavior in humans.}, }
@article {pmid38873652, year = {2024}, author = {Kleih, SC and Botrel, L}, title = {Post-stroke aphasia rehabilitation using an adapted visual P300 brain-computer interface training: improvement over time, but specificity remains undetermined.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1400336}, pmid = {38873652}, issn = {1662-5161}, abstract = {INTRODUCTION: This study aimed to evaluate the efficacy of visual P300 brain-computer interface use to support rehabilitation of chronic language production deficits commonly experienced by individuals with a left-sided stroke resulting in post-stroke aphasia.<br><br>METHODS: The study involved twelve participants, but five dropped out. Additionally, data points were missing for three participants in the remaining sample of seven participants. The participants underwent four assessments-a baseline, pre-assessment, post-assessment, and follow-up assessment. Between the pre-and post-assessment, the participants underwent at least 14 sessions of visual spelling using a brain-computer interface. The study aimed to investigate the impact of this intervention on attention, language production, and language comprehension and to determine whether there were any potential effects on quality of life and well-being.<br><br>RESULTS: None of the participants showed a consistent improvement in attention. All participants showed an improvement in spontaneous speech production, and three participants experienced a reduction in aphasia severity. We found an improvement in subjective quality of life and daily functioning. However, we cannot rule out the possibility of unspecific effects causing or at least contributing to these results.<br><br>CONCLUSION: Due to challenges in assessing the patient population, resulting in a small sample size and missing data points, the results of using visual P300 brain-computer interfaces for chronic post-stroke aphasia rehabilitation are preliminary. Thus, we cannot decisively judge the potential of this approach.}, }
@article {pmid38872939, year = {2024}, author = {Zhuo, F and Zhang, X and Tang, F and Yu, Y and Liu, L}, title = {Riemannian transfer learning based on log-Euclidean metric for EEG classification.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1381572}, pmid = {38872939}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios.<br><br>METHODS: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject.<br><br>RESULTS: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient.<br><br>DISCUSSION: With the help of proposed transfer learning method, the Riemannian classifier obtained competitive performance to existing methods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios.}, }
@article {pmid38872529, year = {2024}, author = {Takeuchi, N}, title = {A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {21}, number = {4}, pages = {5118-5137}, doi = {10.3934/mbe.2024226}, pmid = {38872529}, issn = {1551-0018}, mesh = {Humans ; *Brain/physiology ; *Neurosciences/methods ; Interpersonal Relations ; Social Interaction ; Transcranial Magnetic Stimulation/methods ; Neuronal Plasticity ; Psychotherapy/methods ; Neuroimaging/methods ; Social Behavior ; Brain-Computer Interfaces ; }, abstract = {Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning-a neuroimaging technique that simultaneously measures the activity of multiple brain regions-has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.}, }
@article {pmid38872209, year = {2024}, author = {Phang, CR and Su, KH and Cheng, YY and Chen, CH and Ko, LW}, title = {Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {101}, pmid = {38872209}, issn = {1743-0003}, mesh = {Humans ; Male ; *Stroke/physiopathology/complications ; *Stroke Rehabilitation ; Female ; Middle Aged ; *Gait/physiology ; *Electroencephalography ; *Parietal Lobe/physiopathology/physiology ; Evoked Potentials, Motor/physiology ; Frontal Lobe/physiopathology/physiology ; Aged ; Adult ; Motor Cortex/physiopathology/physiology ; Support Vector Machine ; }, abstract = {BACKGROUND: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.<br><br>METHODS: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.<br><br>RESULTS: Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.<br><br>CONCLUSIONS: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.}, }
@article {pmid38872109, year = {2024}, author = {Biswas, P and Dodakian, L and Wang, PT and Johnson, CA and See, J and Chan, V and Chou, C and Lazouras, W and McKenzie, AL and Reinkensmeyer, DJ and Nguyen, DV and Cramer, SC and Do, AH and Nenadic, Z}, title = {A single-center, assessor-blinded, randomized controlled clinical trial to test the safety and efficacy of a novel brain-computer interface controlled functional electrical stimulation (BCI-FES) intervention for gait rehabilitation in the chronic stroke population.}, journal = {BMC neurology}, volume = {24}, number = {1}, pages = {200}, pmid = {38872109}, issn = {1471-2377}, support = {UL1 TR001414/TR/NCATS NIH HHS/United States ; 5R01HD095457//National Institutes of Health, United States/ ; }, mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Chronic Disease ; *Electric Stimulation Therapy/methods ; Gait/physiology ; *Gait Disorders, Neurologic/rehabilitation/etiology ; Single-Blind Method ; Stroke/complications/physiopathology ; *Stroke Rehabilitation/methods ; Treatment Outcome ; }, abstract = {BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies.<br><br>METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (>&#x2009;6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally.<br><br>DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial.<br><br>TRIAL REGISTRATION: The trial was registered as "BCI-FES Therapy for Stroke Rehabilitation" on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.}, }
@article {pmid38871468, year = {2024}, author = {,  and , }, title = {[Chinese expert consensus on implementation and management of brain-computer interface clinical research in neurological diseases].}, journal = {Zhonghua yi xue za zhi}, volume = {104}, number = {23}, pages = {2105-2112}, doi = {10.3760/cma.j.cn112137-20240326-00690}, pmid = {38871468}, issn = {0376-2491}, support = {2021YFC2501100//National Key Research and Development Program of China/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Nervous System Diseases/therapy ; Consensus ; Delphi Technique ; China ; }, abstract = {The development of brain-computer interface (BCI) technology and its preliminary research results show great clinical application prospects. In particular, the application of BCI technologyis a hot research topic in the field of nervous system diseases at present, but the current BCI technology is still in the stage of experimental exploration, needing systematic and standardized clinical research validation. For this purpose, the consensus is led by the Society of Neurosurgery of Chinese Medical Association and Society of Cerebrovascular Surgery of Chinese Stroke Association. Based on the in-depth discussion of multidisciplinary experts and the vote of the Delphi Method, the guidelines and principles are proposed for pre-clinical qualification review, clinical research implementation and management, and long-term effect tracking and evaluation, so as to standardize research ethics and clinical research procedures and further promote the extensive application and in-depth development of BCI technology in the treatment of nervous system diseases.}, }
@article {pmid38871467, year = {2024}, author = {Zhao, JZ}, title = {[The role and responsibility of clinical neuroscience in brain-computer interface clinical research].}, journal = {Zhonghua yi xue za zhi}, volume = {104}, number = {23}, pages = {2102-2104}, doi = {10.3760/cma.j.cn112137-20240410-00837}, pmid = {38871467}, issn = {0376-2491}, support = {2021YFC2501100//National Key Research and Development Program of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Neurosciences ; Biomedical Research ; Nervous System Diseases/therapy ; Electroencephalography ; Brain/physiology ; }, abstract = {Brain-computer interface (BCI) constructs the direct communication between human brain and external devices, which has been extensively applied in clinical research of the diagnosis and treatment of nervous system diseases, and plays a crucial role in functional evaluation, communication control, reconstruction, rehabilitation training, and neural regulation. This paper summarizes the different forms of BCI technology, the progress of clinical research on BCI for neurological diseases, and the importance of developing appropriate clinical trial standards and ethical norms. Further emphasis is placed on the interpretation of the scope of BCI clinical research in neurological diseases, the ethical principles of BCI clinical research in neurological diseases, and the key points of BCI clinical research implementation and management summarized in the Chinese expert Consensus on the implementation and management of BCI clinical research in Neurological diseases. Finally, the role and responsibility of clinical neuroscience in BCI clinical trials are put forward, emphasizing interdisciplinary cooperation in clinical research to better promote the clinical transformation of BCI technology.}, }
@article {pmid38869771, year = {2024}, author = {Telli, ML and Litton, JK and Beck, JT and Jones, JM and Andersen, J and Mina, LA and Brig, R and Danso, M and Yuan, Y and Symmans, WF and Hopkins, JF and Albacker, LA and Abbattista, A and Noonan, K and Mata, M and Laird, AD and Blum, JL}, title = {Neoadjuvant talazoparib in patients with germline BRCA1/2 mutation-positive, early-stage triple-negative breast cancer: exploration of tumor BRCA mutational status.}, journal = {Breast cancer (Tokyo, Japan)}, volume = {31}, number = {5}, pages = {886-897}, pmid = {38869771}, issn = {1880-4233}, mesh = {Adult ; Aged ; Female ; Humans ; Middle Aged ; Biomarkers, Tumor/genetics ; *BRCA1 Protein/genetics ; *BRCA2 Protein/genetics ; *Germ-Line Mutation ; Loss of Heterozygosity ; *Neoadjuvant Therapy/methods ; *Phthalazines/therapeutic use/administration &amp; dosage ; Retrospective Studies ; *Triple Negative Breast Neoplasms/genetics/drug therapy/pathology ; }, abstract = {BACKGROUND: Talazoparib monotherapy in patients with germline BRCA-mutated, early-stage triple-negative breast cancer (TNBC) showed activity in the neoadjuvant setting in the phase II NEOTALA study (NCT03499353). These biomarker analyses further assessed the mutational landscape of the patients enrolled in the NEOTALA study.<br><br>METHODS: Baseline tumor tissue from the NEOTALA study was tested retrospectively using FoundationOne[&#xae;]CDx. To further hypothesis-driven correlative analyses, agnostic heat-map visualizations of the FoundationOne[&#xae;]CDx tumor dataset were used to assess overall mutational landscape and identify additional candidate predictive biomarkers of response.<br><br>RESULTS: All patients enrolled (N&#x2009;=&#x2009;61) had TNBC. In the biomarker analysis population, 75.0% (39/52) and 25.0% (13/52) of patients exhibited BRCA1 and BRCA2 mutations, respectively. Strong concordance (97.8%) was observed between tumor BRCA and germline BRCA mutations, and 90.5% (38/42) of patients with tumor BRCA mutations evaluable for somatic-germline-zygosity were predicted to exhibit BRCA loss of heterozygosity (LOH). No patients had non-BRCA germline DNA damage response (DDR) gene variants with known/likely pathogenicity, based on a panel of 14 non-BRCA DDR genes. Ninety-eight percent of patients had TP53 mutations. Genomic LOH, assessed continuously or categorically, was not associated with response.<br><br>CONCLUSION: The results from this exploratory biomarker analysis support the central role of BRCA and TP53 mutations in tumor pathobiology. Furthermore, these data support assessing germline BRCA mutational status for molecular eligibility for talazoparib in patients with TNBC.}, }
@article {pmid38869769, year = {2024}, author = {Zhu, CH and Yu, JY and Ma, Y and Dong, Y and Wu, ZY}, title = {Progressive Ataxia due to de novo Missense Variants in the CACNA1A Gene.}, journal = {Cerebellum (London, England)}, volume = {23}, number = {5}, pages = {2197-2204}, pmid = {38869769}, issn = {1473-4230}, support = {82230062//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Mutation, Missense ; Male ; Female ; *Calcium Channels/genetics ; Middle Aged ; Ataxia/genetics ; Adult ; }, abstract = {The CACNA1A gene encodes the alpha-1A subunit of P/Q type voltage-gated calcium channel Cav2.1, which is associated with a broad clinical spectrum and variable symptomatology. While few patients with progressive ataxia caused by CACNA1A missense variants have been reported, here we report three unrelated Chinese patients with progressive ataxia due to de novo missense variants in the CACNA1A gene, including a novel pathogenic variant (c.4999C > G) and a previously reported pathogenic variant (c.4037G > A). Our findings and a systematic literature review show the unique phenotype of progressive ataxia caused by missense variants and enlarge the genetic and clinical spectrum of CACNA1A. This suggests that in addition to routine screening for dynamic mutations, screening for CACNA1A variants is important for clinicians facing patients with progressive ataxia.}, }
@article {pmid38869147, year = {2024}, author = {Zhang, C and Yang, Z and Li, X and Zhao, L and Guo, W and Deng, W and Wang, Q and Hu, X and Li, M and Sham, PC and Xiao, X and Li, T}, title = {Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach.}, journal = {Schizophrenia bulletin}, volume = {50}, number = {5}, pages = {1185-1196}, pmid = {38869147}, issn = {1745-1701}, support = {R37 MH057881/MH/NIMH NIH HHS/United States ; R01 MH075916/MH/NIMH NIH HHS/United States ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; 2020HXBH163//Postdoctoral Foundation of West China Hospital/ ; 2022SCP001//Spring City Plan: the High-level Talent Promotion and Training Project of Kunming/ ; P50 MH084053/MH/NIMH NIH HHS/United States ; HHSN271201300031C/DA/NIDA NIH HHS/United States ; 82001413//National Natural Science Foundation of China Project/ ; P50 MH066392/MH/NIMH NIH HHS/United States ; R01 MH080405/MH/NIMH NIH HHS/United States ; 81920108018//National Natural Science Foundation of China Key Project/ ; R01 MH097276/MH/NIMH NIH HHS/United States ; P01 AG002219/AG/NIA NIH HHS/United States ; R01 MH093725/MH/NIMH NIH HHS/United States ; P50 AG005138/AG/NIA NIH HHS/United States ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence and Key Project for Hangzhou Medical Disciplines/ ; R01 MH085542/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; Humans ; *Bipolar Disorder/genetics/metabolism ; Brain/metabolism ; *Genome-Wide Association Study ; Mendelian Randomization Analysis ; *NIMA-Related Kinases/genetics/metabolism ; Proteome ; *Proteomics ; Quantitative Trait Loci ; *Schizophrenia/genetics/metabolism ; }, abstract = {BACKGROUND AND HYPOTHESIS: Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets.<br><br>STUDY DESIGN: To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined.<br><br>STUDY RESULTS: Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons.<br><br>CONCLUSIONS: These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.}, }
@article {pmid38866001, year = {2024}, author = {Jin, X and Yang, X and Kong, W and Zhu, L and Tang, J and Peng, Y and Ding, Y and Zhao, Q}, title = {TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad5761}, pmid = {38866001}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Biometric Identification/methods ; Male ; Attention/physiology ; Female ; Neural Networks, Computer ; Adult ; Young Adult ; }, abstract = {Objective.Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected.Approach.In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions.Main results.Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation.Significance.The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10-20 standard system are able to extract stable identity information.}, }
@article {pmid38865781, year = {2024}, author = {Pfeffer, MA and Ling, SSH and Wong, JKW}, title = {Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces.}, journal = {Computers in biology and medicine}, volume = {178}, number = {}, pages = {108705}, doi = {10.1016/j.compbiomed.2024.108705}, pmid = {38865781}, issn = {1879-0534}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems.}, }
@article {pmid38862476, year = {2024}, author = {Kosnoff, J and Yu, K and Liu, C and He, B}, title = {Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {4382}, pmid = {38862476}, issn = {2041-1723}, support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; DGE2140739//National Science Foundation (NSF)/ ; R01NS124564, RF1NS131069, R01NS096761, and NS127849//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; T32 EB029365/EB/NIBIB NIH HHS/United States ; R01AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; RF1 NS131069/NS/NINDS NIH HHS/United States ; T32EB029365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; R01 NS127849/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; U18EB029354, T32EB029365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; *Attention/physiology ; Adult ; Female ; Young Adult ; Visual Cortex/physiology ; Motion Perception/physiology ; Photic Stimulation/methods ; }, abstract = {A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.}, }
@article {pmid38862406, year = {2024}, author = {Wang, XQ and Sun, HQ and Si, JY and Lin, ZY and Zhai, XM and Lu, L}, title = {Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces.}, journal = {Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih}, volume = {39}, number = {2}, pages = {131-139}, doi = {10.24920/004377}, pmid = {38862406}, issn = {1001-9294}, support = {2021ZD0201900//Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201905//Project 5/ ; }, mesh = {Humans ; Biomedical Research/ethics ; *Brain-Computer Interfaces/ethics ; Ethical Review ; }, abstract = {Brain-computer interface (BCI) technology is rapidly advancing in medical research and application. As an emerging biomedical engineering technology, it has garnered significant attention in the clinical research of brain disease diagnosis and treatment, neurological rehabilitation, and mental health. However, BCI also raises several challenges and ethical concerns in clinical research. In this article, the authors investigate and discuss three aspects of BCI in medicine and healthcare: the state of international ethical governance, multidimensional ethical challenges pertaining to BCI in clinical research, and suggestive concerns for ethical review. Despite the great potential of frontier BCI research and development in the field of medical care, the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI. To ensure "responsible innovation" in BCI research in healthcare and medicine, the creation of an ethical global governance framework and system, along with special guidelines for cutting-edge BCI research in medicine, is suggested.}, }
@article {pmid38861961, year = {2024}, author = {Hore, A and Bandyopadhyay, S and Chakrabarti, S}, title = {Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad56c8}, pmid = {38861961}, issn = {1741-2552}, mesh = {*Neural Networks, Computer ; *Action Potentials/physiology ; *Models, Neurological ; *Neurons/physiology ; Humans ; Synapses/physiology ; Computer Simulation ; Neural Inhibition/physiology ; Nerve Net/physiology ; }, abstract = {Objective. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory synapses are designed to establish a connection between two hardware neurons, effectively forming a network. The model demonstrates the occurrence of PIRE under strong inhibitory input. Emphasizing the significance of incorporating PIRE in neuromorphic circuits, the study showcases generation of persistent activity within cyclic and recurrent spiking neuronal networks.Approach. The neuronal and synaptic circuits are designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The operating mechanism of the PIRE phenomenon integrated into a hardware neuron is discussed. The proposed circuit encompasses several parameters for effectively controlling multiple electrophysiological features of a neuron.Main results. The neuronal circuit has been tuned to match the response of a biological neuron. The efficiency of this circuit is evaluated by computing the average power dissipation and energy consumption per spike through simulation. The sustained firing of neural spikes is observed till 1.7 s using the two neuronal networks.Significance. Persistent activity has significant implications for various cognitive functions such as working memory, decision-making, and attention. Therefore, hardware implementation of these functions will require our PIRE-integrated model. Energy-efficient neuromorphic systems are useful in many artificial intelligence applications, including human-machine interaction, IoT devices, autonomous systems, and brain-computer interfaces.}, }
@article {pmid38861953, year = {2024}, author = {Janardhan Reddy, T and Ramasubba Reddy, M}, title = {Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {4}, pages = {}, doi = {10.1088/2057-1976/ad567f}, pmid = {38861953}, issn = {2057-1976}, mesh = {*Evoked Potentials, Visual ; Datasets as Topic ; *Image Processing, Computer-Assisted/methods ; Parietal Lobe/diagnostic imaging ; Occipital Lobe/diagnostic imaging ; Reproducibility of Results ; Humans ; Male ; Female ; Young Adult ; Adult ; Electroencephalography/methods ; *Brain-Computer Interfaces ; }, abstract = {Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.}, }
@article {pmid38857138, year = {2024}, author = {Leng, J and Li, H and Shi, W and Gao, L and Lv, C and Wang, C and Xu, F and Zhang, Y and Jung, TP}, title = {Time-Frequency-Space EEG Decoding Model Based on Dense Graph Convolutional Network for Stroke.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {9}, pages = {5214-5226}, doi = {10.1109/JBHI.2024.3411646}, pmid = {38857138}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Algorithms ; Deep Learning ; Neural Networks, Computer ; Brain/physiopathology ; Male ; Female ; Middle Aged ; Stroke Rehabilitation/methods ; Adult ; }, abstract = {Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals from stroke patients is challenging because of their low signal-to-noise ratio and high variability. Therefore, we propose a novel approach that combines the modified S-transform (MST) and a dense graph convolutional network (DenseGCN) algorithm to enhance the MI-BCI performance across time, frequency, and space domains. MST is a time-frequency analysis method that efficiently concentrates energy in EEG signals, while DenseGCN is a deep learning model that uses EEG feature maps from each layer as inputs for subsequent layers, facilitating feature reuse and hyper-parameters optimization. Our approach outperforms conventional networks, achieving a peak classification accuracy of 90.22% and an average information transfer rate (ITR) of 68.52 bits per minute. Moreover, we conduct an in-depth analysis of the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon in the deep-level EEG features of stroke patients. Our experimental results confirm the feasibility and efficacy of the proposed approach for MI-BCI rehabilitation systems.}, }
@article {pmid38856329, year = {2024}, author = {Ladouceur, F and Al Abed, A and Lehmann, T and Almasri, RM and Firth, J and Lovell, NH}, title = {All optical neural interfaces.}, journal = {Applied optics}, volume = {63}, number = {14}, pages = {D21-D27}, doi = {10.1364/AO.512480}, pmid = {38856329}, issn = {1539-4522}, abstract = {Brain/computer interfaces (BCIs) rely on the concurrent recording of many channels of electrical activity from excitable tissue. Traditionally such neural interfacing has been performed using cumbersome, channel-limited multielectrode arrays. We believe that BCIs can greatly benefit from using an optical approach based on simple yet powerful liquid-crystal based transducer technology. This approach potentially offers a technology platform that can sustain the necessary bandwidth, density of channels, responsivity, and conformability that are required for the long-term viability of such interfaces. In this paper we review the overall architecture of this approach, the challenges it faces, and the solutions that are being developed at UNSW Sydney.}, }
@article {pmid38855665, year = {2024}, author = {Pang, R and Sang, H and Yi, L and Gao, C and Xu, H and Wei, Y and Zhang, L and Sun, J}, title = {Working memory load recognition with deep learning time series classification.}, journal = {Biomedical optics express}, volume = {15}, number = {5}, pages = {2780-2797}, pmid = {38855665}, issn = {2156-7085}, abstract = {Working memory load (WML) is one of the widely applied signals in the areas of human-machine interaction. The precise evaluation of the WML is crucial for this kind of application. This study aims to propose a deep learning (DL) time series classification (TSC) model for inter-subject WML decoding. We used fNIRS to record the hemodynamic signals of 27 participants during visual working memory tasks. Traditional machine learning and deep time series classification algorithms were respectively used for intra-subject and inter-subject WML decoding from the collected blood oxygen signals. The intra-subject classification accuracy of LDA and SVM were 94.6% and 79.1%. Our proposed TAResnet-BiLSTM model had the highest inter-subject WML decoding accuracy, reaching 92.4%. This study provides a new idea and method for the brain-computer interface application of fNIRS in real-time WML detection.}, }
@article {pmid38855230, year = {2024}, author = {Okamoto, Y and Matsui, K and Ando, T and Atsuumi, K and Taniguchi, K and Hirai, H and Nishikawa, A}, title = {Pilot study of the relation between various dynamics of avatar experience and perceptual characteristics.}, journal = {PeerJ. Computer science}, volume = {10}, number = {}, pages = {e2042}, pmid = {38855230}, issn = {2376-5992}, abstract = {In recent years, due to the prevalence of virtual reality (VR) and human-computer interaction (HCI) research, along with the expectation that understanding the process of establishing sense of ownership, sense of agency, and limb heaviness (in this study, limb heaviness is replaced with comfort level) will contribute to the development of various medical rehabilitation, various studies have been actively conducted in these fields. Previous studies have indicated that each perceptual characteristics decrease in response to positive delay. However, it is still unclear how each perceptual characteristic changes in response to negative delay. Therefore, the purpose of this study was to deduce how changes occur in the perceptual characteristics when certain settings are manipulated using the avatar developed in this study. This study conducted experiments using an avatar system developed for this research that uses electromyography as the interface. Two separate experiments involved twelve participants: a preliminary experiment and a main experiment. As observed in the previous study, it was confirmed that each perceptual characteristics decreased for positive delay. In addition, the range of the preliminary experiment was insufficient for the purpose of this study, which was to confirm the perceptual characteristics for negative delay, thus confirming the validity of conducting this experiment. Meanwhile, the main experiment showed that the sense of ownership, sense of agency, and comfort level decreased gradually as delay time decreased, (i.e., this event is prior to action with intention, which could not be examined in the previous study). This suggests that control by the brain-machine interface is difficult to use when it is too fast. In addition, the distribution of the most strongly perceived settings in human perceptual characteristics was wider in regions with larger delays, suggesting this may lead to the evaluation of an internal model believed to exist in the human cerebellum. The avatar developed for this study may have the potential to create a new experimental paradigm for perceptual characteristics.}, }
@article {pmid38853549, year = {2024}, author = {Jiang, Y and Dong, Y and Hu, H}, title = {The N-methyl-d-aspartate receptor hypothesis of ketamine's antidepressant action: evidence and controversies.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1906}, pages = {20230225}, pmid = {38853549}, issn = {1471-2970}, support = {//STI2030 Major Projects/ ; //National Natural Science Foundation of China/ ; //New Cornerstone Science Foundation/ ; }, mesh = {*Ketamine/pharmacology ; *Receptors, N-Methyl-D-Aspartate/metabolism/antagonists &amp; inhibitors ; *Antidepressive Agents/pharmacology/therapeutic use ; Humans ; Animals ; Depression/drug therapy ; }, abstract = {Substantial clinical evidence has unravelled the superior antidepressant efficacy of ketamine: in comparison to traditional antidepressants targeting the monoamine systems, ketamine, as an N-methyl-d-aspartate receptor (NMDAR) antagonist, acts much faster and more potently. Surrounding the antidepressant mechanisms of ketamine, there is ample evidence supporting an NMDAR-antagonism-based hypothesis. However, alternative arguments also exist, mostly derived from the controversial clinical results of other NMDAR inhibitors. In this article, we first summarize the historical development of the NMDAR-centred hypothesis of rapid antidepressants. We then classify different NMDAR inhibitors based on their mechanisms of inhibition and evaluate preclinical as well as clinical evidence of their antidepressant effects. Finally, we critically analyse controversies and arguments surrounding ketamine's NMDAR-dependent and NMDAR-independent antidepressant action. A better understanding of ketamine's molecular targets and antidepressant mechanisms should shed light on the future development of better treatment for depression. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.}, }
@article {pmid38853074, year = {2024}, author = {Pilz da Cunha, G and Coupé, VMH and Zonderhuis, BM and Bonjer, HJ and Erdmann, JI and Kazemier, G and Besselink, MG and Swijnenburg, RJ}, title = {Healthcare cost expenditure for robotic versus laparoscopic liver resection: a bottom-up economic evaluation.}, journal = {HPB : the official journal of the International Hepato Pancreato Biliary Association}, volume = {26}, number = {8}, pages = {971-980}, doi = {10.1016/j.hpb.2024.05.017}, pmid = {38853074}, issn = {1477-2574}, mesh = {Humans ; *Laparoscopy/economics ; *Hepatectomy/economics ; *Robotic Surgical Procedures/economics ; Male ; Retrospective Studies ; Female ; Middle Aged ; Aged ; *Health Expenditures ; Hospital Costs ; Cost-Benefit Analysis ; Operative Time ; Health Care Costs ; Treatment Outcome ; Time Factors ; }, abstract = {BACKGROUND: Minimally invasive liver surgery (MILS) is increasingly performed via the robot-assisted approach but may be associated with increased costs. This study is a post-hoc comparison of healthcare cost expenditure for robotic liver resection (RLR) and laparoscopic liver resection (LLR) in a high-volume center.<br><br>METHODS: In-hospital and 30-day postoperative healthcare costs were calculated per patient in a retrospective series (October 2015-December 2022).<br><br>RESULTS: Overall, 298 patients were included (143 RLR and 155 LLR). Benefits of RLR were lower conversion rate (2.8% vs 12.3%, p&#xa0;=&#xa0;0.002), shorter operating time (167&#xa0;min vs 198&#xa0;min, p&#xa0;=&#xa0;0.044), and less blood loss (50&#xa0;mL vs 200&#xa0;mL, p&#xa0;<&#xa0;0.001). Total per-procedure costs of RLR (&#x20ac;10260) and LLR (&#x20ac;9931) were not significantly different (mean difference &#x20ac;329 [95% bootstrapped confidence interval (BCI) &#x20ac;-1179-&#x20ac;2120]). Lower costs with RLR due to shorter surgical and operating room time were offset by higher disposable instrumentation costs resulting in comparable intraoperative costs (&#x20ac;5559 vs &#x20ac;5247, mean difference &#x20ac;312 [95% BCI &#x20ac;-25-&#x20ac;648]). Postoperative costs were similar for RLR (&#x20ac;4701) and LLR (&#x20ac;4684), mean difference &#x20ac;17 [95% BCI &#x20ac;-1357-&#x20ac;1727]. When also considering purchase and maintenance costs, RLR resulted in higher total per-procedure costs.<br><br>DISCUSSION: In a high-volume center, RLR can have similar per-procedure cost expenditure as LLR when disregarding capital investment.}, }
@article {pmid38851750, year = {2024}, author = {Cao, B and Xu, Q and Shi, Y and Zhao, R and Li, H and Zheng, J and Liu, F and Wan, Y and Wei, B}, title = {Pathology of pain and its implications for therapeutic interventions.}, journal = {Signal transduction and targeted therapy}, volume = {9}, number = {1}, pages = {155}, pmid = {38851750}, issn = {2059-3635}, support = {82273231//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073192//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81974166, 31872774, 32171002//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Pain/genetics/pathology ; Pain Management ; Animals ; }, abstract = {Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.}, }
@article {pmid38851575, year = {2024}, author = {McAloon, CI and McAloon, CG and Barrett, D and Tratalos, JA and McGrath, G and Guelbenzu, M and Graham, DA and Kelly, A and O'Keeffe, K and More, SJ}, title = {Estimation of sensitivity and specificity of bulk tank milk PCR and 2 antibody ELISA tests for herd-level diagnosis of Mycoplasma bovis infection using Bayesian latent class analysis.}, journal = {Journal of dairy science}, volume = {107}, number = {10}, pages = {8464-8478}, doi = {10.3168/jds.2023-24590}, pmid = {38851575}, issn = {1525-3198}, mesh = {Animals ; Cattle ; *Mycoplasma bovis ; *Mycoplasma Infections/veterinary/diagnosis ; *Bayes Theorem ; *Enzyme-Linked Immunosorbent Assay/veterinary ; *Latent Class Analysis ; *Milk/microbiology ; *Cattle Diseases/diagnosis ; *Sensitivity and Specificity ; *Polymerase Chain Reaction/veterinary ; Female ; Ireland ; }, abstract = {Mycoplasmosis (due to infection with Mycoplasma bovis) is a serious disease of beef and dairy cattle that can adversely affect health, welfare, and productivity. Mycoplasmosis can lead to a range of often severe, clinical presentations. Mycoplasma bovis infection can present either clinically or subclinically, with the potential for recrudescence of shedding in association with stressful periods. Infection can be maintained within herds because of intermittent shedding. Mycoplasma bovis is recognized as poorly responsive to treatment, which presents a major challenge for control in infected herds. Given this, particular focus is needed on biosecurity measures to prevent introduction into uninfected herds in the first place. A robust and reliable laboratory test for surveillance is important for both herd-level prevention and control. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of 3 diagnostic tests (1 PCR and 2 ELISA tests) on bulk tank milk (BTM), for the herd-level detection of M. bovis using Bayesian latent class analysis (BLCA). In autumn 2018, BTM samples from 11,807 herds, covering the majority of the main dairy regions in Ireland had been submitted to the Department of Agriculture testing laboratory for routine surveillance and were made available for study. A stratified random sample approach was used to select a cohort of herds for testing from this larger sample set. A final study population of 728 herds had BTM samples analyzed using a Bio-X ELISA (ELISA 1), an IDvet ELISA (ELISA 2) and a PCR test. A BLCA was conducted to estimate the Se and Sp of the 3 diagnostic tests applied to BTM for the detection of herd-level infection. An overall latent class analysis was conducted on all herds within a single population (a 3-test, 1-population model). The herds were also split into 2 populations based on herd size (small herds had <82 cattle; a 3-test, 2-population model) and separately into 3 regions in Ireland (Leinster, Munster, and Connacht/Ulster; a 3-test, 3-population model). The latent variable of interest was the herd-level M. bovis infection status. In total, 363/728 (50%) were large herds, 7 (1.0%) were positive on PCR, 88 (12%) positive on ELISA 1, and 406 (56%) positive on ELISA 2. Based on the 2-population model, the Se (95% Bayesian credible interval [BCI] was 0.03 (upper and lower limits: 0.02, 0.05), 0.22 (0.18, 0.27), and 0.94 (0.88, 0.98) for PCR, ELISA 1, and ELISA 2, respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.97 (0.95, 0.99), and 0.92 (0.86, 0.97) for PCR, ELISA 1, and ELISA 2, respectively. The herd-level true prevalence was estimated at 0.43 (BCI 0.35, 0.5) for smaller herds. The true prevalence was estimated at 0.62 (BCI 0.55, 0.69) for larger herds. The true prevalence was estimated at 0.56 (BCI 0.49, 0.463) in the 1-population model. For the 3-population model, the Se (95% BCI) was 0.03 (0.02, 0.05), 0.24 (0.18, 0.29), and 0.95 (0.9, 0.98) for PCR, ELISA 1, and ELISA 2 respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.98 (0.96, 0.99), and 0.88 (0.79, 0.95) for PCR, ELISA 1 and ELISA 2, respectively. The herd-level true prevalence (95% BCI) was estimated at 0.65 (0.56, 0.73), 0.38 (0.28, 0.46), and 0.53 (0.4, 0.65) for populations 1, 2, and 3 respectively. Across all 3 models, the range in true prevalence was 38% to 65% of Irish dairy herds infected with M. bovis. The operating characteristics vary substantially between tests. The IDvet ELISA had a relatively high Se (the highest Se of the 3 tests studied) but it was estimated at 0.95 at its highest in 3-test, 3-population model. This test may be an appropriate test for herd-level screening or prevalence estimation within the context of the endemically infected Irish dairy cattle population. Further work is required to optimize this test and its interpretation when applied at herd-level to offset concerns related to the lower than optimal test Sp.}, }
@article {pmid38850348, year = {2025}, author = {Qian, SX and Bao, YF and Li, XY and Dong, Y and Zhang, XL and Wu, ZY}, title = {Multi-omics Analysis Reveals Key Gut Microbiota and Metabolites Closely Associated with Huntington's Disease.}, journal = {Molecular neurobiology}, volume = {62}, number = {1}, pages = {351-365}, pmid = {38850348}, issn = {1559-1182}, support = {2022KY1249//the Medical and Health Science and Technology Plan of Zhejiang Provincial Health Commission/ ; 2019C03039//Key Research and Development project of Zhejiang Province/ ; }, mesh = {Humans ; *Huntington Disease/microbiology/metabolism ; *Gastrointestinal Microbiome ; Male ; Female ; Middle Aged ; *Metabolomics ; *Feces/microbiology ; Adult ; Metagenomics/methods ; Metabolome ; Aged ; Multiomics ; }, abstract = {Dysbiosis of the gut microbiota is closely associated with neurodegenerative diseases, including Huntington's disease (HD). Gut microbiome-derived metabolites are key factors in host-microbiome interactions. This study aimed to investigate the crucial gut microbiome and metabolites in HD and their correlations. Fecal and serum samples from 11 to 26 patients with HD, respectively, and 16 and 23 healthy controls, respectively, were collected. The fecal samples were used for shotgun metagenomics while the serum samples for metabolomics analysis. Integrated analysis of the metagenomics and metabolomics data was also conducted. Firmicutes, Bacteroidota, Proteobacteria, Uroviricota, Actinobacteria, and Verrucomicrobia were the dominant phyla. At the genus level, the presence of Bacteroides, Faecalibacterium, Parabacteroides, Alistipes, Dialister, and Christensenella was higher in HD patients, while the abundance of Lachnospira, Roseburia, Clostridium, Ruminococcus, Blautia, Butyricicoccus, Agathobaculum, Phocaeicola, Coprococcus, and Fusicatenibacter decreased. A total of 244 differential metabolites were identified and found to be enriched in the glycerophospholipid, nucleotide, biotin, galactose, and alpha-linolenic acid metabolic pathways. The AUC value from the integrated analysis (1) was higher than that from the analysis of the gut microbiota (0.8632). No significant differences were found in the ACE, Simpson, Shannon, Sobs, and Chao indexes between HD patients and controls. Our study determined crucial functional gut microbiota and potential biomarkers associated with HD pathogenesis, providing new insights into the role of the gut microbiota-brain axis in HD occurrence and development.}, }
@article {pmid38849033, year = {2024}, author = {El Khoury, MA and Chartier-Kastler, E and Parra, J and Vaessen, C and Roupret, M and Seisen, T and Lenfant, L}, title = {Continent cutaneous diversion: Unveiling the interplay of neuro-urology and oncological challenges.}, journal = {The French journal of urology}, volume = {34}, number = {6}, pages = {102665}, doi = {10.1016/j.fjurol.2024.102665}, pmid = {38849033}, issn = {2950-3930}, mesh = {Humans ; *Urinary Diversion/methods ; *Urinary Bladder Neoplasms/surgery ; Male ; Female ; *Cystectomy/adverse effects/methods ; Retrospective Studies ; *Quality of Life ; Aged ; Middle Aged ; Urinary Reservoirs, Continent ; Postoperative Complications/epidemiology/etiology ; Treatment Outcome ; }, abstract = {OBJECTIVES: The objective of our study is to demonstrate the practical application of continent cutaneous urinary diversion (CCUD) in oncological patients, with a focus on various aspects of the procedure: surgical challenges, functional outcomes, and quality of life.<br><br>MATERIALS AND METHODS: We studied the perioperative and follow-up data of patients who underwent cystectomy for cancer associated with CCUD (Mitrofanoff, Monti or Casale). We retrospectively analyzed complications within 30days and beyond 30days post-surgery. We evaluated oncological outcomes. Patients' quality of life was assessed using the Bladder Cancer Index (BCI) questionnaire. Results are given on an intention-to-treat basis.<br><br>RESULTS: A total of 24 patients were included in the study (July 2001 and May 2022), with a median follow-up of 62.5months. We report three deaths due to neoplasic recurrence. Forty-six percent had an early postoperative complication, two of whom required revision surgery. Overall, the medium-term complication rate was 70% and the reoperation rate was 62%. There were 8 stomal cutaneous stenoses (33%) and 3 uretero-ileal stenoses (12.5%). Overall satisfaction was rated at 9.2/10 on average, and body image was unaltered or slightly altered in 62.5% of patients. Of the patients who responded to the BCI, 75% had complete continence.<br><br>DISCUSSION: The experience gained with continent stomas in neuro-urology has allowed, in carefully selected cases, to offer patients an alternative that can improve their quality of life in a context already burdened by the shadow of cancer. CCUD can be proposed as an alternative to Bricker diversion in cases of urethral invasion or a high risk of neobladder incontinence, in selected patients.}, }
@article {pmid38848710, year = {2024}, author = {Zhao, X and Xu, R and Xu, R and Wang, X and Cichocki, A and Jin, J}, title = {An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.}, journal = {Journal of neural engineering}, volume = {21}, number = {4}, pages = {}, doi = {10.1088/1741-2552/ad558a}, pmid = {38848710}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Evoked Potentials/physiology ; Male ; Adult ; Female ; Young Adult ; Time Factors ; }, abstract = {Objective.Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs.Approach.This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signedR-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling.Main results.We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity.Significance.These results indicate that AWDSNet has great potential for applications in ERP decoding.}, }
@article {pmid38846893, year = {2024}, author = {Kantawala, B and Emir Hamitoglu, A and Nohra, L and Abdullahi Yusuf, H and Jonathan Isaac, K and Shariff, S and Nazir, A and Soju, K and Yenkoyan, K and Wojtara, M and Uwishema, O}, title = {Microengineered neuronal networks: enhancing brain-machine interfaces.}, journal = {Annals of medicine and surgery (2012)}, volume = {86}, number = {6}, pages = {3535-3542}, pmid = {38846893}, issn = {2049-0801}, abstract = {The brain-machine interface (BMI), a crucial conduit between the human brain and computers, holds transformative potential for various applications in neuroscience. This manuscript explores the role of micro-engineered neuronal networks (MNNs) in advancing BMI technologies and their therapeutic applications. As the interdisciplinary collaboration intensifies, the need for innovative and user-friendly BMI technologies becomes paramount. A comprehensive literature review sourced from reputable databases (PubMed Central, Medline, EBSCOhost, and Google Scholar) aided in the foundation of the manuscript, emphasizing the pivotal role of MNNs. This study aims to synthesize and analyze the diverse facets of MNNs in the context of BMI technologies, contributing insights into neural processes, technological advancements, therapeutic potentials, and ethical considerations surrounding BMIs. MNNs, exemplified by dual-mode neural microelectrodes, offer a controlled platform for understanding complex neural processes. Through case studies, we showcase the pivotal role of MNNs in BMI innovation, addressing challenges, and paving the way for therapeutic applications. The integration of MNNs with BMI technologies marks a revolutionary stride in neuroscience, refining brain-computer interactions and offering therapeutic avenues for neurological disorders. Challenges, ethical considerations, and future trends in BMI research necessitate a balanced approach, leveraging interdisciplinary collaboration to ensure responsible and ethical advancements. Embracing the potential of MNNs is paramount for the betterment of individuals with neurological conditions and the broader community.}, }
@article {pmid38846633, year = {2023}, author = {Saito, Y and Kamagata, K and Akashi, T and Wada, A and Shimoji, K and Hori, M and Kuwabara, M and Kanai, R and Aoki, S}, title = {Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications.}, journal = {Juntendo Iji zasshi = Juntendo medical journal}, volume = {69}, number = {4}, pages = {319-326}, pmid = {38846633}, issn = {2188-2126}, abstract = {Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices. They can extend a person's degree of freedom by either strengthening or substituting the human peripheral working capacity. Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI. This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.}, }
@article {pmid38844171, year = {2024}, author = {Guo, X and Chen, Y and Huang, H and Liu, Y and Kong, L and Chen, L and Lyu, H and Gao, T and Lai, J and Zhang, D and Hu, S}, title = {Serum signature of antibodies to Toxoplasma gondii, rubella virus, and cytomegalovirus in females with bipolar disorder: A cross-sectional study.}, journal = {Journal of affective disorders}, volume = {361}, number = {}, pages = {82-90}, doi = {10.1016/j.jad.2024.06.014}, pmid = {38844171}, issn = {1573-2517}, mesh = {Humans ; *Bipolar Disorder/immunology/blood ; Female ; *Toxoplasma/immunology ; Adult ; *Rubella virus/immunology ; *Cytomegalovirus/immunology ; Cross-Sectional Studies ; *Antibodies, Viral/blood ; Young Adult ; *Immunoglobulin G/blood ; *Antibodies, Protozoan/blood ; Adolescent ; Middle Aged ; *Immunoglobulin M/blood ; Child ; Toxoplasmosis/immunology/blood ; Rubella/immunology ; Cytomegalovirus Infections/immunology ; }, abstract = {BACKGROUND AND AIM: Immunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association.<br><br>METHODS: Antibody seropositivity of IgM and IgG to T gondii, rubella virus, and CMV of females with BD and controls was extracted based on medical records from January 2018 to January 2023. Family history, type of BD, onset age, and psychotic symptom history were also collected.<br><br>RESULTS: 585 individuals with BD and 800 healthy controls were involved. Individuals with BD revealed a lower positive rate of T gondii IgG in the 10-20 aged group (OR&#xa0;=&#xa0;0.10), and a higher positive rate of rubella IgG in the 10-20 (OR&#xa0;=&#xa0;5.44) and 20-30 aged group (OR&#xa0;=&#xa0;3.15). BD with family history preferred a higher positive rate of T gondii IgG (OR&#xa0;=&#xa0;24.00). Type-I BD owned a decreased positive rate of rubella IgG (OR&#xa0;=&#xa0;0.37) and an elevated positive rate of CMV IgG (OR&#xa0;=&#xa0;2.12) compared to type-II BD, while BD with early onset showed contrast results compared to BD without early onset (Rubella IgG, OR&#xa0;=&#xa0;2.54; CMV IgG, OR&#xa0;=&#xa0;0.26). BD with psychotic symptom history displayed a lower positive rate of rubella IgG (OR&#xa0;=&#xa0;0.50).<br><br>LIMITATIONS: Absence of male evidence and control of socioeconomic status and environmental exposure.<br><br>CONCLUSIONS: Differential antibody seropositive rates of T gondii, rubella, and cytomegalovirus in BD were observed.}, }
@article {pmid38843055, year = {2024}, author = {Kilmarx, J and Tashev, I and Millan, JDR and Sulzer, J and Lewis-Peacock, J}, title = {Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2209-2219}, pmid = {38843055}, issn = {1558-0210}, support = {R01 EY028746/EY/NEI NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; Male ; Female ; *Feasibility Studies ; *Visual Perception/physiology ; Adult ; Young Adult ; Memory, Short-Term/physiology ; Photic Stimulation ; Algorithms ; Cues ; }, abstract = {Visual imagery, or the mental simulation of visual information from memory, could serve as an effective control paradigm for a brain-computer interface (BCI) due to its ability to directly convey the user's intention with many natural ways of envisioning an intended action. However, multiple initial investigations into using visual imagery as a BCI control strategies have been unable to fully evaluate the capabilities of true spontaneous visual mental imagery. One major limitation in these prior works is that the target image is typically displayed immediately preceding the imagery period. This paradigm does not capture spontaneous mental imagery as would be necessary in an actual BCI application but something more akin to short-term retention in visual working memory. Results from the present study show that short-term visual imagery following the presentation of a specific target image provides a stronger, more easily classifiable neural signature in EEG than spontaneous visual imagery from long-term memory following an auditory cue for the image. We also show that short-term visual imagery and visual perception share commonalities in the most predictive electrodes and spectral features. However, visual imagery received greater influence from frontal electrodes whereas perception was mostly confined to occipital electrodes. This suggests that visual perception is primarily driven by sensory information whereas visual imagery has greater contributions from areas associated with memory and attention. This work provides the first direct comparison of short-term and long-term visual imagery tasks and provides greater insight into the feasibility of using visual imagery as a BCI control strategy.}, }
@article {pmid38842111, year = {2024}, author = {Luo, Y and Mu, W and Wang, L and Wang, J and Wang, P and Gan, Z and Zhang, L and Kang, X}, title = {An EEG channel selection method for motor imagery based on Fisher score and local optimization.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad504a}, pmid = {38842111}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Movement/physiology ; }, abstract = {Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.}, }
@article {pmid38841120, year = {2024}, author = {Liu, T and Wu, Y and Ye, A and Cao, L and Cao, Y}, title = {Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1400077}, pmid = {38841120}, issn = {1662-5161}, abstract = {BACKGROUND: Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.<br><br>METHODS: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.<br><br>RESULTS: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.<br><br>CONCLUSION: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.}, }
@article {pmid38837930, year = {2024}, author = {Yu, S and Mao, B and Zhou, Y and Liu, Y and Yi, C and Li, F and Yao, D and Xu, P and San Liang, X and Zhang, T}, title = {Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2187-2197}, doi = {10.1109/TNSRE.2024.3409872}, pmid = {38837930}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Bayes Theorem ; Male ; *Imagination/physiology ; *Electroencephalography/methods ; Young Adult ; Adult ; Female ; *Algorithms ; *Nerve Net/physiology ; Hand/physiology ; Cerebral Cortex/physiology ; Functional Laterality/physiology ; Movement/physiology ; }, abstract = {Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.}, }
@article {pmid38837089, year = {2024}, author = {Khondakar, MFK and Sarowar, MH and Chowdhury, MH and Majumder, S and Hossain, MA and Dewan, MAA and Hossain, QD}, title = {A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.}, journal = {Brain informatics}, volume = {11}, number = {1}, pages = {17}, pmid = {38837089}, issn = {2198-4018}, abstract = {Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.}, }
@article {pmid38837017, year = {2024}, author = {Bhavsar, P and Shah, P and Sinha, S and Kumar, D}, title = {Musical Neurofeedback Advancements, Feedback Modalities, and Applications: A Systematic Review.}, journal = {Applied psychophysiology and biofeedback}, volume = {49}, number = {3}, pages = {347-363}, pmid = {38837017}, issn = {1573-3270}, mesh = {Humans ; Auditory Perception/physiology ; Electroencephalography ; *Music Therapy/methods ; *Neurofeedback/methods ; }, abstract = {The field of EEG-Neurofeedback (EEG-NF) training has showcased significant promise in treating various mental disorders, while also emerging as a cognitive enhancer across diverse applications. The core principle of EEG-NF involves consciously guiding the brain in desired directions, necessitating active engagement in neurofeedback (NF) tasks over an extended period. Music listening tasks have proven to be effective stimuli for such training, influencing emotions, mood, and brainwave patterns. This has spurred the development of musical NF systems and training protocols. Despite these advancements, there exists a gap in systematic literature that comprehensively explores and discusses the various modalities of feedback mechanisms, its benefits, and the emerging applications. Addressing this gap, our review article presents a thorough literature survey encompassing studies on musical NF conducted over the past decade. This review highlights the several benefits and applications ranging from neurorehabilitation to therapeutic interventions, stress management, diagnostics of neurological disorders, and sports performance enhancement. While acknowledged for advantages and popularity of musical NF, there is an opportunity for growth in the literature in terms of the need for systematic randomized controlled trials to compare its effectiveness with other modalities across different tasks. Addressing this gap will involve developing standardized methodologies for studying protocols and optimizing parameters, presenting an exciting prospect for advancing the field.}, }
@article {pmid38836741, year = {2024}, author = {Zhang, X and Zhang, S and Wang, R and Wang, Y and Shen, Y and Huang, H}, title = {Impact of Detrusor Muscle Activity on Short-term Prognosis Following 1470 nm Semiconductor Laser Surgery in Elderly Patients with Benign Prostatic Hyperplasia.}, journal = {Alternative therapies in health and medicine}, volume = {}, number = {}, pages = {}, pmid = {38836741}, issn = {1078-6791}, abstract = {OBJECTIVE: To investigate the influence of preoperative detrusor muscle activity on the short-term prognosis of elderly patients diagnosed with benign prostatic hyperplasia (BPH) undergoing 1470 nm semiconductor laser surgery.<br><br>METHODS: A retrospective study was conducted on clinical data from 165 elderly BPH patients who underwent 1470 nm semiconductor laser surgery between May 2019 and April 2023. Patients were stratified based on preoperative urodynamic study findings, specifically their bladder contractility index (BCI). Patients with a BCI &#x2264;100 constituted the detrusor underactivity (DU) group (n=64), while those with a BCI >100 formed the non-DU group (n=101). Surgical parameters, including duration, intraoperative blood loss, postoperative hospital stay, bladder irrigation, and catheterization duration, were compared. Additionally, changes in International Prostate Symptom Score (IPSS), Quality of Life (QOL) score, residual urine volume, and peak urinary flow rate (Qmax) were assessed before and three months after surgery in both groups.<br><br>RESULTS: There were no statistically significant differences observed between the DU and non-DU groups concerning surgical duration, intraoperative blood loss, postoperative hospitalization duration, bladder irrigation duration, and postoperative catheterization duration (P > .05). Similarly, no significant disparities were noted in the IPSS and QOL scores preoperatively and at the three-month follow-up in both groups (P > .05). Both cohorts exhibited no significant differences in residual urine volume before surgery and at the three-month mark postoperatively (P > .05). However, the postoperative Qmax was significantly reduced in the DU group compared to the non-DU group (P < .05).<br><br>CONCLUSIONS: Detrusor muscle activity does not exert a significant impact on clinical symptom improvement and quality of life in elderly BPH patients treated with 1470 nm semiconductor laser surgery. However, patients with DU exhibited inferior postoperative recovery in Qmax, underscoring the importance of preoperative urodynamic studies for early intervention and enhanced surgical outcomes in this patient population.}, }
@article {pmid38835164, year = {2024}, author = {Gangadharan, SK and Ramakrishnan, S and Paek, A and Ravindran, A and Prasad, VA and Vidal, JLC}, title = {Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation.}, journal = {Annals of Indian Academy of Neurology}, volume = {27}, number = {3}, pages = {297-306}, pmid = {38835164}, issn = {0972-2327}, abstract = {OBJECTIVE: Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention.<br><br>METHODS: Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain-computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm.<br><br>RESULTS: Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm.<br><br>CONCLUSIONS: The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.}, }
@article {pmid38834300, year = {2024}, author = {Harlow, TJ and Marquez, SM and Bressler, S and Read, HL}, title = {Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings.}, journal = {eNeuro}, volume = {11}, number = {6}, pages = {}, pmid = {38834300}, issn = {2373-2822}, mesh = {Humans ; *Acoustic Stimulation/methods ; *Evoked Potentials, Auditory/physiology ; Male ; Female ; *Electroencephalography/methods ; *Alpha Rhythm/physiology ; Adult ; Young Adult ; Brain/physiology ; Auditory Perception/physiology ; Algorithms ; Feasibility Studies ; }, abstract = {Following repetitive visual stimulation, post hoc phase analysis finds that visually evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. This approach has not successfully revealed an alpha phase dependence for auditory evoked or induced responses. Here, we test the feasibility of tracking alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound-evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound-evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to the estimated poststimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced post-stimulus alpha level and frequency modulations. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates the feasibility of using individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.}, }
@article {pmid38834062, year = {2024}, author = {Roebben, A and Heintz, N and Geirnaert, S and Francart, T and Bertrand, A}, title = {'Are you even listening?' - EEG-based decoding of absolute auditory attention to natural speech.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad5403}, pmid = {38834062}, issn = {1741-2552}, mesh = {Humans ; *Attention/physiology ; *Electroencephalography/methods ; Female ; Male ; *Speech Perception/physiology ; Adult ; Young Adult ; Acoustic Stimulation/methods ; Auditory Perception/physiology ; }, abstract = {Objective.In this study, we use electroencephalography (EEG) recordings to determine whether a subject is actively listening to a presented speech stimulus. More precisely, we aim to discriminate between an active listening condition, and a distractor condition where subjects focus on an unrelated distractor task while being exposed to a speech stimulus. We refer to this task as absolute auditory attention decoding.Approach.We re-use an existing EEG dataset where the subjects watch a silent movie as a distractor condition, and introduce a new dataset with two distractor conditions (silently reading a text and performing arithmetic exercises). We focus on two EEG features, namely neural envelope tracking (NET) and spectral entropy (SE). Additionally, we investigate whether the detection of such an active listening condition can be combined with a selective auditory attention decoding (sAAD) task, where the goal is to decide to which of multiple competing speakers the subject is attending. The latter is a key task in so-called neuro-steered hearing devices that aim to suppress unattended audio, while preserving the attended speaker.Main results.Contrary to a previous hypothesis of higher SE being related with actively listening rather than passively listening (without any distractors), we find significantly lower SE in the active listening condition compared to the distractor conditions. Nevertheless, the NET is consistently significantly higher when actively listening. Similarly, we show that the accuracy of a sAAD task improves when evaluating the accuracy only on the highest NET segments. However, the reverse is observed when evaluating the accuracy only on the lowest SE segments.Significance.We conclude that the NET is more reliable for decoding absolute auditory attention as it is consistently higher when actively listening, whereas the relation of the SE between actively and passively listening seems to depend on the nature of the distractor.}, }
@article {pmid38834058, year = {2024}, author = {Fang, H and Berman, SA and Wang, Y and Yang, Y}, title = {Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad5406}, pmid = {38834058}, issn = {1741-2552}, mesh = {*Deep Brain Stimulation/methods ; Humans ; *Parkinson Disease/therapy/physiopathology ; *Computer Simulation ; Basal Ganglia/physiopathology/physiology ; Beta Rhythm/physiology ; Models, Neurological ; Cerebral Cortex/physiopathology/physiology ; Thalamus/physiology/physiopathology ; Nonlinear Dynamics ; }, abstract = {Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.}, }
@article {pmid38834056, year = {2024}, author = {Yang, K and Wang, J and Yang, L and Bian, L and Luo, Z and Yang, C}, title = {A diagonal masking self-attention-based multi-scale network for motor imagery classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad5405}, pmid = {38834056}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; *Imagination/physiology ; *Brain-Computer Interfaces ; Humans ; Neural Networks, Computer ; Movement/physiology ; }, abstract = {Objective. Electroencephalography (EEG)-based motor imagery (MI) is a promising paradigm for brain-computer interface (BCI), but the non-stationarity and low signal-to-noise ratio of EEG signals make it a challenging task.Approach. To achieve high-precision MI classification, we propose a Diagonal Masking Self-Attention-based Multi-Scale Network (DMSA-MSNet) to fully develop, extract, and emphasize features from different scales. First, for local features, a multi-scale temporal-spatial block is proposed to extract features from different receptive fields. Second, an adaptive branch fusion block is specifically designed to bridge the semantic gap between these coded features from different scales. Finally, in order to analyze global information over long ranges, a diagonal masking self-attention block is introduced, which highlights the most valuable features in the data.Main results. The proposed DMSA-MSNet outperforms state-of-the-art models on the BCI Competition IV 2a and the BCI Competition IV 2b datasets.Significance. Our study achieves rich information extraction from EEG signals and provides an effective solution for MI classification.}, }
@article {pmid38830946, year = {2024}, author = {Dubiel, M and Barghouti, Y and Kudryavtseva, K and Leiva, LA}, title = {On-device query intent prediction with lightweight LLMs to support ubiquitous conversations.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {12731}, pmid = {38830946}, issn = {2045-2322}, support = {101071147 (SYMBIOTIK)//European Innovation Council Pathfinder program/ ; CHIST-ERA-20-BCI-001//Horizon 2020 FET program/ ; }, abstract = {Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services. To address these problems, we leverage the potential of transfer learning and study how to best fine-tune lightweight pre-trained LLMs to predict the intent of user queries. Importantly, our LLMs allow for on-device deployment, making them suitable for personalised, ubiquitous, and privacy-preserving scenarios. Our experiments suggest that RoBERTa and XLNet offer the best trade-off considering these constraints. We also show that, after fine-tuning, these models perform on par with ChatGPT. We also discuss the implications of this research for relevant stakeholders, including researchers and practitioners. Taken together, this paper provides insights into LLM suitability for on-device CAs and highlights the middle ground between LLM performance and memory footprint while also considering privacy implications.}, }
@article {pmid38829759, year = {2024}, author = {Xing, L and Casson, AJ}, title = {Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {11}, pages = {3111-3122}, doi = {10.1109/TBME.2024.3408331}, pmid = {38829759}, issn = {1558-2531}, mesh = {*Smartphone ; *Electroencephalography/methods/instrumentation ; Humans ; *Signal Processing, Computer-Assisted ; *Artifacts ; *Deep Learning ; *Brain-Computer Interfaces ; Algorithms ; Software ; Neural Networks, Computer ; }, abstract = {OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications.<br><br>METHODS: We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data.<br><br>RESULTS: DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm.<br><br>CONCLUSION: The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration.<br><br>SIGNIFICANCE: This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.}, }
@article {pmid38829754, year = {2024}, author = {Zhou, W and Wu, L and Gao, Y and Chen, X}, title = {A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2114-2123}, doi = {10.1109/TNSRE.2024.3408273}, pmid = {38829754}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Reinforcement, Psychology ; *Bayes Theorem ; *Neural Networks, Computer ; *Electroencephalography/methods ; Discriminant Analysis ; Male ; Adult ; Young Adult ; Female ; Machine Learning ; }, abstract = {Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.}, }
@article {pmid38829709, year = {2024}, author = {Jin, J and Zheng, Q and Liu, H and Feng, K and Bai, Y and Ni, G}, title = {Musical experience enhances time discrimination: Evidence from cortical responses.}, journal = {Annals of the New York Academy of Sciences}, volume = {1536}, number = {1}, pages = {167-176}, doi = {10.1111/nyas.15153}, pmid = {38829709}, issn = {1749-6632}, support = {//the National Natural Science Foundation of China/ ; 2023YFF1203500//the Key Technologies Research and Development Program of China/ ; }, mesh = {Humans ; *Music/psychology ; Male ; *Auditory Perception/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Time Perception/physiology ; Reaction Time/physiology ; Acoustic Stimulation/methods ; Discrimination, Psychological/physiology ; Evoked Potentials, Auditory/physiology ; Brain/physiology ; }, abstract = {Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music-experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music-experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.}, }
@article {pmid38826644, year = {2024}, author = {Kaya, E and Saritas, I}, title = {Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {987-1003}, pmid = {38826644}, issn = {1871-4080}, abstract = {The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.}, }
@article {pmid38826642, year = {2024}, author = {Liu, D and Cui, J and Pan, Z and Zhang, H and Cao, J and Kong, W}, title = {Machine to brain: facial expression recognition using brain machine generative adversarial networks.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {863-875}, pmid = {38826642}, issn = {1871-4080}, abstract = {The human brain can effectively perform Facial Expression Recognition (FER) with a few samples by utilizing its cognitive ability. However, unlike the human brain, even the well-trained deep neural network is data-dependent and lacks cognitive ability. To tackle this challenge, this paper proposes a novel framework, Brain Machine Generative Adversarial Networks (BM-GAN), which utilizes the concept of brain's cognitive ability to guide a Convolutional Neural Network to generate LIKE-electroencephalograph (EEG) features. More specifically, we firstly obtain EEG signals triggered from facial emotion images, then we adopt BM-GAN to carry out the mutual generation of image visual features and EEG cognitive features. BM-GAN intends to use the cognitive knowledge learnt from EEG signals to instruct the model to perceive LIKE-EEG features. Thereby, BM-GAN has a superior performance for FER like the human brain. The proposed model consists of VisualNet, EEGNet, and BM-GAN. More specifically, VisualNet can obtain image visual features from facial emotion images and EEGNet can obtain EEG cognitive features from EEG signals. Subsequently, the BM-GAN completes the mutual generation of image visual features and EEG cognitive features. Finally, the predicted LIKE-EEG features of test images are used for FER. After learning, without the participation of the EEG signals, an average classification accuracy of 96.6 % is obtained on Chinese Facial Affective Picture System dataset using LIKE-EEG features for FER. Experiments demonstrate that the proposed method can produce an excellent performance for FER.}, }
@article {pmid38826067, year = {2024}, author = {Taghi Zadeh Makouei, S and Uyulan, C}, title = {Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.}, journal = {Biomedizinische Technik. Biomedical engineering}, volume = {69}, number = {5}, pages = {501-513}, pmid = {38826067}, issn = {1862-278X}, mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Brain-Computer Interfaces ; *Hand/physiology ; *Movement/physiology ; *Neural Networks, Computer ; Arm/physiology ; Reproducibility of Results ; Spinal Cord Injuries/physiopathology ; }, abstract = {OBJECTIVES: The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI).<br><br>METHODS: The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model's robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept.<br><br>RESULTS: The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75&#x202f;% and low standard deviation (+/- 0.74&#x202f;%) in 10-fold cross-validation, confirming its reliability.<br><br>CONCLUSIONS: In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.}, }
@article {pmid38825665, year = {2024}, author = {Meenakshinathan, J and Gupta, V and Reddy, TK and Behera, L and Sandhan, T}, title = {Session-independent subject-adaptive mental imagery BCI using selective filter-bank adaptive Riemannian features.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {11}, pages = {3293-3310}, pmid = {38825665}, issn = {1741-0444}, support = {FIG-100953//Indian Institute of Technology Roorkee/ ; SER-1968-ECD//Department of Science and Technology, Ministry of Science and Technology, India/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively.}, }
@article {pmid38825023, year = {2024}, author = {He, Y and Ding, Y and Gong, C and Zhou, J and Gong, Z}, title = {The tail segments are required by the performance but not the accomplishment of various modes of Drosophila larval locomotion.}, journal = {Behavioural brain research}, volume = {471}, number = {}, pages = {115074}, doi = {10.1016/j.bbr.2024.115074}, pmid = {38825023}, issn = {1872-7549}, mesh = {Animals ; *Larva/physiology ; *Locomotion/physiology ; *Drosophila/physiology ; *Tail/physiology ; Neurons/physiology ; Animals, Genetically Modified ; Drosophila Proteins/metabolism ; }, abstract = {The tail plays important roles in locomotion control in many animals. But in animals with multiple body segments, the roles of the hind body segments and corresponding innervating neurons in locomotion control are not clear. Here, using the Drosophila larva as the model animal, we investigated the roles of the posterior terminal segments in various modes of locomotion and found that they participate in all of them. In forward crawling, paralysis of the larval tail by blocking the Abdb-Gal4 labeled neurons in the posterior segments of VNC led to a slower locomotion speed but did not prevent the initiation of forward peristalsis. In backward crawling, larvae with the Abdb-Gal4 neurons inhibited were unable to generate effective displacement although waves of backward peristalsis could be initiated and persist. In head swing where the movement of the tail is not obvious, disabling the larval tail by blocking Abdb-Gal4 neurons led to increased bending amplitude upon touching the head. In the case of larval lateral rolling, larval tail paralysis by inhibition of Abdb-Gal4 neurons did not prevent the accomplishment of rolling, but resulted in slower rolling speed. Our work reveals that the contribution of Drosophila larval posterior VNC segments and corresponding body segments in the tail to locomotion is comprehensive but could be compensated at least partially by other body segments. We suggest that the decentralization in locomotion control with respect to animal body parts helps to maintain the robustness of locomotion in multi-segment animals.}, }
@article {pmid38819973, year = {2024}, author = {Guetarni, B and Windal, F and Benhabiles, H and Petit, M and Dubois, R and Leteurtre, E and Collard, D}, title = {A Vision Transformer-Based Framework for Knowledge Transfer From Multi-Modal to Mono-Modal Lymphoma Subtyping Models.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {9}, pages = {5562-5572}, doi = {10.1109/JBHI.2024.3407878}, pmid = {38819973}, issn = {2168-2208}, mesh = {Humans ; *Deep Learning ; *Image Interpretation, Computer-Assisted/methods ; *Lymphoma, Large B-Cell, Diffuse ; Lymphoma ; Algorithms ; }, abstract = {Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly expensive and time-consuming, making it less accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. Whole Slide Image (WSI) analysis using deep learning models has shown promising potential for cancer diagnosis, that could offer cost-effective and faster alternatives to existing methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we introduce a multi-modal architecture to train a classifier model from various WSI modalities. We then leverage this model through a knowledge distillation process to efficiently guide the learning of a mono-modal classifier. Our experimental study conducted on a lymphoma dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent state-of-the-art methods. In addition, the power-law curve, estimated on our experimental data, suggests that with more training data from a reasonable number of additional patients, our model could achieve competitive diagnosis accuracy with IHC technologies. Furthermore, the efficiency of our framework is confirmed through an additional experimental study on an external breast cancer dataset (BCI dataset).}, }
@article {pmid38819673, year = {2024}, author = {Li, M and Yang, L and Liu, Y and Shang, Z and Wan, H}, title = {Dynamic temporal neural patterns based on multichannel LFPs Identify different brain states during anesthesia in pigeons: comparison of three anesthetics.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {11}, pages = {3249-3262}, pmid = {38819673}, issn = {1741-0444}, mesh = {Animals ; *Columbidae/physiology ; *Brain/physiology/drug effects ; *Anesthesia ; Anesthetics/pharmacology ; Urethane/pharmacology ; Male ; Chloral Hydrate/pharmacology ; }, abstract = {Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials (LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon's brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon's brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.}, }
@article {pmid38816665, year = {2024}, author = {Liao, L and Lu, J and Wang, L and Zhang, Y and Gao, D and Wang, M}, title = {CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {10}, pages = {3233-3247}, pmid = {38816665}, issn = {1741-0444}, support = {2023YFG0018//Sichuan Science and Technology Program/ ; KYQN202241//Scientific Research Foundation for Returned Scholars of Ministry of Education/ ; }, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Neural Networks, Computer ; Algorithms ; Brain/diagnostic imaging/physiology ; Brain-Computer Interfaces ; Adult ; Deep Learning ; Male ; Brain Mapping/methods ; Young Adult ; Female ; }, abstract = {Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS classification problem. In this paper, a novel approach based on convolutional neural network and Transformer, named CT-Net, is established to guide the deep modeling for the classification of mental arithmetic (MA) tasks. We explore the effect of data representations, and design a temporal-level combination of two raw chromophore signals to improve the data utilization and enrich the feature learning of the model. We evaluate our model on two open-access datasets and achieve the classification accuracy of 98.05% and 77.61%, respectively. Moreover, we explain our model by the gradient-weighted class activation mapping, which presents a high consistent between the contributing value of features learned by the model and the mapping of brain activity in the MA task. The results suggest the feasibility and interpretability of CT-Net for decoding MA tasks.}, }
@article {pmid38815645, year = {2024}, author = {Li, J and Wang, L and Zhang, Z and Feng, Y and Huang, M and Liang, D}, title = {Analysis and recognition of a novel experimental paradigm for musical emotion brain-computer interface.}, journal = {Brain research}, volume = {1839}, number = {}, pages = {149039}, doi = {10.1016/j.brainres.2024.149039}, pmid = {38815645}, issn = {1872-6240}, mesh = {Humans ; *Music/psychology ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; Adult ; Brain/physiology ; Auditory Perception/physiology ; Acoustic Stimulation/methods ; }, abstract = {Musical emotions have received increasing attention over the years. To better recognize the emotions by brain-computer interface (BCI), the random music-playing and sequential music-playing experimental paradigms are proposed and compared in this paper. Two experimental paradigms consist of three positive pieces, three neutral pieces and three negative pieces of music. Ten subjects participate in two experimental paradigms. The features of electroencephalography (EEG) signals are firstly analyzed in the time, frequency and spatial domains. To improve the effect of emotion recognition, a recognition model is proposed with the optimal channels selecting by Pearson's correlation coefficient, and the feature fusion combining differential entropy and wavelet packet energy. According to the analysis results, the features of sequential music-playing experimental paradigm are more different among three emotions. The classification results of sequential music-playing experimental paradigm are also better, and its average results of positive, neutral and negative emotions are 78.53%, 72.81% and 77.35%, respectively. The more obvious the changes of EEG induced by the emotions, the higher the classification accuracy will be. After analyzing two experimental paradigms, a better way for music to induce the emotions can be explored. Therefore, our research offers a novel perspective on affective BCIs.}, }
@article {pmid38815513, year = {2024}, author = {Becker, ER and Price, AD and Whitrock, JN and Smith, M and Baucom, MR and Makley, AT and Goodman, MD}, title = {Re-evaluating the Use of High Sensitivity Troponin to Diagnose Blunt Cardiac Injury.}, journal = {The Journal of surgical research}, volume = {300}, number = {}, pages = {150-156}, doi = {10.1016/j.jss.2024.04.074}, pmid = {38815513}, issn = {1095-8673}, mesh = {Humans ; Female ; Retrospective Studies ; Male ; Middle Aged ; Adult ; *Wounds, Nonpenetrating/diagnosis/blood ; *Electrocardiography ; Aged ; Heart Injuries/diagnosis/blood ; Troponin I/blood ; Sternum/injuries ; Sensitivity and Specificity ; Biomarkers/blood ; Fractures, Bone/blood/diagnosis ; Echocardiography ; }, abstract = {INTRODUCTION: Blunt cardiac injury (BCI) can be challenging diagnostically, and if misdiagnosed, can lead to life-threatening complications. Our institution previously evaluated BCI screening with troponin and electrocardiogram (EKG) during a transition from troponin I to high sensitivity troponin (hsTnI), a more sensitive troponin I assay. The previous study found an hsTnI of 76 ng/L had the highest capability of accurately diagnosing a clinically significant BCI. The aim of this study was to determine the efficacy of the newly implemented protocol.<br><br>METHODS: Patients diagnosed with a sternal fracture from March 2022 to April 2023 at our urban level-1 trauma center were retrospectively reviewed for EKG findings, hsTnI trend, echocardiogram changes, and clinical outcomes. The BCI cohort and non-BCI cohort ordinal measures were compared using Wilcoxon's two-tailed rank sum test and categorical measures were compared with Fisher's exact test. Youden indices were used to evaluate hsTnI sensitivity and specificity.<br><br>RESULTS: Sternal fractures were identified in 206 patients, of which 183 underwent BCI screening. Of those screened, 103 underwent echocardiogram, 28 were diagnosed with clinically significant BCIs, and 15 received intervention. The peak hsTnI threshold of 76&#xa0;ng/L was found to have a Youden index of 0.31. Rather, the Youden index was highest at 0.50 at 40&#xa0;ng/L (sensitivity 0.79 and specificity 0.71) for clinically significant BCI.<br><br>CONCLUSIONS: Screening patients with sternal fractures for BCI using hsTnI and EKG remains effective. To optimize the hsTnI threshold, this study determined the hsTnI threshold should be lowered to 40&#xa0;ng/L. Further improvements to the institutional protocol may be derived from multicenter analysis.}, }
@article {pmid38813519, year = {2024}, author = {Pan, Y and Zander, TO and Klug, M}, title = {Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1346791}, pmid = {38813519}, issn = {2673-6195}, abstract = {The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.}, }
@article {pmid38812288, year = {2024}, author = {Ji, D and Xiao, X and Wu, J and He, X and Zhang, G and Guo, R and Liu, M and Xu, M and Lin, Q and Jung, TP and Ming, D}, title = {A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad44d8}, pmid = {38812288}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetoencephalography/methods ; *Evoked Potentials, Visual/physiology ; Adult ; Male ; Female ; *Photic Stimulation/methods ; Young Adult ; Visual Cortex/physiology ; }, abstract = {Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min[-1]with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min[-1].Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.}, }
@article {pmid38812257, year = {2024}, author = {Kılınç Bülbül, D and Güçlü, B}, title = {Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays.}, journal = {Somatosensory &amp; motor research}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/08990220.2024.2358522}, pmid = {38812257}, issn = {1369-1651}, abstract = {AIM OF THE STUDY: Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms.<br><br>MATERIALS AND METHODS: 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and &#xb5;ECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel &#xb5;ECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such.<br><br>RESULTS: The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine.<br><br>CONCLUSION: According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.}, }
@article {pmid38812014, year = {2024}, author = {Ma, ZZ and Wu, JJ and Cao, Z and Hua, XY and Zheng, MX and Xing, XX and Ma, J and Xu, JG}, title = {Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {91}, pmid = {38812014}, issn = {1743-0003}, support = {81902301//National Natural Science Foundation of China/ ; 81802249//National Natural Science Foundation of China/ ; 81871836//National Natural Science Foundation of China/ ; 19QA1409000//Shanghai Rising-Star Program/ ; 2018YQ02//Shanghai Municipal Commission of Health and Family Planning/ ; 22010504200//Shanghai Science and Technology Committee/ ; 2018YFC2001600//National Key R&amp;D Program of China/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke Rehabilitation/methods ; Female ; Middle Aged ; *Upper Extremity/physiopathology ; *Magnetic Resonance Imaging ; *Imagery, Psychotherapy/methods ; *Stroke/physiopathology/complications ; Aged ; Adult ; Imagination/physiology ; Cerebral Cortex/diagnostic imaging/physiopathology ; }, abstract = {BACKGROUND: The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain-computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia.<br><br>DESIGN: A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial.<br><br>METHODS: Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures.<br><br>RESULTS: A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r&#x2009;=&#x2009;0.425, P&#x2009;<&#x2009;0.05), the mean zReHo of the right cuneus (r&#x2009;=&#x2009;0.399, P&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients.<br><br>TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry (number ChiCTR2000034848, registered July 21, 2020).}, }
@article {pmid38811613, year = {2024}, author = {Mou, X and He, C and Tan, L and Yu, J and Liang, H and Zhang, J and Tian, Y and Yang, YF and Xu, T and Wang, Q and Cao, M and Chen, Z and Hu, CP and Wang, X and Liu, Q and Wu, H}, title = {ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {550}, pmid = {38811613}, issn = {2052-4463}, support = {2021A1515012509//Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation)/ ; }, mesh = {Humans ; Brain/physiology ; Brain-Computer Interfaces ; China ; *Electroencephalography ; Language ; Linguistics ; Natural Language Processing ; Reading ; *Semantics ; }, abstract = {An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.}, }
@article {pmid38809965, year = {2024}, author = {Rabut, C and Norman, SL and Griggs, WS and Russin, JJ and Jann, K and Christopoulos, V and Liu, C and Andersen, RA and Shapiro, MG}, title = {Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window.}, journal = {Science translational medicine}, volume = {16}, number = {749}, pages = {eadj3143}, doi = {10.1126/scitranslmed.adj3143}, pmid = {38809965}, issn = {1946-6242}, support = {R01 NS123663/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain/diagnostic imaging ; Animals ; *Skull/diagnostic imaging ; Ultrasonography/methods ; Rats ; Acoustics ; Phantoms, Imaging ; Polymethyl Methacrylate/chemistry ; Signal-To-Noise Ratio ; Male ; }, abstract = {Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game "connect the dots" task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 μm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.}, }
@article {pmid38809723, year = {2025}, author = {Liu, S and Liu, M and Zhang, D and Ming, Z and Liu, Z and Chen, Q and Ma, L and Luo, J and Zhang, J and Suo, D and Pei, G and Yan, T}, title = {Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm.}, journal = {IEEE journal of biomedical and health informatics}, volume = {29}, number = {4}, pages = {2932-2944}, doi = {10.1109/JBHI.2024.3406684}, pmid = {38809723}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Exoskeleton Device ; *Hand/physiology ; Male ; Adult ; *Augmented Reality ; Female ; Algorithms ; Young Adult ; Signal Processing, Computer-Assisted ; Movement/physiology ; }, abstract = {Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.}, }
@article {pmid38808372, year = {2025}, author = {Pitt, KM and Spoor, A and Zosky, J}, title = {Considering preferences, speed and the animation of multiple symbols in developing P300 brain-computer interface for children.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {20}, number = {1}, pages = {171-183}, doi = {10.1080/17483107.2024.2359479}, pmid = {38808372}, issn = {1748-3115}, mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Communication Devices for People with Disabilities ; Adolescent ; Electroencephalography ; }, abstract = {PURPOSE: Prior research has begun establishing the efficacy of animation in brain-computer interfaces access to augmentative and alternative communication (BCI-AAC). However, the use of animation in P300-BCI-AAC for children is in the early stages and largely limited to single item highlighting of extended durations. In pursuit of practical application, the present study aims to evaluate children's event-related potential (ERP) characteristics and user experience during a task involving an animated P300-BCI-AAC system.<br><br>MATERIALS AND METHODS: The system utilizes multi-item zoom animations to access a 28-pictorial symbols. Participants completed a fast (100&#x2009;ms) and slow (200&#x2009;ms) zoom animation highlighting conditions wherein four pictorial symbols were highlighted concurrently.<br><br>RESULTS: The proposed display appears feasible, eliciting all targeted ERPs. However, ERP amplitudes may be reduced in comparison to single-item animation highlighting, possibly due to distraction. Ratings of mental effort were significantly higher for the 100&#x2009;ms condition, though differences in the frontal P200/P300 ERP did not achieve significance. Most participants identified a preference for the 100&#x2009;ms condition, though age may impact preference.<br><br>CONCLUSIONS: Overall, findings support the preliminary feasibility of a proposed 28-item interface that utilises group zoom animation highlighting of pictorial symbols. Further research is needed evaluating ERP characteristics and outcomes from online (real-time) use of animation-based P300-BCI-AAC for children with severe speech and physical impairments across multiple training sessions.}, }
@article {pmid38808030, year = {2024}, author = {Xie, X and Shi, R and Yu, H and Wan, X and Liu, T and Duan, D and Li, D and Wen, D}, title = {Executive function rehabilitation and evaluation based on brain-computer interface and virtual reality: our opinion.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1377097}, pmid = {38808030}, issn = {1662-4548}, }
@article {pmid38806531, year = {2024}, author = {Zhang, X and Wang, Q and Li, J and Gao, X and Li, B and Nie, B and Wang, J and Zhou, Z and Yang, Y and Wang, H}, title = {An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {546}, pmid = {38806531}, issn = {2052-4463}, support = {52072215//National Natural Science Foundation of China (National Science Foundation of China)/ ; U1964203//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52221005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52221005//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Adult ; Female ; Humans ; Male ; Automation ; *Automobile Driving ; Brain-Computer Interfaces ; *Cognition ; Prefrontal Cortex/physiology ; Spectroscopy, Near-Infrared ; }, abstract = {For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.}, }
@article {pmid38806037, year = {2024}, author = {Zhang, Y and Li, M and Wang, H and Zhang, M and Xu, G}, title = {Preparatory movement state enhances premovement EEG representations for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad5109}, pmid = {38806037}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Movement/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; Young Adult ; Psychomotor Performance/physiology ; Intention ; Algorithms ; }, abstract = {Objective. Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions.Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy.Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p< 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95.Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.}, }
@article {pmid38805956, year = {2024}, author = {Ruiz-Mateos Serrano, R and Aguzin, A and Mitoudi-Vagourdi, E and Tao, X and Naegele, TE and Jin, AT and Lopez-Larrea, N and Picchio, ML and Vinicio Alban-Paccha, M and Minari, RJ and Mecerreyes, D and Dominguez-Alfaro, A and Malliaras, GG}, title = {3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.}, journal = {Biomaterials}, volume = {310}, number = {}, pages = {122624}, doi = {10.1016/j.biomaterials.2024.122624}, pmid = {38805956}, issn = {1878-5905}, mesh = {*Printing, Three-Dimensional ; Humans ; *Electrodes ; *Polystyrenes/chemistry ; *Textiles ; *Machine Learning ; Electric Conductivity ; Wearable Electronic Devices ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Gels/chemistry ; Polymers/chemistry ; Polyethylene Glycols/chemistry ; Electromyography/methods ; Biocompatible Materials/chemistry ; }, abstract = {The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA) to develop printable and biocompatible electrodes for long-term cutaneous electrophysiology recordings. The impact of printing parameters on the conducting properties, morphological characteristics, mechanical stability and biocompatibility of the material were investigated. The optimised eutectogel formulations were fabricated in four different patterns -flat, pyramidal, striped and wavy- to explore the influence of electrode geometry on skin conformability and mechanical contact. These electrodes were employed for impedance and forearm EMG measurements. Furthermore, arrays of twenty electrodes were embedded into a textile and used to generate body surface potential maps (BSPMs) of the forearm, where different finger movements were recorded and analysed. Finally, BSPMs for three different letters (B, I, O) in sign-language were recorded and used to train a logistic regressor classifier able to reliably identify each letter. This novel cutaneous electrode fabrication approach offers new opportunities for long-term electrophysiological recordings, online sign-language translation and brain-machine interfaces.}, }
@article {pmid38805337, year = {2024}, author = {Wang, J and Bi, L and Fei, W and Xu, X and Liu, A and Mo, L and Genetu Feleke, A}, title = {Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2087-2095}, doi = {10.1109/TNSRE.2024.3406371}, pmid = {38805337}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Movement/physiology ; Female ; Adult ; *Hand/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Algorithms ; Motor Cortex/physiology ; Healthy Volunteers ; }, abstract = {Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.}, }
@article {pmid38804201, year = {2024}, author = {Li, X and Zuo, Y and Lin, X and Guo, B and Jiang, H and Guan, N and Zheng, H and Huang, Y and Gu, X and Yu, B and Wang, X}, title = {Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design.}, journal = {Advanced healthcare materials}, volume = {13}, number = {28}, pages = {e2401793}, doi = {10.1002/adhm.202401793}, pmid = {38804201}, issn = {2192-2659}, support = {82371374//National Natural Science Foundation of China/ ; 2021ZD0200408//Scientific and Technological Innovation 2030 Program of China/ ; 010904006//Nanhu Brain-computer Interface Institute/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2019R01007//Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; }, mesh = {Animals ; *High-Throughput Screening Assays/methods ; *Drug Carriers/chemistry ; Polyethyleneimine/chemistry ; Mice ; Proteins/chemistry ; Polymers/chemistry ; Humans ; Drug Delivery Systems/methods ; }, abstract = {Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.}, }
@article {pmid38801716, year = {2024}, author = {Jahangir, TN and Abdel-Azeim, S and Kandiel, TA}, title = {BiVO4 Photoanode with NiV2O6 Back Contact Interfacial Layer for Improved Hole-Diffusion Length and Photoelectrochemical Water Oxidation Activity.}, journal = {ACS applied materials &amp; interfaces}, volume = {16}, number = {22}, pages = {28742-28755}, doi = {10.1021/acsami.4c05489}, pmid = {38801716}, issn = {1944-8252}, abstract = {The short hole diffusion length (HDL) and high interfacial recombination are among the main drawbacks of semiconductor-based solar energy systems. Surface passivation and introducing an interfacial layer are recognized for enhancing HDL and charge carrier separation. Herein, we introduced a facile recipe to design a pinholes-free BiVO4 photoanode with a NiV2O6 back contact interfacial (BCI) layer, marking a significant advancement in the HDL and photoelectrochemical activity. The fabricated BiVO4 photoanode with NiV2O6 BCI layer exhibits a 2-fold increase in the HDL compared to pristine BiVO4. Despite this improvement, we found that the front surface recombination still hinders the water oxidation process, as revealed by photoelectrochemical (PEC) studies employing Na2SO3 electron donors and by intensity-modulated photocurrent spectroscopy measurements. To address this limitation, the surface of the NiV2O6/BiVO4 photoanode was passivated with a cobalt phosphate electrocatalyst, resulting in a dramatic enhancement in the PEC performance. The optimized photoanode achieved a stable photocurrent density of 4.8 mA cm[-2] at 1.23 VRHE, which is 12-fold higher than that of the pristine BiVO4 photoanode. Density Functional Theory (DFT) simulations revealed an abrupt electrostatic potential transition at the NiV2O6/BiVO4 interface with BiVO4 being more negative than NiV2O6. A strong built-in electric field is thus generated at the interface and drifts photogenerated electrons toward the NiV2O6 BCI layer and photogenerated holes toward the BiVO4 top layer. As a result, the back-surface recombination is minimized, and ultimately, the HDL is extended in agreement with the experimental findings.}, }
@article {pmid38801273, year = {2024}, author = {Lakshminarayanan, K and Shah, R and Ramu, V and Madathil, D and Yao, Y and Wang, I and Brahmi, B and Rahman, MH}, title = {Motor Imagery Performance through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {207}, pages = {}, doi = {10.3791/66859}, pmid = {38801273}, issn = {1940-087X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Neurological Rehabilitation/methods ; }, abstract = {This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) technologies with the customization of three-dimensional (3D) avatars. Traditional approaches to rehabilitation often fail to fully engage patients, primarily due to their inability to provide a deeply immersive and interactive experience. This research endeavors to fill this gap by utilizing motor imagery (MI) techniques, where participants visualize physical movements without actual execution. This method capitalizes on the brain's neural mechanisms, activating areas involved in movement execution when imagining movements, thereby facilitating the recovery process. The integration of VR's immersive capabilities with the precision of electroencephalography (EEG) to capture and interpret brain activity associated with imagined movements forms the core of this system. Digital Twins in the form of personalized 3D avatars are employed to significantly enhance the sense of immersion within the virtual environment. This heightened sense of embodiment is crucial for effective rehabilitation, aiming to bolster the connection between the patient and their virtual counterpart. By doing so, the system not only aims to improve motor imagery performance but also seeks to provide a more engaging and efficacious rehabilitation experience. Through the real-time application of BCI, the system allows for the direct translation of imagined movements into virtual actions performed by the 3D avatar, offering immediate feedback to the user. This feedback loop is essential for reinforcing the neural pathways involved in motor control and recovery. The ultimate goal of the developed system is to significantly enhance the effectiveness of motor imagery exercises by making them more interactive and responsive to the user's cognitive processes, thereby paving a new path in the field of neurological rehabilitation.}, }
@article {pmid38800720, year = {2024}, author = {Zhu, Y and Jiang, D and Qiu, Y and Liu, X and Bian, Y and Tian, S and Wang, X and Hsia, KJ and Wan, H and Zhuang, L and Wang, P}, title = {Dynamic microphysiological system chip platform for high-throughput, customizable, and multi-dimensional drug screening.}, journal = {Bioactive materials}, volume = {39}, number = {}, pages = {59-73}, pmid = {38800720}, issn = {2452-199X}, abstract = {Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening. By employing diverse types of spheroids or organoids, it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening. High-throughput microphysiological systems that support optional, parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research. However, establishing such a system is highly challenging and requires a multidisciplinary approach. This study introduces a dynamic Microphysiological System Chip Platform (MSCP) with multiple functional microstructures that encompass the mentioned advantages. We developed a high-throughput lung cancer spheroids model and an intestine-liver-heart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs, demonstrating the feasibility of the MSCP. This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects. Moreover, the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication. The MSCP could serves as a valuable platform for microphysiological system research, making significant contributions to disease modeling, drug development, and personalized medical treatment.}, }
@article {pmid38800606, year = {2024}, author = {Fu, P and Liu, Y and Zhu, L and Wang, M and Yu, Y and Yang, F and Zhang, W and Zhang, H and Shoham, S and Roe, AW and Xi, W}, title = {Two-photon imaging of excitatory and inhibitory neural response to infrared neural stimulation.}, journal = {Neurophotonics}, volume = {11}, number = {2}, pages = {025003}, pmid = {38800606}, issn = {2329-423X}, abstract = {SIGNIFICANCE: Pulsed infrared neural stimulation (INS, 1875 nm) is an emerging neurostimulation technology that delivers focal pulsed heat to activate functionally specific mesoscale networks and holds promise for clinical application. However, little is known about its effect on excitatory and inhibitory cell types in cerebral cortex.<br><br>AIM: Estimates of summed population neuronal response time courses provide a potential basis for neural and hemodynamic signals described in other studies.<br><br>APPROACH: Using two-photon calcium imaging in mouse somatosensory cortex, we have examined the effect of INS pulse train application on hSyn neurons and mDlx neurons tagged with GCaMP6s.<br><br>RESULTS: We find that, in anesthetized mice, each INS pulse train reliably induces robust response in hSyn neurons exhibiting positive going responses. Surprisingly, mDlx neurons exhibit negative going responses. Quantification using the index of correlation illustrates responses are reproducible, intensity-dependent, and focal. Also, a contralateral activation is observed when INS applied.<br><br>CONCLUSIONS: In sum, the population of neurons stimulated by INS includes both hSyn and mDlx neurons; within a range of stimulation intensities, this leads to overall excitation in the stimulated population, leading to the previously observed activations at distant post-synaptic sites.}, }
@article {pmid38800199, year = {2024}, author = {Vafopoulou, E and Christodoulou, N and Papathanasiou, IV}, title = {Treatment Adherence and Quality of Life Among Elderly Patients With Diabetes Mellitus Registered in the Community.}, journal = {Cureus}, volume = {16}, number = {4}, pages = {e58986}, pmid = {38800199}, issn = {2168-8184}, abstract = {Background This study investigates the association between medication adherence and health-related quality of life among adults with type 2 diabetes mellitus at the Health Center of Tyrnavos community level. Materials and methods This cross-sectional study involved 125 patients with type 2 diabetes mellitus, aged 60 years and older, who were visiting community healthcare facilities. The research was conducted with a structured questionnaire that included 34 questions related to socio-demographic data, self-reported compliance, and stress. The DQOL-BCI (Diabetes Quality of Life - Brief Clinical Inventory) scale was used to measure health-related quality of life. Results A total of 125 patients with a mean (SD) age of 69.2 (8.1) years were included in the study (64 women and 61 men). Based on the results of the descriptive analysis, 88.0% reported high medication adherence. However, 66% of the participants reported having high anxiety levels, with 33.6% having difficulty controlling their anxiety. Quality of life was negatively correlated with lower medication adherence (P < 0.05). Conclusions Older age and low medication adherence are associated with lower quality of life among diabetic patients. Interventions to improve the quality of life in elderly diabetic patients should consider the effect of age and medication adherence.}, }
@article {pmid38799637, year = {2024}, author = {Wang, L and Zhang, J and Zhang, W and Zheng, M and Guo, H and Pan, X and Li, W and Yang, B and Ding, L}, title = {The inhibitory effect of adenosine on tumor adaptive immunity and intervention strategies.}, journal = {Acta pharmaceutica Sinica. B}, volume = {14}, number = {5}, pages = {1951-1964}, pmid = {38799637}, issn = {2211-3835}, abstract = {Adenosine (Ado) is significantly elevated in the tumor microenvironment (TME) compared to normal tissues. It binds to adenosine receptors (AdoRs), suppressing tumor antigen presentation and immune cell activation, thereby inhibiting tumor adaptive immunity. Ado downregulates major histocompatibility complex II (MHC II) and co-stimulatory factors on dendritic cells (DCs) and macrophages, inhibiting antigen presentation. It suppresses anti-tumor cytokine secretion and T cell activation by disrupting T cell receptor (TCR) binding and signal transduction. Ado also inhibits chemokine secretion and KCa3.1 channel activity, impeding effector T cell trafficking and infiltration into the tumor site. Furthermore, Ado diminishes T cell cytotoxicity against tumor cells by promoting immune-suppressive cytokine secretion, upregulating immune checkpoint proteins, and enhancing immune-suppressive cell activity. Reducing Ado production in the TME can significantly enhance anti-tumor immune responses and improve the efficacy of other immunotherapies. Preclinical and clinical development of inhibitors targeting Ado generation or AdoRs is underway. Therefore, this article will summarize and analyze the inhibitory effects and molecular mechanisms of Ado on tumor adaptive immunity, as well as provide an overview of the latest advancements in targeting Ado pathways in anti-tumor immune responses.}, }
@article {pmid38799297, year = {2024}, author = {Wider, W and Mutang, JA and Chua, BS and Pang, NTP and Jiang, L and Fauzi, MA and Udang, LN}, title = {Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1339444}, pmid = {38799297}, issn = {1662-5161}, abstract = {INTRODUCTION: This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.<br><br>METHODS: It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.<br><br>RESULTS: The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.<br><br>DISCUSSION: This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.}, }
@article {pmid38798494, year = {2025}, author = {Jung, T and Zeng, N and Fabbri, JD and Eichler, G and Li, Z and Zabeh, E and Das, A and Willeke, K and Wingel, KE and Dubey, A and Huq, R and Sharma, M and Hu, Y and Ramakrishnan, G and Tien, K and Mantovani, P and Parihar, A and Yin, H and Oswalt, D and Misdorp, A and Uguz, I and Shinn, T and Rodriguez, GJ and Nealley, C and Sanborn, S and Gonzales, I and Roukes, M and Knecht, J and Yoshor, D and Canoll, P and Spinazzi, E and Carloni, LP and Pesaran, B and Patel, S and Jacobs, J and Youngerman, B and Cotton, RJ and Tolias, A and Shepard, KL}, title = {Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38798494}, issn = {2692-8205}, support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; }, abstract = {Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating a 256×256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.}, }
@article {pmid38798438, year = {2024}, author = {Rosenthal, IA and Bashford, L and Bjånes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA}, title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38798438}, issn = {2692-8205}, support = {T32 NS105595/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; }, abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously when visual stimuli were more realistic, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts.}, }
@article {pmid38798056, year = {2024}, author = {Mathewson, KE and Kuziek, JP and Scanlon, JEM and Robles, D}, title = {The moving wave: Applications of the mobile EEG approach to study human attention.}, journal = {Psychophysiology}, volume = {61}, number = {9}, pages = {e14603}, doi = {10.1111/psyp.14603}, pmid = {38798056}, issn = {1469-8986}, mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Mobile Applications ; Event-Related Potentials, P300/physiology ; }, abstract = {Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.}, }
@article {pmid38796879, year = {2024}, author = {Chen, A and Sun, D and Gao, X and Zhang, D}, title = {A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.}, journal = {Computers in biology and medicine}, volume = {177}, number = {}, pages = {108619}, doi = {10.1016/j.compbiomed.2024.108619}, pmid = {38796879}, issn = {1879-0534}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Algorithms ; }, abstract = {In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.}, }
@article {pmid38796691, year = {2024}, author = {Tao, Q and Xu, Y and He, Y and Luo, T and Li, X and Han, L}, title = {Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data.}, journal = {Briefings in bioinformatics}, volume = {25}, number = {4}, pages = {}, pmid = {38796691}, issn = {1477-4054}, support = {2021ZD0204400//National Science and Technology Innovation 2030 Major Program/ ; }, mesh = {Animals ; Mice ; *Algorithms ; *Brain/metabolism ; *Single-Cell Analysis/methods ; RNA-Seq/methods ; Transcriptome ; Sequence Analysis, RNA/methods ; Neurons/metabolism ; Gene Expression Profiling/methods ; }, abstract = {Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.}, }
@article {pmid38795979, year = {2024}, author = {Bhatt, MW and Sharma, S}, title = {Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces.}, journal = {Journal of neuroscience methods}, volume = {408}, number = {}, pages = {110182}, doi = {10.1016/j.jneumeth.2024.110182}, pmid = {38795979}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Attention/physiology ; Motor Activity/physiology ; Brain/physiology ; Hand/physiology ; Signal Processing, Computer-Assisted ; Adult ; Neural Networks, Computer ; }, abstract = {BACKGROUND: Motor imagery-based electroencephalogram (EEG) brain-computer interface (BCI) technology has seen tremendous advancements in the past several years. Deep learning has outperformed more traditional approaches, such next-gen neuro-technologies, in terms of productivity. It is still challenging to develop and train an end-to-end network that can sufficiently extract the possible characteristics from EEG data used in motor imaging. Brain-computer interface research is largely reliant on the fundamental problem of accurately classifying EEG data. There are still many challenges in the field of MI classification even after researchers have proposed a variety of methods, such as deep learning and machine learning techniques.<br><br>METHODOLOGY: We provide a model for four-class categorization of motor imagery EEG signals using attention mechanisms: left hand, right hand, foot, and tongue/rest. The model is built on multi-scale spatiotemporal self-attention networks. To determine the most effective channels, self-attention networks are implemented spatially to assign greater weight to channels associated with motion and lesser weight to channels unrelated to motion. To eliminate noise in the temporal domain, parallel multi-scale Temporal Convolutional Network (TCN) layers are utilized to extract temporal domain features at various scales.<br><br>RESULT: On the IV-2b dataset from the BCI Competition, the suggested model achieved an accuracy of 85.09&#x202f;%; on the IV-2a and IV-2b datasets from the HGD datasets, it was 96.26&#x202f;%.<br><br>In single-subject classification, this approach demonstrates superior accuracy when compared to existing methods.<br><br>CONCLUSION: The findings suggest that this approach exhibits commendable performance, resilience, and capacity for transfer learning.}, }
@article {pmid38795798, year = {2024}, author = {Han, G and Jiao, B and Zhang, Y and Wang, Z and Liang, C and Li, Y and Hsu, YC and Bai, R}, title = {Arterial pulsation dependence of perivascular cerebrospinal fluid flow measured by dynamic diffusion tensor imaging in the human brain.}, journal = {NeuroImage}, volume = {297}, number = {}, pages = {120653}, doi = {10.1016/j.neuroimage.2024.120653}, pmid = {38795798}, issn = {1095-9572}, mesh = {Humans ; Adult ; Female ; Male ; Young Adult ; *Diffusion Tensor Imaging/methods ; Adolescent ; *Cerebrospinal Fluid/physiology/diagnostic imaging ; *Glymphatic System/diagnostic imaging/physiology ; Brain/physiology/diagnostic imaging/blood supply ; Pulsatile Flow/physiology ; Cerebral Arteries/diagnostic imaging/physiology ; }, abstract = {Perivascular cerebrospinal fluid (pCSF) flow is a key component of the glymphatic system. Arterial pulsation has been proposed as the main driving force of pCSF influx along the superficial and penetrating arteries; however, evidence of this mechanism in humans is limited. We proposed an experimental framework of dynamic diffusion tensor imaging with low b-values and ultra-long echo time (dynDTIlow-b) to capture pCSF flow properties during the cardiac cycle in human brains. Healthy adult volunteers (aged 17-28 years; seven men, one woman) underwent dynDTIlow-b using a 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) with simultaneously recorded cardiac output. The results showed that diffusion tensors reconstructed from pCSF were mainly oriented in the direction of the neighboring arterial flow. When switching from vasoconstriction to vasodilation, the axial and radial diffusivities of the pCSF increased by 5.7 % and 4.94 %, respectively, suggesting that arterial pulsation alters the pCSF flow both parallel and perpendicular to the arterial wall. DynDTIlow-b signal intensity at b=0 s/mm[2] (i.e., T2-weighted, [S(b=0 s/mm[2])]) decreased in systole, but this change was ∼7.5 % of a cardiac cycle slower than the changes in apparent diffusivity, suggesting that changes in S(b=0 s/mm[2]) and apparent diffusivity arise from distinct physiological processes and potential biomarkers associated with perivascular space volume and pCSF flow, respectively. Additionally, the mean diffusivities of white matter showed cardiac-cycle dependencies similar to pCSF, although a delay relative to the peak time of apparent diffusivity in pCSF was present, suggesting that dynDTIlow-b could potentially reveal the dynamics of magnetic resonance imaging-invisible pCSF surrounding small arteries and arterioles in white matter; this delay may result from pulse wave propagation along penetrating arteries. In conclusion, the vasodilation-induced increases in axial and radial diffusivities of pCSF and mean diffusivities of white matter are consistent with the notion that arterial pulsation can accelerate pCSF flow in human brain. Furthermore, the proposed dynDTIlow-b technique can capture various pCSF dynamics in artery pulsation.}, }
@article {pmid38794022, year = {2024}, author = {Khabti, J and AlAhmadi, S and Soudani, A}, title = {Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {10}, pages = {}, pmid = {38794022}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Attention/physiology ; }, abstract = {The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.}, }
@article {pmid38793895, year = {2024}, author = {Akhter, J and Naseer, N and Nazeer, H and Khan, H and Mirtaheri, P}, title = {Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {10}, pages = {}, pmid = {38793895}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Spectroscopy, Near-Infrared/methods ; *Algorithms ; Male ; *Neural Networks, Computer ; Adult ; Female ; Young Adult ; Brain/physiology/diagnostic imaging ; }, abstract = {Brain-computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.}, }
@article {pmid38793855, year = {2024}, author = {Zhang, X and Li, J and Zhang, R and Liu, T}, title = {A Brain-Controlled and User-Centered Intelligent Wheelchair: A Feasibility Study.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {10}, pages = {}, pmid = {38793855}, issn = {1424-8220}, mesh = {*Wheelchairs ; Humans ; *Brain-Computer Interfaces ; *Feasibility Studies ; Persons with Disabilities ; Equipment Design ; Ergonomics/methods ; User-Centered Design ; Surveys and Questionnaires ; }, abstract = {Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain-machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.}, }
@article {pmid38793401, year = {2024}, author = {Wang, S and Yan, X and Jiao, X and Yang, H}, title = {Experimental Study of the Implantation Process for Array Electrodes into Highly Transparent Agarose Gel.}, journal = {Materials (Basel, Switzerland)}, volume = {17}, number = {10}, pages = {}, pmid = {38793401}, issn = {1996-1944}, abstract = {Brain-computer interface (BCI) technology is currently a cutting-edge exploratory problem in the field of human-computer interaction. However, in experiments involving the implantation of electrodes into brain tissue, particularly high-speed or array implants, existing technologies find it challenging to observe the damage in real time. Considering the difficulties in obtaining biological brain tissue and the challenges associated with real-time observation of damage during the implantation process, we have prepared a transparent agarose gel that closely mimics the mechanical properties of biological brain tissue for use in electrode implantation experiments. Subsequently, we developed an experimental setup for synchronized observation of the electrode implantation process, utilizing the Digital Gradient Sensing (DGS) method. In the single electrode implantation experiments, with the increase in implantation speed, the implantation load increases progressively, and the tissue damage region around the electrode tip gradually diminishes. In the array electrode implantation experiments, compared to a single electrode, the degree of tissue indentation is more severe due to the coupling effect between adjacent electrodes. As the array spacing increases, the coupling effect gradually diminishes. The experimental results indicate that appropriately increasing the velocity and array spacing of the electrodes can enhance the likelihood of successful implantation. The research findings of this article provide valuable guidance for the damage assessment and selection of implantation parameters during the process of electrode implantation into real brain tissue.}, }
@article {pmid38793168, year = {2024}, author = {Bi, M and Zhang, H and Ma, Y and Wang, H and Wang, W and Shi, Y and Sheng, W and Li, Q and Gao, G and Cai, L}, title = {Modulation Steering Motion by Quantitative Electrical Stimulation in Pigeon Robots.}, journal = {Micromachines}, volume = {15}, number = {5}, pages = {}, pmid = {38793168}, issn = {2072-666X}, support = {31500858, 61973159//National Natural Science Foundation of China/ ; 2021YFB3201200//National Key Research and Development Program of China/ ; }, abstract = {The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain-machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2-3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain-machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.}, }
@article {pmid38790494, year = {2024}, author = {Jiang, C and Dai, Y and Ding, Y and Chen, X and Li, Y and Tang, Y}, title = {TSANN-TG: Temporal-Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition.}, journal = {Brain sciences}, volume = {14}, number = {5}, pages = {}, pmid = {38790494}, issn = {2076-3425}, support = {21ZR1481500//Science and Technology Commission of Shanghai Municipality/ ; }, abstract = {Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain-computer interfaces. In this paper, we propose TSANN-TG (temporal-spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal-spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block. Leveraging the distinct temporal scales of features from EEG signals, TSANN-TG incorporates attention mechanisms for efficient feature extraction. By constructing task-specific adjacency matrices, the graph convolutional network with an attention mechanism captures the dynamic changes in dependency information between EEG channels. Additionally, TSANN-TG emphasizes feature integration at multiple levels, leading to improved performance in emotion-recognition tasks. Our proposed TSANN-TG is applied to both our FTEHD dataset and the publicly available DEAP dataset. Comparative experiments and ablation studies highlight the excellent recognition results achieved. Compared to the baseline algorithms, TSANN-TG demonstrates significant enhancements in accuracy and F1 score on the two benchmark datasets for four types of cognitive tasks. These results underscore the significant potential of the TSANN-TG method to advance EEG-based emotion recognition.}, }
@article {pmid38790476, year = {2024}, author = {Huang, D and Wang, Y and Fan, L and Yu, Y and Zhao, Z and Zeng, P and Wang, K and Li, N and Shen, H}, title = {Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface.}, journal = {Brain sciences}, volume = {14}, number = {5}, pages = {}, pmid = {38790476}, issn = {2076-3425}, support = {JCKY2020550B003//Defense Industrial Technology Development Program/ ; }, abstract = {In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.}, }
@article {pmid38790456, year = {2024}, author = {Yang, G and Liu, J}, title = {A New Framework Combining Diffusion Models and the Convolution Classifier for Generating Images from EEG Signals.}, journal = {Brain sciences}, volume = {14}, number = {5}, pages = {}, pmid = {38790456}, issn = {2076-3425}, support = {2018YFB1304600//National Key R\&amp;D Program of China/ ; JCTD-2018-11//CAS Interdisciplinary Innovation Team/ ; }, abstract = {The generation of images from electroencephalography (EEG) signals has become a popular research topic in recent research because it can bridge the gap between brain signals and visual stimuli and has wide application prospects in neuroscience and computer vision. However, due to the high complexity of EEG signals, the reconstruction of visual stimuli through EEG signals continues to pose a challenge. In this work, we propose an EEG-ConDiffusion framework that involves three stages: feature extraction, fine-tuning of the pretrained model, and image generation. In the EEG-ConDiffusion framework, classification features of EEG signals are first obtained through the feature extraction block. Then, the classification features are taken as conditions to fine-tune the stable diffusion model in the image generation block to generate images with corresponding semantics. This framework combines EEG classification and image generation means to enhance the quality of generated images. Our proposed framework was tested on an EEG-based visual classification dataset. The performance of our framework is measured by classification accuracy, 50-way top-k accuracy, and inception score. The results indicate that the proposed EEG-Condiffusion framework can extract effective classification features and generate high-quality images from EEG signals to realize EEG-to-image conversion.}, }
@article {pmid38790441, year = {2024}, author = {Shiam, AA and Hassan, KM and Islam, MR and Almassri, AMM and Wagatsuma, H and Molla, MKI}, title = {Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.}, journal = {Brain sciences}, volume = {14}, number = {5}, pages = {}, pmid = {38790441}, issn = {2076-3425}, support = {Japan Society for the Promotion of Science//S19169/ ; }, abstract = {Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.}, }
@article {pmid38790340, year = {2024}, author = {Polo-Hortigüela, C and Maximo, M and Jara, CA and Ramon, JL and Garcia, GJ and Ubeda, A}, title = {A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {5}, pages = {}, pmid = {38790340}, issn = {2306-5354}, abstract = {In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.}, }
@article {pmid38789029, year = {2024}, author = {Awuah, WA and Ahluwalia, A and Darko, K and Sanker, V and Tan, JK and Tenkorang, PO and Ben-Jaafar, A and Ranganathan, S and Aderinto, N and Mehta, A and Shah, MH and Lee Boon Chun, K and Abdul-Rahman, T and Atallah, O}, title = {Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications.}, journal = {World neurosurgery}, volume = {189}, number = {}, pages = {138-153}, doi = {10.1016/j.wneu.2024.05.104}, pmid = {38789029}, issn = {1878-8769}, mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Neurosurgical Procedures/methods ; Neurosurgery ; Nervous System Diseases/surgery ; }, abstract = {Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.}, }
@article {pmid38788849, year = {2024}, author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J}, title = {Validity of luminometry and bacteriological tests for diagnosing intramammary infection at dry-off in dairy cows.}, journal = {Journal of dairy science}, volume = {107}, number = {9}, pages = {7221-7229}, doi = {10.3168/jds.2024-24693}, pmid = {38788849}, issn = {1525-3198}, mesh = {Animals ; Cattle ; Female ; *Animal Husbandry/methods ; *Bacteriological Techniques/standards/veterinary ; Cross-Sectional Studies ; Dairying/methods ; *Mastitis, Bovine/diagnosis ; Reproducibility of Results ; }, abstract = {The objective of this cross-sectional study was to estimate the validity of laboratory culture, Petrifilm and Tri-Plate on-farm culture systems, as well as luminometry to correctly identify IMI at dry-off in dairy cows, considering all tests to be imperfect. From September 2020 until December 2021, we collected composite milk samples from cows before dry-off and divided them into 4 aliquots for luminometry, Petrifilm (aerobic count), Tri-Plate, and laboratory culture tests. We assessed multiple thresholds of relative light units (RLU) for luminometry, and we used thresholds of ≥100 cfu/mL for the laboratory culture, ≥50 cfu/mL for Petrifilm, and ≥1 cfu for Tri-Plate tests. We fitted Bayesian latent class analysis models to estimate the sensitivity (Se) and specificity (Sp) for each test to identify IMI, with 95% credibility interval (BCI). Using different prevalence measures (0.30, 0.50, and 0.70), we calculated the predictive values (PV) and misclassification cost terms (MCT) at different false negative-to-false-positive ratios (FN:FP). A total of 333 cows were enrolled in the study from one commercial Holstein herd. The validity of the luminometry was poor for all thresholds, with an Se of 0.51 (95% BCI = 0.43-0.59) and Sp of 0.38 (95% BCI = 0.26-0.50) when using a threshold of ≥150 RLU. The laboratory culture had an Se of 0.93 (95% BCI = 0.85-0.98) and Sp of 0.69 (95% BCI = 0.49-0.89); the Petrifilm had an Se of 0.91 (95% BCI = 0.80-0.98) and Sp of 0.71 (95% BCI = 0.51-0.90); and the Tri-Plate had an Se of 0.65 (95% BCI = 0.53-0.82) and Sp of 0.85 (95% BCI = 0.66-0.97). Bacteriological tests had good PV, with comparable positive PV for all 3 tests, but lower negative PV for the Tri-Plate compared with the laboratory culture and the Petrifilm. For a prevalence of IMI of 0.30, all 3 tests had similar MCT, but for prevalence of 0.50 and 0.70, the Tri-Plate had higher MCT in scenarios where leaving a cow with IMI untreated is considered to have greater detrimental effects than treating a healthy cow (i.e., FN:FP of 3:1). Our results showed that the bacteriological tests have adequate validity to diagnose IMI at dry-off, but luminometry does not. We concluded that although luminometry is not useful to identify IMI at dry-off, the Petrifilm and Tri-Plate tests performed similarly to laboratory culture, depending on the prevalence and importance of the FP and FN results.}, }
@article {pmid38788835, year = {2024}, author = {Denis-Robichaud, J and Oliveira, AP and Sica, A and Soriano, S and Araújo, RL and Pereira, MHC and Pohler, KG and Cerri, RLA and Vasconcelos, JLM}, title = {Is prolonged luteal phase a problem in lactating Holstein cows?.}, journal = {Journal of dairy science}, volume = {107}, number = {10}, pages = {8582-8591}, doi = {10.3168/jds.2024-24792}, pmid = {38788835}, issn = {1525-3198}, mesh = {Animals ; Female ; Cattle ; *Lactation ; *Luteal Phase ; *Insemination, Artificial/veterinary ; Pregnancy ; Estrus Synchronization ; Corpus Luteum ; Estrous Cycle ; Brazil ; }, abstract = {In this study, the main objective was to assess if long luteal phases could have causes other than pregnancy loss. We enrolled Holstein dairy cows ≥50 DIM from a commercial herd in Brazil from October 2016 to August 2017. All cows received an estradiol-based synchronization protocol, and, on the day of insemination (d 0), were randomly assigned either an AI or a placebo insemination (PBO) in a 3:1 ratio. An ultrasound was used to assess the presence of a corpus luteum (CL) on d 17, 24, and 31, which, combined to the information from patches for the detection of estrus, was used to determine the length of the luteal phase following AI or PBO. Pregnancy was assessed by ultrasound on d 31 and cows that were pregnant were excluded from the analyses. The length of the estrous cycles was categorized as short (<17 d), normal (17-23 d), long (24-30 d), and very long (≥31 d). We compared the proportion of cows in each category between the AI and PBO groups using a cumulative ordinal mixed model. We define prolonged luteal phase as estrous cycles ≥24 d and tested its association with potential risk factors (parity, season, DIM, uterine size and position score, milk production, BCS, and the presence of a CL at enrollment to the synchronization protocol) using mixed logistic regression models. Results are presented as odds ratio (OR) and 95% Bayesian credible intervals (BCI). Data from 876 inseminations (AI: n = 616, PBO: n = 260) was collected. Overall, 12% of estrous cycles were short, 31% were normal, 19% were long, and 38% were very long. There was no difference in the odds of being in longer estrous cycle categories for the AI compared with the PBO group (OR = 0.92; 95% BCI = 0.76-1.10). Season and presence of a CL at enrollment were associated with prolonged luteal phase. In the AI group, there was a possible effect of early pregnancy losses on the lifespan of the CL, but not the PBO group, which led us to conclude that long and very long estrous cycles were not all caused by the embryonic loss. In fact, the high prevalence of cows with an extended CL lifespan in the present study suggests this could be an under- or miss-reported characteristic of high-producing lactating Holstein cows. This finding may have important repercussions in the understanding of the CL function physiology of lactating Holstein cows.}, }
@article {pmid38788794, year = {2024}, author = {Semeraro, F and Schnaubelt, S and Malta Hansen, C and Bignami, EG and Piazza, O and Monsieurs, KG}, title = {Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations.}, journal = {Resuscitation}, volume = {200}, number = {}, pages = {110250}, doi = {10.1016/j.resuscitation.2024.110250}, pmid = {38788794}, issn = {1873-1570}, mesh = {Humans ; *Cardiopulmonary Resuscitation/methods/instrumentation ; *Heart Arrest/therapy ; Inventions ; Forecasting ; Artificial Intelligence ; Defibrillators ; }, abstract = {INTRODUCTION: Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.<br><br>METHODS: We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.<br><br>RESULTS: We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.<br><br>CONCLUSION: Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.}, }
@article {pmid38788704, year = {2024}, author = {Chen, K and Forrest, AM and Burgos, GG and Kozai, TDY}, title = {Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, pmid = {38788704}, issn = {1741-2552}, support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Microelectrodes ; *Mice, Inbred C57BL ; *Electrodes, Implanted ; Mice ; Male ; Female ; Brain-Computer Interfaces ; Nerve Net/physiology ; Neurons/physiology ; Primary Visual Cortex/physiology ; Photic Stimulation/methods ; Foreign-Body Reaction/etiology ; CA1 Region, Hippocampal/physiology ; }, abstract = {Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain-computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.Approach.This study utilized single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.}, }
@article {pmid38785851, year = {2024}, author = {Pan, Y and Wang, H and Geng, Y and Lai, J and Hu, S}, title = {Latent Profile Analysis of Suicidal Ideation in Chinese Individuals with Bipolar Disorder.}, journal = {Behavioral sciences (Basel, Switzerland)}, volume = {14}, number = {5}, pages = {}, pmid = {38785851}, issn = {2076-328X}, support = {226-2022-00193, 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; No. 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; No. 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; }, abstract = {Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.}, }
@article {pmid38785685, year = {2024}, author = {Kong, X and Wu, C and Chen, S and Wu, T and Han, J}, title = {Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion.}, journal = {Biosensors}, volume = {14}, number = {5}, pages = {}, pmid = {38785685}, issn = {2079-6374}, support = {2022YFF1202400, 2023YFF1204200, 2023YFF1203900//National Key Research and Development Program of China/ ; }, mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Imagination/physiology ; }, abstract = {Brain-computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model's input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model's overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.}, }
@article {pmid38784100, year = {2024}, author = {Zhang, Y and Hua, W and Zhou, Z and Zhu, H and Xiong, J and Zhang, J and Chen, D and Guo, J}, title = {A novel acupuncture technique at the Zusanli point based on virtual reality and EEG: a pilot study.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1269903}, pmid = {38784100}, issn = {1662-4548}, abstract = {INTRODUCTION: Acupuncture is a Traditional Chinese Medicine (TCM) method that achieves therapeutic effects through the interaction of neurotransmitters and neural regulation. It is generally carried out manually, making the related process expert-biased. Meanwhile, the neural stimulation effect of acupuncture is difficult to track objectively. In recent years, virtual reality (VR) in medicine has been on the fast lane to widespread use, especially in therapeutic stimulation. However, the use of related technologies in acupuncture has not been reported.<br><br>METHODS: In this work, a novel acupuncture stimulation technique using VR is proposed. To track the stimulation effect, the electroencephalogram (EEG) is used as the marker to validate brain activities under acupuncture.<br><br>RESULTS AND DISCUSSION: After statistically analyzing the data of 24 subjects during acupuncture at the "Zusanli (ST36)" acupoint, it has been determined that Virtual Acupuncture (VA) has at least a 63.54% probability of inducing similar EEG activities as in Manual Acupuncture (MA). This work may provide a new solution for researchers and clinical practitioners using Brain-Computer Interface (BCI) in acupuncture.}, }
@article {pmid38784094, year = {2024}, author = {Gouret, A and Le Bars, S and Porssut, T and Waszak, F and Chokron, S}, title = {Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1373377}, pmid = {38784094}, issn = {1662-4548}, abstract = {This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.}, }
@article {pmid38784093, year = {2024}, author = {Chen, W and Liu, X and Wan, P and Chen, Z and Chen, Y}, title = {Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1393206}, pmid = {38784093}, issn = {1662-4548}, abstract = {In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.}, }
@article {pmid38783889, year = {2024}, author = {Arulkumaran, K and Di Vincenzo, M and Dossa, RFJ and Akiyama, S and Ogawa Lillrank, D and Sato, M and Tomeoka, K and Sasai, S}, title = {A comparison of visual and auditory EEG interfaces for robot multi-stage task control.}, journal = {Frontiers in robotics and AI}, volume = {11}, number = {}, pages = {1329270}, pmid = {38783889}, issn = {2296-9144}, abstract = {Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy-most commonly electroencephalography-based-the two most common choices are to provide either auditory or visual stimuli to the user-each with their respective pros and cons. Using the oddball paradigm, we designed comparable auditory and visual interfaces to speak/display the choices to the user, and had users complete a multi-stage robotic manipulation task involving location and object selection. Users displayed differing competencies-and preferences-for the different interfaces, highlighting the importance of considering modalities outside of vision when constructing human-robot interfaces.}, }
@article {pmid38782530, year = {2024}, author = {Yaeger, K and Mocco, J}, title = {Venous Sinus Stent to Treat Paralysis.}, journal = {Neurosurgery clinics of North America}, volume = {35}, number = {3}, pages = {375-378}, doi = {10.1016/j.nec.2024.03.003}, pmid = {38782530}, issn = {1558-1349}, mesh = {Humans ; *Stents ; Animals ; *Paralysis/surgery ; Cranial Sinuses/surgery ; Electrodes, Implanted ; }, abstract = {Transvenous treatment of paralysis is a concept less than a decade old. The Stentrode (Synchron, Inc, New York, USA) is a novel electrode on stent device intended to be implanted in the superior sagittal sinus adjacent to the motor cortex. Initial animal studies in sheep demonstrated the safety of the implant as well as its accuracy in detecting neural signals at both short and long term. Early human trials have shown the safety of the device and demonstrated the use of the Stentrode system in facilitating patients with paralysis to carry out daily activities such as texting, email, and personal finance. This is an emerging technology with promise, although certainly more research is required to better understand the capabilities and limitations of the device.}, }
@article {pmid38782122, year = {2024}, author = {Kapgate, DD}, title = {The use of happy faces as visual stimuli improves the performance of the hybrid SSVEP+P300 brain computer interface.}, journal = {Journal of neuroscience methods}, volume = {408}, number = {}, pages = {110170}, doi = {10.1016/j.jneumeth.2024.110170}, pmid = {38782122}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Event-Related Potentials, P300/physiology ; Young Adult ; Female ; *Electroencephalography/methods ; *Photic Stimulation/methods ; Adult ; Facial Expression ; Brain/physiology ; Facial Recognition/physiology ; }, abstract = {BACKGROUND: This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli.<br><br>NEW METHOD: In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF).<br><br>RESULTS AND CONCLUSIONS: The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.}, }
@article {pmid38781932, year = {2024}, author = {R, V and Ramasubba Reddy, M}, title = {Optimizing motor imagery BCI models with hard trials removal and model refinement.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {4}, pages = {}, doi = {10.1088/2057-1976/ad4f8e}, pmid = {38781932}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; *Algorithms ; Imagination ; Artificial Intelligence ; Neural Networks, Computer ; }, abstract = {Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance. The first method leverages model prediction scores to discern hard trials. The second approach employs a quantitative explainable artificial intelligence (XAI) approach, enabling a more transparent and interpretable means of hard trial identification. The identified hard trials are removed from the entire motor imagery training and validation dataset, and the deep learning model is further re-trained using the dataset without hard trials. To evaluate the efficacy of these proposed methods, experiments were conducted on the Open BMI dataset. The results for hold-out analysis show that the proposed quantitative XAI- based hard trial removal method has statistically improved the average classification accuracy of the baseline deep CNN model from 63.77% to 68.70%, withp-value =7.66e-11for the subject-specific MI classification. Additionally, analyzing the scalp map representing the average relevance scores of correctly classified trials compared to a misclassified trial provides a deeper insight into identifying hard trials. The results indicate that the proposed quantitaive-based XAI approach outperformes the prediction-score-based approach in hard trial identification.}, }
@article {pmid38781127, year = {2024}, author = {Kojima, S and Kanoh, S}, title = {An auditory brain-computer interface based on selective attention to multiple tone streams.}, journal = {PloS one}, volume = {19}, number = {5}, pages = {e0303565}, pmid = {38781127}, issn = {1932-6203}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Attention/physiology ; *Acoustic Stimulation/methods ; Auditory Perception/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; Electrooculography/methods ; }, abstract = {In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.}, }
@article {pmid38781061, year = {2024}, author = {Chen, SY and Chang, CM and Chiang, KJ and Wei, CS}, title = {SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {2027-2037}, doi = {10.1109/TNSRE.2024.3404432}, pmid = {38781061}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Algorithms ; *Electroencephalography ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; Calibration ; Reproducibility of Results ; }, abstract = {Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.}, }
@article {pmid38781054, year = {2024}, author = {Chen, D and Huang, H and Guan, Z and Pan, J and Li, Y}, title = {An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {10}, pages = {2956-2967}, doi = {10.1109/TBME.2024.3404131}, pmid = {38781054}, issn = {1558-2531}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Young Adult ; Brain/physiology ; Algorithms ; }, abstract = {OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet.<br><br>METHOD: Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects.<br><br>MAIN RESULTS: DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% &#xb1; 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% &#xb1; 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%.<br><br>SIGNIFICANCE: An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.}, }
@article {pmid38780699, year = {2024}, author = {Ge, Q and Lock, M and Yang, X and Ding, Y and Yue, J and Zhao, N and Hu, YS and Zhang, Y and Yao, M and Zang, YF}, title = {Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T.}, journal = {Neuroinformatics}, volume = {22}, number = {4}, pages = {421-435}, pmid = {38780699}, issn = {1559-0089}, mesh = {Humans ; *Magnetic Resonance Imaging/methods/standards ; *Transcranial Magnetic Stimulation/methods/standards ; Male ; Female ; Reproducibility of Results ; Adult ; *Brain/physiology/diagnostic imaging ; Young Adult ; Brain Mapping/methods ; Image Processing, Computer-Assisted/methods ; }, abstract = {US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.}, }
@article {pmid38778312, year = {2024}, author = {Lu, J and Jin, Y and Liang, S and Wang, Q and Li, X and Li, T}, title = {Risk factors and their association network for young adults' suicidality: a cross-sectional study.}, journal = {BMC public health}, volume = {24}, number = {1}, pages = {1378}, pmid = {38778312}, issn = {1471-2458}, support = {82001440//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; 2022C03096//Key Research and Development Program of Zhejiang Province/ ; 2018B030334001//Special Foundation for Brain Research from Science and Technology Program of Guangdong/ ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence &amp; Key Project for Hangzhou Medical Disciplines/ ; }, mesh = {Humans ; Male ; Risk Factors ; Female ; Cross-Sectional Studies ; Young Adult ; China/epidemiology ; *Suicidal Ideation ; *Students/psychology/statistics &amp; numerical data ; Suicide, Attempted/statistics &amp; numerical data/psychology ; Adolescent ; Universities ; Adult ; Suicide/psychology/statistics &amp; numerical data ; Machine Learning ; }, abstract = {BACKGROUND: Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students.<br><br>METHODS: A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay.<br><br>RESULTS: Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors.<br><br>CONCLUSIONS: The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.}, }
@article {pmid38776898, year = {2024}, author = {Junqueira, B and Aristimunha, B and Chevallier, S and de Camargo, RY}, title = {A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad4f18}, pmid = {38776898}, issn = {1741-2552}, mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Algorithms ; }, abstract = {Objective:Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks. While deep learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean alignment (EA) due to its ease of use, low computational complexity, and compatibility with DL models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals.Approach:We used EA as a pre-processing step to train shared DL models with data from multiple subjects and evaluated their transferability to new subjects.Main results:Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.71%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.Significance:EA succeeds in the task of improving transfer learning performance with DL models and, could be used as a standard pre-processing technique.}, }
@article {pmid38774060, year = {2024}, author = {Fukushi, T}, title = {East Asian perspective of responsible research and innovation in neurotechnology.}, journal = {IBRO neuroscience reports}, volume = {16}, number = {}, pages = {582-597}, pmid = {38774060}, issn = {2667-2421}, abstract = {After more than half a century of research and development (R&amp;D), Brain-computer interface (BCI)-based Neurotechnology continues to progress as one of the leading technologies of the 2020 s worldwide. Various reports and academic literature in Europe and the United States (U.S.) have outlined the trends in the R&amp;D of neurotechnology and the consideration of ethical issues, and the importance of the formulation of ethical principles, guidance and industrial standards as well as the development of relevant human resources has been discussed. However, limited number studies have focused on neurotechnology R&amp;D, the dissemination of neuroethics related to the academic foundation advancing the discussion on ethical principles, guidance and standards or human resource development in the Asian region. This study fills in this gap in understanding of Eastern Asian (China, Korea and Japan) situation based on the participation in activities to develop ethical principles, guidance, and industrial standards for appropriate use of neurotechnology, in addition to literature survey and clinical registries' search investigation reflecting the trends in neurotechnology R&amp;D as well as its social implication in Asian region. The current study compared the results with the situation in Europa and the U.S. and discussed issues that need to be addressed in the future and discussed the significance and potential of corporate consortium initiatives in Japan and examples of ethics and governance activities in Asian Countries.}, }
@article {pmid38773929, year = {2024}, author = {Tang, H and Li, Y and Liao, S and Liu, H and Qiao, Y and Zhou, J}, title = {Multifunctional Conductive Hydrogel Interface for Bioelectronic Recording and Stimulation.}, journal = {Advanced healthcare materials}, volume = {13}, number = {22}, pages = {e2400562}, doi = {10.1002/adhm.202400562}, pmid = {38773929}, issn = {2192-2659}, support = {62201624//National Natural Science Foundation of China/ ; 32000939//National Natural Science Foundation of China/ ; 21775168//National Natural Science Foundation of China/ ; 22174167//National Natural Science Foundation of China/ ; 51861145202//National Natural Science Foundation of China/ ; U20A20168//National Natural Science Foundation of China/ ; 2024A1515012056//Guangdong Basic and Applied Basic Research Foundation/ ; 2019A1515111183//Guangdong Basic and Applied Basic Research Foundation/ ; RCBS20221008093310024//Shenzhen Science and Technology Program/ ; JCYJ20190807160401657//Shenzhen Research Funding Program/ ; JCYJ201908073000608//Shenzhen Research Funding Program/ ; JCYJ20150831192224146//Shenzhen Research Funding Program/ ; BR2023KF02010//Open Research Fund Program of Beijing National Research Center for Information Science and Technology/ ; 2020B1212060077//Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province/ ; }, mesh = {*Hydrogels/chemistry ; Humans ; *Electric Conductivity ; Wearable Electronic Devices ; Biocompatible Materials/chemistry ; Animals ; Electronics ; }, abstract = {The past few decades have witnessed the rapid advancement and broad applications of flexible bioelectronics, in wearable and implantable electronics, brain-computer interfaces, neural science and technology, clinical diagnosis, treatment, etc. It is noteworthy that soft and elastic conductive hydrogels, owing to their multiple similarities with biological tissues in terms of mechanics, electronics, water-rich, and biological functions, have successfully bridged the gap between rigid electronics and soft biology. Multifunctional hydrogel bioelectronics, emerging as a new generation of promising material candidates, have authentically established highly compatible and reliable, high-quality bioelectronic interfaces, particularly in bioelectronic recording and stimulation. This review summarizes the material basis and design principles involved in constructing hydrogel bioelectronic interfaces, and systematically discusses the fundamental mechanism and unique advantages in bioelectrical interfacing with the biological surface. Furthermore, an overview of the state-of-the-art manufacturing strategies for hydrogel bioelectronic interfaces with enhanced biocompatibility and integration with the biological system is presented. This review finally exemplifies the unprecedented advancement and impetus toward bioelectronic recording and stimulation, especially in implantable and integrated hydrogel bioelectronic systems, and concludes with a perspective expectation for hydrogel bioelectronics in clinical and biomedical applications.}, }
@article {pmid38771245, year = {2024}, author = {Guo, B and Mao, T and Tao, R and Fu, S and Deng, Y and Liu, Z and Wang, M and Wang, R and Zhao, W and Chai, Y and Jiang, C and Rao, H}, title = {Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {5}, pages = {}, doi = {10.1093/cercor/bhae212}, pmid = {38771245}, issn = {1460-2199}, support = {32200889//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; }, mesh = {Humans ; Male ; Female ; Adult ; *Cerebrovascular Circulation/physiology ; Reproducibility of Results ; *Rest/physiology ; *Brain/diagnostic imaging/physiology/blood supply ; Young Adult ; *Magnetic Resonance Imaging/methods ; Perfusion Imaging/methods ; Psychomotor Performance/physiology ; Circadian Rhythm/physiology ; Arousal/physiology ; }, abstract = {Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.}, }
@article {pmid38770525, year = {2024}, author = {Xu, M and Zhang, Y and Zhang, Y and Liu, X and Qing, K}, title = {EEG biomarkers analysis in different cognitive impairment after stroke: an exploration study.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1358167}, pmid = {38770525}, issn = {1664-2295}, abstract = {Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain's blood vessels, or when these vessels rupture. And, the cognitive impairment's evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.}, }
@article {pmid38769157, year = {2024}, author = {Silva, AB and Liu, JR and Metzger, SL and Bhaya-Grossman, I and Dougherty, ME and Seaton, MP and Littlejohn, KT and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF}, title = {A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.}, journal = {Nature biomedical engineering}, volume = {8}, number = {8}, pages = {977-991}, pmid = {38769157}, issn = {2157-846X}, support = {T32 GM007618/GM/NIGMS NIH HHS/United States ; T32GM007618//U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; U01 DC018671/DC/NIDCD NIH HHS/United States ; F30 DC021872/DC/NIDCD NIH HHS/United States ; DC018671-01A1//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; }, mesh = {Humans ; *Speech/physiology ; *Multilingualism ; *Electrocorticography/methods ; Motor Cortex/physiology ; Male ; Brain-Computer Interfaces ; Language ; Deep Learning ; Neural Prostheses ; }, abstract = {Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish-English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across languages, which allowed us to build a syllable classifier that generalized across a shared set of English and Spanish syllables. Transfer learning expedited training of the bilingual decoder by enabling neural data recorded in one language to improve decoding in the other language. Overall, our findings suggest shared cortical articulatory representations that persist after paralysis and enable the decoding of multiple languages without the need to train separate language-specific decoders.}, }
@article {pmid38769115, year = {2024}, author = {Komeiji, S and Mitsuhashi, T and Iimura, Y and Suzuki, H and Sugano, H and Shinoda, K and Tanaka, T}, title = {Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {11491}, pmid = {38769115}, issn = {2045-2322}, support = {20H00235//JSPS KAKENHI/ ; }, mesh = {Humans ; Female ; Male ; Adult ; *Brain-Computer Interfaces ; *Speech/physiology ; *Electrocorticography ; Speech Perception/physiology ; Young Adult ; Feasibility Studies ; Epilepsy/physiopathology ; Neural Networks, Computer ; Middle Aged ; Adolescent ; }, abstract = {Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p > 0.05 ; d = 0.07) . Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.}, }
@article {pmid38767327, year = {2024}, author = {Ferdi, AY and Ghazli, A}, title = {Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2024.2355490}, pmid = {38767327}, issn = {1476-8259}, abstract = {Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can help a rehabilitated or motor-impaired stroke patient perform certain tasks. Robust classification of these signals is an important step toward making the use of EEG more practical in many applications and less dependent on trained professionals. Deep learning methods have produced impressive results in BCI in recent years, especially with the availability of large electroencephalography (EEG) data sets. Dealing with EEG-MI signals is difficult because noise and other signal sources can interfere with the electrical amplitude of the brain, and its generalization ability is limited, so it is difficult to improve EEG classifiers. To address these issues, this paper presents a methodology based on one-dimensional convolutional neural networks (1-D CNN) for motor imagery (MI) recognition for the right hand, left hand, feet, and sedentary task. The proposed model is a lightweight model with fewer parameters and has an accuracy of 91.75%. Then, in an innovative exploitation of the four output classes, there is an idea that allows people with disabilities who are deprived of security measures, such as entering a secret code, to use the output classification, such as password codes. It is also an idea for a unique authentication system that is more secure and less vulnerable to theft or the like for a healthy person at the same time.}, }
@article {pmid38763997, year = {2024}, author = {Zheng, J and Wu, X and Xu, H}, title = {Oxytocinergic Control of a Hypothalamic Social Fear Circuitry.}, journal = {Neuroscience bulletin}, volume = {40}, number = {9}, pages = {1399-1401}, pmid = {38763997}, issn = {1995-8218}, mesh = {Animals ; Humans ; *Fear/physiology/psychology ; *Hypothalamus/physiology/metabolism ; *Oxytocin/metabolism/physiology ; Social Behavior ; Mice ; }, }
@article {pmid38763932, year = {2024}, author = {El Kaim, A and Serra, M and De Noray, H and Lallemant, A and Gobatto, C and Degos, V and Carpentier, A and Riche, M and Apra, C}, title = {Safety and practicality study of using an exoskeleton in acute neurosurgery patients.}, journal = {Acta neurochirurgica}, volume = {166}, number = {1}, pages = {221}, pmid = {38763932}, issn = {0942-0940}, mesh = {Humans ; Male ; Female ; Middle Aged ; *Exoskeleton Device ; Retrospective Studies ; Aged ; *Neurosurgical Procedures/methods ; Adult ; Early Ambulation/methods ; Patient Satisfaction ; Feasibility Studies ; }, abstract = {INTRODUCTION: Early mobilization is key in neurologically impaired persons, limiting complications and improving long-term recovery. Self-balanced exoskeletons are used in rehabilitation departments to help patients stand and walk. We report the first case series of exoskeleton use in acute neurosurgery and intensive care patients, evaluating safety, clinical feasibility and patients' satisfaction.<br><br>METHODS: We report a retrospective observational study including individuals hospitalized in the neurosurgical intensive care and neurosurgery departments. We included patients with a medical prescription for an exoskeleton session, and who met no contraindication. Patients benefited from standing sessions using a self-balanced exoskeleton (Atalante, Wandercraft, France). Patients and sessions data were collected. Safety, feasibility and adherence were evaluated.<br><br>RESULTS: Seventeen patients were scheduled for 70 standing sessions, of which 27 (39%) were completed. They were typically hospitalized for intracranial hemorrhage (74%) and presented with unilateral motor impairments, able to stand but with very insufficient weight shifting to the hemiplegic limb, requiring support (MRC 36.2&#x2009;&#xb1;&#x2009;3.70, SPB 2.0&#x2009;&#xb1;&#x2009;1.3, SPD 0.7&#x2009;&#xb1;&#x2009;0.5). The average duration of standing sessions was 16&#x2009;&#xb1;&#x2009;9&#xa0;min. The only side effect was orthostatic hypotension (18.5%), which resolved with returning to seating position. The most frequent reason for not completing a session was understaffing (75%). All patients were satisfied and expressed a desire to repeat it.<br><br>CONCLUSIONS: Physiotherapy using the exoskeleton is safe and feasible in the acute neurosurgery setting, although it requires adaptation from the staff to organize the sessions. An efficacy study is ongoing to evaluate the benefits for the patients.}, }
@article {pmid38762683, year = {2024}, author = {Fukuma, R and Majima, K and Kawahara, Y and Yamashita, O and Shiraishi, Y and Kishima, H and Yanagisawa, T}, title = {Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {595}, pmid = {38762683}, issn = {2399-3642}, support = {JPMJCR18A5//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJPR2128//MEXT | Japan Science and Technology Agency (JST)/ ; JPMXS0120330644//MEXT | Japan Science and Technology Agency (JST)/ ; JP19dm0207070//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0307103//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0207070//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0307103//Japan Agency for Medical Research and Development (AMED)/ ; JP23dm0307009//Japan Agency for Medical Research and Development (AMED)/ ; 22K15623//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 20K16465//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; }, mesh = {*Electrocorticography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Machine Learning ; Visual Perception/physiology ; Female ; Reproducibility of Results ; Adult ; Brain-Computer Interfaces ; }, abstract = {Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) features, which can be used in any machine learning algorithm. Using electrocorticographic signals recorded during various movement and visual perception tasks, the sDM features were shown to improve the decoding accuracy and computational time compared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-γ power of the signals, but with higher trial-to-trial reproducibility. The proposed sDM features enable fast, accurate, and interpretable neural decoding.}, }
@article {pmid38760478, year = {2024}, author = {Barret, N and Guillaumée, T and Rimmelé, T and Cortet, M and Mazza, S and Duclos, A and Rode, G and Lilot, M and Schlatter, S}, title = {Associations of coping and health-related behaviors with medical students' well-being and performance during objective structured clinical examination.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {11298}, pmid = {38760478}, issn = {2045-2322}, mesh = {Humans ; *Students, Medical/psychology ; *Adaptation, Psychological ; Female ; Male ; Cross-Sectional Studies ; Surveys and Questionnaires ; Adult ; Young Adult ; Health Behavior ; Clinical Competence ; Exercise/psychology ; Educational Measurement/methods ; }, abstract = {Objective structured clinical examination (OSCE) is a valid method to evaluate medical students' competencies. The present cross-sectional study aimed at determining how students' coping and health-related behaviors are associated with their psychological well-being and performance on the day of the OSCE. Fourth-year medical students answered a set of standardized questionnaires assessing their coping (BCI) and health-related behaviors before the examination (sleep PSQI, physical activity GPAQ). Immediately before the OSCE, they reported their level of instant psychological well-being on multi-dimensional visual analogue scales. OSCE performance was assessed by examiners blinded to the study. Associations were explored using multivariable linear regression models. A total of 482 students were included. Instant psychological well-being was positively associated with the level of positive thinking and of physical activity. It was negatively associated with the level of avoidance and of sleep disturbance. Furthermore, performance was negatively associated with the level of avoidance. Positive thinking, good sleep quality, and higher level of physical activity were all associated with improved well-being before the OSCE. Conversely, avoidance coping behaviors seem to be detrimental to both well-being and OSCE performance. The recommendation is to pay special attention to students who engage in avoidance and to consider implementing stress management programs.Clinical trial: The study protocol was registered on clinicaltrial.gov NCT05393206, date of registration: 11 June 2022.}, }
@article {pmid38760018, year = {2024}, author = {Jiang, J and Li, C and Chen, C and Shi, C and Song, J}, title = {Tunable and Reversible Adhesive of Liquid Metal Ferrofluid Pillars for Magnetically Actuated Noncontact Transfer Printing.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {36}, number = {29}, pages = {e2314004}, doi = {10.1002/adma.202314004}, pmid = {38760018}, issn = {1521-4095}, support = {U21A20502//National Natural Science Foundation of China/ ; 12302214//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; U20A6001//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province/ ; }, abstract = {Transfer printing techniques based on tunable and reversible adhesives enable the heterogeneous integration of materials in desired layouts and are essential for developing both existing and envisioned electronic systems. Here, a novel tunable and reversible adhesive of liquid metal ferrofluid pillars for developing an efficient magnetically actuated noncontact transfer printing is reported. The liquid metal ferrofluid pillars offer the appealing advantages of gentle contact force by minimizing the preload effect and exceptional shape adaptability by maximizing the interfacial contact area due to their inherent fluidity, thus enabling a reliable damage-free pickup. Moreover, the liquid metal ferrofluid pillars harness the rapid stiffness increase and shape change with the magnetic field, generating an instantaneous ejection force to achieve a receiver-independent noncontact printing. Demonstrations of the adhesive of liquid metal ferrofluid pillars in transfer printing of diverse objects with different shapes, materials and dimensions onto various substrates illustrate its great potential in deterministic assembly.}, }
@article {pmid38758616, year = {2024}, author = {Xu, G and Wang, Z and Hu, H and Zhao, X and Li, R and Zhou, T and Xu, T}, title = {Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {8}, pages = {4565-4576}, doi = {10.1109/JBHI.2024.3402324}, pmid = {38758616}, issn = {2168-2208}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Machine Learning ; *Algorithms ; Brain/physiology ; }, abstract = {Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.}, }
@article {pmid38758613, year = {2024}, author = {Zheng, B and Li, Y and Xu, G and Wang, G and Zheng, Y}, title = {Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1994-2004}, doi = {10.1109/TNSRE.2024.3402545}, pmid = {38758613}, issn = {1558-0210}, mesh = {Humans ; *Forearm/physiology ; *Electromyography/methods ; *Fingers/physiology ; Male ; *Motor Neurons/physiology ; Rotation ; *Algorithms ; Young Adult ; Adult ; Female ; Muscle, Skeletal/physiology ; Action Potentials/physiology ; Brain-Computer Interfaces ; Reproducibility of Results ; Muscle Contraction/physiology ; Movement/physiology ; }, abstract = {Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ([Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.}, }
@article {pmid38757738, year = {2024}, author = {Wu, Y and Wang, L and Yan, M and Wang, X and Liao, X and Zhong, C and Ke, D and Lu, Y}, title = {Poly(3,4-Ethylenedioxythiophene)/Functional Gold Nanoparticle films for Improving the Electrode-Neural Interface.}, journal = {Advanced healthcare materials}, volume = {13}, number = {23}, pages = {e2400836}, doi = {10.1002/adhm.202400836}, pmid = {38757738}, issn = {2192-2659}, support = {T2122021//National Natural Science Foundation of China/ ; T2250710685//National Natural Science Foundation of China/ ; 32071035//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2022ZD0209500//STI 2030-Major Projects/ ; 2019DP173024//CAS Key Laboratory of Brain Connectome and Manipulation/ ; 2017B030301017//Guangdong Key Laboratory of Brain Connectome/ ; JCYJ20220818100805013//Shenzhen Key Basic Research Project/ ; 2023M743683//China Postdoctoral Science Foundation/ ; 2024A1515010645//Guangdong Basic and Applied Basic Research Foundation/ ; }, mesh = {*Gold/chemistry ; Animals ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Polymers/chemistry ; *Metal Nanoparticles/chemistry ; Mice ; Neurons/metabolism ; Electrodes, Implanted ; Male ; }, abstract = {Implantable neural electrodes are indispensable tools for recording neuron activity, playing a crucial role in neuroscience research. However, traditional neural electrodes suffer from limited electrochemical performance, compromised biocompatibility, and tentative stability, posing great challenges for reliable long-term studies in free-moving animals. In this study, a novel approach employing a hybrid film composed of poly(3,4-ethylenedioxythiophene)/functional gold nanoparticles (PEDOT/3-MPA-Au) to improve the electrode-neural interface is presented. The deposited PEDOT/3-MPA-Au demonstrates superior cathodal charge storage capacity, reduced electrochemical impedance, and remarkable electrochemical and mechanical stability. Upon implantation into the cortex of mice for a duration of 12 weeks, the modified electrodes exhibit notably decreased levels of glial fibrillary acidic protein and increased neuronal nuclei immunostaining compared to counterparts utilizing poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate). Additionally, the PEDOT/3-MPA-Au modified electrodes consistently capture high-quality, stable long-term electrophysiological signals in vivo, enabling continuous recording of target neurons for up to 16 weeks. This innovative modification strategy offers a promising solution for fabricating low-impedance, tissue-friendly, and long-term stable neural interfaces, thereby addressing the shortcomings of conventional neural electrodes. These findings mark a significant advancement toward the development of more reliable and efficacious neural interfaces, with broad implications for both research and clinical applications.}, }
@article {pmid38757187, year = {2024}, author = {Cunlin, H and Ye, Y and Nenggang, X}, title = {Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad48bc}, pmid = {38757187}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Supervised Machine Learning ; Pattern Recognition, Automated/methods ; }, abstract = {Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.}, }
@article {pmid38754326, year = {2024}, author = {Yang, SH and Huang, CJ and Huang, JS}, title = {Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation.}, journal = {Computer methods and programs in biomedicine}, volume = {251}, number = {}, pages = {108208}, doi = {10.1016/j.cmpb.2024.108208}, pmid = {38754326}, issn = {1872-7565}, mesh = {*Brain-Computer Interfaces ; Animals ; Algorithms ; Calibration ; Humans ; Signal Processing, Computer-Assisted ; Movement ; }, abstract = {BACKGROUND AND OBJECTIVE: Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load.<br><br>METHODS: This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement.<br><br>RESULTS: Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable.<br><br>CONCLUSIONS: This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.}, }
@article {pmid38753110, year = {2024}, author = {Serrano-Amenos, C and Hu, F and Wang, PT and Heydari, P and Do, AH and Nenadic, Z}, title = {Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain-Computer Interface.}, journal = {Annals of biomedical engineering}, volume = {52}, number = {8}, pages = {2269-2281}, pmid = {38753110}, issn = {1573-9686}, support = {1646275//National Science Foundation/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Computer Simulation ; Prostheses and Implants ; Electric Power Supplies ; }, abstract = {This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain-computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 ∘ C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU-an integral part of a fully-implantable BD-BCI system.}, }
@article {pmid38751845, year = {2024}, author = {Lian, J and Dias Pereira, J}, title = {Editorial: IoT, UAV, BCI empowered deep learning models in precision agriculture.}, journal = {Frontiers in plant science}, volume = {15}, number = {}, pages = {1399753}, doi = {10.3389/fpls.2024.1399753}, pmid = {38751845}, issn = {1664-462X}, }
@article {pmid38750661, year = {2024}, author = {Eck, JL and Hernández Hassan, L and Comita, LS}, title = {Intraspecific plant-soil feedback in four tropical tree species is inconsistent in a field experiment.}, journal = {American journal of botany}, volume = {111}, number = {12}, pages = {e16331}, pmid = {38750661}, issn = {1537-2197}, mesh = {*Trees/physiology ; *Seedlings/growth &amp; development/genetics/physiology ; *Tropical Climate ; Panama ; Soil Microbiology ; Soil ; Species Specificity ; Seasons ; }, abstract = {PREMISE: Soil microbes can influence patterns of diversity in plant communities via plant-soil feedbacks. Intraspecific plant-soil feedbacks occur when plant genotype leads to variations in soil microbial composition, resulting in differences in the performance of seedlings growing near their maternal plants versus seedlings growing near nonmaternal conspecific plants. How consistently such intraspecific plant-soil feedbacks occur in natural plant communities is unclear, especially in variable field conditions.<br><br>METHODS: In an in situ experiment with four native tree species on Barro Colorado Island (BCI), Panama, seedlings of each species were transplanted beneath their maternal tree or another conspecific tree in the BCI forest. Mortality and growth were assessed at the end of the wet season (~4 months post-transplant) and at the end of the experiment (~7 months post-transplant).<br><br>RESULTS: Differences in seedling performance among field treatments were inconsistent among species and eroded over time. Effects of field environment were detected at the end of the wet season in two of the four species: Virola surinamensis seedlings had higher survival beneath their maternal tree than other conspecific trees, while seedling survival of Ormosia macrocalyx was higher under other conspecific trees. However, these differences were gone by the end of the experiment.<br><br>CONCLUSIONS: Our results suggest that intraspecific plant-soil feedbacks may not be consistent in the field for tropical tree species and may have a limited role in determining seedling performance in tropical tree communities. Future studies are needed to elucidate the environmental and genetic factors that determine the incidence and direction of intraspecific plant-soil feedbacks in plant communities.}, }
@article {pmid38750479, year = {2024}, author = {Yan, N and Hu, S}, title = {The safety and efficacy of escitalopram and sertraline in post-stroke depression: a randomized controlled trial.}, journal = {BMC psychiatry}, volume = {24}, number = {1}, pages = {365}, pmid = {38750479}, issn = {1471-244X}, mesh = {Humans ; *Sertraline/therapeutic use/adverse effects ; Male ; Aged ; Female ; Middle Aged ; *Stroke/complications/drug therapy ; Adult ; Aged, 80 and over ; *Escitalopram/therapeutic use/adverse effects ; *Activities of Daily Living ; Depression/drug therapy/etiology ; Treatment Outcome ; Selective Serotonin Reuptake Inhibitors/therapeutic use/adverse effects ; Psychiatric Status Rating Scales ; Antidepressive Agents/therapeutic use/adverse effects ; Citalopram/therapeutic use/adverse effects ; }, abstract = {OBJECTIVES: This study aims to evaluate the safety and efficacy of escitalopram and sertraline in post-stroke depression (PSD) patients, to provide more reliable therapeutics for cardiovascular and psychiatric clinical practice.<br><br>METHODS: We recruited 60 patients (aged 40-89 years old) with an ICD-10 diagnosis of PSD, who were then randomly assigned to two groups and treated with flexible doses of escitalopram (10 to 20 mg/day, n&#x2009;=&#x2009;30) or sertraline (50 to 200 mg/day, n&#x2009;=&#x2009;30) for consecutive 8 weeks, respectively. The 24-item Hamilton Depression Rating Scale (HAMD-24), the 14-item Hamilton Anxiety Rating Scale (HAMA-14), the Treatment Emergent Symptom Scale (TESS), the Montreal Cognitive Assessment Scale (MOCA), and the Activity of Daily Living scale (ADL) were used to assess patients before, during, and after treatment for depression, anxiety, adverse effects, cognitive function, and daily living activities. Repeated measures ANOVA, the Mann-Whitney U test, the chi-square test (&#x3c7;[2]), or Fisher's exact test was employed to assess baseline demographics, response rate, adverse effects rate, and changes in other clinical variables.<br><br>RESULTS: Significant reduction in HAMD-24 and HAMA-14 scores was evaluated at baseline, as well as 1, 3, 4, 6, and 8&#xa0;weeks of drug intervention (p&#x2009;<&#x2009;0.01). There was a significant group difference in post-treatment HAMD-24 scores (p&#x2009;<&#x2009;0.05), but no difference was observed in HAMA-14 scores (p&#x2009;>&#x2009;0.05). Further analysis showed a significant variance in the HAMD-24 scores between the two groups at the end of the first week (p&#x2009;<&#x2009;0.01). The incidence of adverse effects in both patient groups was mild, but there was a statistically significant difference between the two groups (p&#x2009;<&#x2009;0.05). The improvement in cognitive function and the recovery of daily living abilities were comparable between both groups (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: Escitalopram and sertraline showed comparable efficacy for anxiety symptoms, cognitive function, and daily living abilities in PSD patients. In addition, escitalopram was more appropriate for alleviating depressive symptoms. To validate the conclusion, trials with a larger sample size are in demand in the future. The registration number is ChiCTR1800017373.}, }
@article {pmid38749619, year = {2024}, author = {Hahn, RT and Wilkoff, BL and Kodali, S and Birgersdotter-Green, UM and Ailawadi, G and Addetia, K and Andreas, M and Auricchio, A and Ehlert, F and George, I and Gupta, A and Harrison, R and Ho, EC and Kusumoto, F and Latib, A and O'Gara, P and Patton, KK and Pinney, S and Zeitler, EP and Mack, MJ and Leon, MB and Epstein, LM and , }, title = {Managing Implanted Cardiac Electronic Devices in Patients With Severe Tricuspid Regurgitation: JACC State-of-the-Art Review.}, journal = {Journal of the American College of Cardiology}, volume = {83}, number = {20}, pages = {2002-2014}, doi = {10.1016/j.jacc.2024.02.045}, pmid = {38749619}, issn = {1558-3597}, mesh = {Humans ; *Tricuspid Valve Insufficiency/surgery ; *Defibrillators, Implantable/adverse effects ; *Pacemaker, Artificial/adverse effects ; Heart Valve Prosthesis Implantation/methods/adverse effects ; Severity of Illness Index ; }, abstract = {Orthotopic transcatheter tricuspid valve replacement (TTVR) devices have been shown to be highly effective in reducing tricuspid regurgitation (TR), and interest in this therapy is growing with the recent commercial approval of the first orthotopic TTVR. Recent TTVR studies report preexisting cardiac implantable electronic device (CIED) transvalvular leads in ∼35% of patients, with entrapment during valve implantation. Concerns have been raised regarding the safety of entrapping leads and counterbalanced against the risks of transvenous lead extraction (TLE) when indicated. This Heart Valve Collaboratory consensus document attempts to define the patient population with CIED lead-associated or lead-induced TR, describe the risks of lead entrapment during TTVR, delineate the risks and benefits of TLE in this setting, and develop a management algorithm for patients considered for TTVR. An electrophysiologist experienced in CIED management should be part of the multidisciplinary heart team and involved in shared decision making.}, }
@article {pmid38746479, year = {2024}, author = {Datta, P and Kaur, A and Sassi, N and Gulzar, Y and Jaziri, W}, title = {An evaluation of intelligent and immersive digital applications in eliciting cognitive states in humans through the utilization of Emotiv Insight.}, journal = {MethodsX}, volume = {12}, number = {}, pages = {102748}, pmid = {38746479}, issn = {2215-0161}, abstract = {The amalgamation of Virtual Reality (VR) and Artificial Intelligence (AI) results in the development of many promising applications that are helpful for society in many aspects. This research was done to study the effect of immersive and non-immersive applications on user's psychological parameters. In this paper, an intelligent, interactive, and immersive digital application was designed, and the various psychological parameters of users while using the application were analyzed through the brain computer interactive device, Emotiv. The impact of these robust and immersive applications on the emotions of human beings was analyzed. According to the observations, the stress and relaxation levels are getting minimally affected, whereas the engagement levels are high for an immersive application rather than a non-immersive application. Hence, it can be concluded that immersive applications put users "in" the application environment and provide a near-realistic experience by blurring the line between the real and virtual worlds. Deeper immersion results from the increased sensation of presence, which in turn is helpful in increasing motivation and emotional investment.•This paper demonstrates the implementation of the A* algorithm within the Unity 3D Game Engine to develop an intelligent digital application, fostering interactivity and depth.•This paper explores the integration of VR technology to transform the digital application into an immersive and interactive experience, enhancing user engagement and realism.•This paper investigates the utilization of the Emotiv Insight device to analyze cognitive parameters within both non-immersive AI-based and immersive AI &amp; VR-based applications, providing insights into user experiences.}, }
@article {pmid38745805, year = {2024}, author = {Livanis, E and Voultsos, P and Vadikolias, K and Pantazakos, P and Tsaroucha, A}, title = {Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?.}, journal = {Cureus}, volume = {16}, number = {4}, pages = {e58243}, pmid = {38745805}, issn = {2168-8184}, abstract = {In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs) are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.}, }
@article {pmid38745103, year = {2024}, author = {Silva, AB and Littlejohn, KT and Liu, JR and Moses, DA and Chang, EF}, title = {The speech neuroprosthesis.}, journal = {Nature reviews. Neuroscience}, volume = {25}, number = {7}, pages = {473-492}, pmid = {38745103}, issn = {1471-0048}, support = {F30 DC021872/DC/NIDCD NIH HHS/United States ; U01 DC018671/DC/NIDCD NIH HHS/United States ; }, mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; Neural Prostheses ; Animals ; }, abstract = {Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.}, }
@article {pmid38744976, year = {2024}, author = {Braun, JM and Fauth, M and Berger, M and Huang, NS and Simeoni, E and Gaeta, E and Rodrigues do Carmo, R and García-Betances, RI and Arredondo Waldmeyer, MT and Gail, A and Larsen, JC and Manoonpong, P and Tetzlaff, C and Wörgötter, F}, title = {A brain machine interface framework for exploring proactive control of smart environments.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {11054}, pmid = {38744976}, issn = {2045-2322}, support = {732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; }, mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; Brain/physiology ; Macaca mulatta ; }, abstract = {Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.}, }
@article {pmid38744836, year = {2024}, author = {Fu, Y and Guo, T and Zheng, J and He, J and Shen, M and Chen, H}, title = {Children exhibit superior memory for attended but outdated information compared to adults.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {4058}, pmid = {38744836}, issn = {2041-1723}, mesh = {Humans ; Female ; Male ; Adult ; *Attention/physiology ; Child ; *Memory/physiology ; Young Adult ; Cognition/physiology ; Child, Preschool ; }, abstract = {Research on the development of cognitive selectivity predominantly focuses on attentional selection. The present study explores another facet of cognitive selectivity-memory selection-by examining the ability to filter attended yet outdated information in young children and adults. Across five experiments involving 130 children and 130 adults, participants are instructed to use specific information to complete a task, and then unexpectedly asked to report this information in a surprise test. The results consistently demonstrate a developmental reversal-like phenomenon, with children outperforming adults in reporting this kind of attended yet outdated information. Furthermore, we provide evidence against the idea that the results are due to different processing strategies or attentional deployments between adults and children. These results suggest that the ability of memory selection is not fully developed in young children, resulting in their inefficient filtering of attended yet outdated information that is not required for memory retention.}, }
@article {pmid38744273, year = {2024}, author = {Cui, Q and Liu, Z and Bai, G}, title = {Friend or foe: The role of stress granule in neurodegenerative disease.}, journal = {Neuron}, volume = {112}, number = {15}, pages = {2464-2485}, doi = {10.1016/j.neuron.2024.04.025}, pmid = {38744273}, issn = {1097-4199}, mesh = {Humans ; *Neurodegenerative Diseases/metabolism ; Animals ; *Stress Granules/metabolism ; Cytoplasmic Granules/metabolism ; }, abstract = {Stress granules (SGs) are dynamic membraneless organelles that form in response to cellular stress. SGs are predominantly composed of RNA and RNA-binding proteins that assemble through liquid-liquid phase separation. Although the formation of SGs is considered a transient and protective response to cellular stress, their dysregulation or persistence may contribute to various neurodegenerative diseases. This review aims to provide a comprehensive overview of SG physiology and pathology. It covers the formation, composition, regulation, and functions of SGs, along with their crosstalk with other membrane-bound and membraneless organelles. Furthermore, this review discusses the dual roles of SGs as both friends and foes in neurodegenerative diseases and explores potential therapeutic approaches targeting SGs. The challenges and future perspectives in this field are also highlighted. A more profound comprehension of the intricate relationship between SGs and neurodegenerative diseases could inspire the development of innovative therapeutic interventions against these devastating diseases.}, }
@article {pmid38744056, year = {2024}, author = {Qian, D and Zeng, H and Cheng, W and Liu, Y and Bikki, T and Pan, J}, title = {NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model.}, journal = {Computer methods and programs in biomedicine}, volume = {251}, number = {}, pages = {108213}, doi = {10.1016/j.cmpb.2024.108213}, pmid = {38744056}, issn = {1872-7565}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Brain/diagnostic imaging/physiology ; Algorithms ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Evoked Potentials, Visual/physiology ; }, abstract = {BACKGROUND AND OBJECTIVE: Brain-Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity.<br><br>METHODS: In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features.<br><br>RESULTS: We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods.<br><br>CONCLUSIONS: The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.}, }
@article {pmid38740991, year = {2024}, author = {}, title = {Brain-machine-interface device translates internal speech into text.}, journal = {Nature human behaviour}, volume = {8}, number = {6}, pages = {1014-1015}, pmid = {38740991}, issn = {2397-3374}, mesh = {*Brain-Computer Interfaces ; *Speech Disorders/etiology/rehabilitation ; *Verbal Behavior/physiology ; Humans ; Action Potentials ; Neurons/physiology ; Parietal Lobe/cytology/physiology ; Quadriplegia/complications ; *Writing ; }, }
@article {pmid38740984, year = {2024}, author = {Wandelt, SK and Bjånes, DA and Pejsa, K and Lee, B and Liu, C and Andersen, RA}, title = {Representation of internal speech by single neurons in human supramarginal gyrus.}, journal = {Nature human behaviour}, volume = {8}, number = {6}, pages = {1136-1149}, pmid = {38740984}, issn = {2397-3374}, support = {U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; U01NS098975//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; *Parietal Lobe/physiology/physiopathology ; Adult ; Neurons/physiology ; Quadriplegia/physiopathology ; Female ; Somatosensory Cortex/physiology/physiopathology ; Speech Perception/physiology ; }, abstract = {Speech brain-machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.}, }
@article {pmid38740844, year = {2024}, author = {Mercier, M and Pepi, C and Carfi-Pavia, G and De Benedictis, A and Espagnet, MCR and Pirani, G and Vigevano, F and Marras, CE and Specchio, N and De Palma, L}, title = {The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {10887}, pmid = {38740844}, issn = {2045-2322}, support = {PE0000006//This study was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) - A Multiscale integrated approach to the study of the nervous system in health and disease/ ; 202205//Precision medicine in epilepsy: definition of a predictive model of epilepsy outcome and creation of an automated tool based on machine learning." Ricerca 5x1000 Bambino Gesu' Children' Hospital. Project code: 202205/ ; }, mesh = {Humans ; *Electroencephalography/methods ; Child ; *Machine Learning ; Female ; Male ; Child, Preschool ; Adolescent ; Epilepsy/surgery/physiopathology/diagnosis ; Neural Networks, Computer ; Treatment Outcome ; Infant ; Sleep/physiology ; }, abstract = {Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.}, }
@article {pmid38740778, year = {2024}, author = {Wang, J and Yang, Q and Liu, X and Li, J and Wen, YL and Hu, Y and Xu, TL and Duan, S and Xu, H}, title = {The basal forebrain to lateral habenula circuitry mediates social behavioral maladaptation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {4013}, pmid = {38740778}, issn = {2041-1723}, support = {32125018, 32071005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171079//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Habenula/physiology ; Male ; *Fear/physiology ; *Basal Forebrain/physiology/metabolism ; Mice ; *Neurons/physiology/metabolism ; Optogenetics ; Mice, Inbred C57BL ; Social Behavior ; Behavior, Animal/physiology ; Neural Pathways/physiology ; Glutamic Acid/metabolism ; Conditioning, Classical/physiology ; }, abstract = {Elucidating the neural basis of fear allows for more effective treatments for maladaptive fear often observed in psychiatric disorders. Although the basal forebrain (BF) has an essential role in fear learning, its function in fear expression and the underlying neuronal and circuit substrates are much less understood. Here we report that BF glutamatergic neurons are robustly activated by social stimulus following social fear conditioning in male mice. And cell-type-specific inhibition of those excitatory neurons largely reduces social fear expression. At the circuit level, BF glutamatergic neurons make functional contacts with the lateral habenula (LHb) neurons and these connections are potentiated in conditioned mice. Moreover, optogenetic inhibition of BF-LHb glutamatergic pathway significantly reduces social fear responses. These data unravel an important function of the BF in fear expression via its glutamatergic projection onto the LHb, and suggest that selective targeting BF-LHb excitatory circuitry could alleviate maladaptive fear in relevant disorders.}, }
@article {pmid38738941, year = {2024}, author = {Ramezani, Z and André, V and Khizroev, S}, title = {Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.}, journal = {Biointerphases}, volume = {19}, number = {3}, pages = {}, doi = {10.1116/5.0199163}, pmid = {38738941}, issn = {1559-4106}, mesh = {*Neurons/physiology/drug effects ; Nanoparticles/chemistry ; Humans ; Models, Neurological ; Action Potentials/drug effects/physiology ; Magnetic Fields ; }, abstract = {This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.}, }
@article {pmid38738283, year = {2024}, author = {Xue, R and Li, X and Deng, W and Liang, C and Chen, M and Chen, J and Liang, S and Wei, W and Zhang, Y and Yu, H and Xu, Y and Guo, W and Li, T}, title = {Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.}, journal = {Psychological medicine}, volume = {54}, number = {11}, pages = {3036-3043}, doi = {10.1017/S0033291724001132}, pmid = {38738283}, issn = {1469-8978}, mesh = {Humans ; *Bipolar Disorder/physiopathology ; *Schizophrenia/physiopathology ; *Depressive Disorder, Major/physiopathology ; Male ; *Electroencephalography ; Female ; Adult ; Cross-Sectional Studies ; Middle Aged ; *Brain/physiopathology ; Young Adult ; }, abstract = {BACKGROUND: Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.<br><br>METHODS: This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.<br><br>RESULTS: Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.<br><br>CONCLUSIONS: Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.}, }
@article {pmid38738213, year = {2023}, author = {Fan, C and Hahn, N and Kamdar, F and Avansino, D and Wilson, GH and Hochberg, L and Shenoy, KV and Henderson, JM and Willett, FR}, title = {Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.}, journal = {Advances in neural information processing systems}, volume = {36}, number = {}, pages = {42258-42270}, pmid = {38738213}, issn = {1049-5258}, support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 EB028171/EB/NIBIB NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.}, }
@article {pmid38736974, year = {2024}, author = {Poo, MM}, title = {China's new ethical guidelines for the use of brain-computer interfaces.}, journal = {National science review}, volume = {11}, number = {4}, pages = {nwae154}, doi = {10.1093/nsr/nwae154}, pmid = {38736974}, issn = {2053-714X}, }
@article {pmid38732846, year = {2024}, author = {Kawaguchi, T and Ono, K and Hikawa, H}, title = {Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {9}, pages = {}, pmid = {38732846}, issn = {1424-8220}, support = {JP20K11999//Japan Society for the Promotion of Science/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Gestures ; *Brain-Computer Interfaces ; Face/physiology ; Algorithms ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted ; Brain/physiology ; Male ; }, abstract = {Brain-computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain's status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.}, }
@article {pmid38729914, year = {2024}, author = {Kelly, AR and Glover, DJ}, title = {Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function.}, journal = {ACS applied bio materials}, volume = {7}, number = {6}, pages = {3605-3628}, doi = {10.1021/acsabm.4c00435}, pmid = {38729914}, issn = {2576-6422}, mesh = {Humans ; *Biocompatible Materials/chemistry ; Brain-Computer Interfaces ; }, abstract = {Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.}, }
@article {pmid38729262, year = {2024}, author = {Zhang, Y and Wu, ZY}, title = {Chinese patients with adult onset leukodystrophy caused by CST3 variants.}, journal = {Journal of genetics and genomics = Yi chuan xue bao}, volume = {51}, number = {7}, pages = {778-780}, doi = {10.1016/j.jgg.2024.05.002}, pmid = {38729262}, issn = {1673-8527}, mesh = {Humans ; Age of Onset ; China ; *East Asian People ; Mutation ; *Hereditary Central Nervous System Demyelinating Diseases/genetics ; *Cystatin C/genetics ; }, }
@article {pmid38728121, year = {2025}, author = {Hu, Z and Zhou, Z and Lyu, H}, title = {A Power-and-Area-Efficient Channel-Interleaved Neural Signal Processor for Wireless Brain-Computer Interfaces With Unsupervised Spike Sorting.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {19}, number = {1}, pages = {108-119}, doi = {10.1109/TBCAS.2024.3395353}, pmid = {38728121}, issn = {1940-9990}, mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; *Signal Processing, Computer-Assisted/instrumentation ; Humans ; Algorithms ; *Action Potentials/physiology ; }, abstract = {Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neuroscience research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-µW power consumption and occupies 0.0057 mm[2] for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.}, }
@article {pmid38727216, year = {2024}, author = {Alruwaili, R and Alanazi, F and Alrashidi, A and Hazazi, M and Alenezi, M}, title = {Comparative Analysis of Silicone Tube Intubation Versus Probing and Balloon Dilation for Congenital Nasolacrimal Duct Obstruction: A Systematic Review and Meta-Analysis.}, journal = {The Journal of craniofacial surgery}, volume = {35}, number = {4}, pages = {1114-1119}, doi = {10.1097/SCS.0000000000010273}, pmid = {38727216}, issn = {1536-3732}, mesh = {Humans ; Infant ; Dilatation/methods/instrumentation ; *Intubation/instrumentation ; *Lacrimal Duct Obstruction/congenital/therapy ; Nasolacrimal Duct/surgery ; *Silicones ; Treatment Outcome ; Child, Preschool ; }, abstract = {OBJECTIVE: Congenital nasolacrimal duct obstruction (CNLDO) is a pediatric disorder with a wide range of pathology. If untreated, the condition may end up with serious complications. Multiple treatment options for CNLDO exist throughout the literature, and there is an ongoing debate on the best intervention for each disease subgroup and the best timing of such interventions. This study compares the success and failure rates of silicone tube intubation (STI) against probing and balloon dilation (BD).<br><br>METHODS: The authors searched the literature for relevant articles using PubMed, Scopus, web of Science, and Cochrane Library until January 2024. Using RevMan 5.4, the authors compared STI's success and failure rates to probing and BD using risk ratios (RRs) and a random-effect model. In addition, the complication rate of monocanalicular intubation (MCI) versus bicanalicular intubation (BCI) was investigated. The authors used the leave-one-out method to check for influential studies and to resolve heterogeneity.<br><br>RESULTS: The screening process resulted in 23 eligible articles for inclusion in the authors' review. Silicone tube intubation had a higher chance of resolving the symptoms of CNLDO than probing (RR = 1.11; 95% CI: 1.04, 1.20; P = 0.004) while having less risk of surgical failure (RR = 0.48; 95% CI: 0.30, 0.76; P = 0.002]. Monocanalicular intubation showed no statistically significant difference when compared with BCI in terms of surgical success and failure; however, MCI had a lower risk of complications (RR = 0.68; 95% CI: 0.48, 0.97; P = 0.04). In addition, STI did not demonstrate any significant difference from BD.<br><br>CONCLUSION: There was no significant difference in success/failure between MCI and BCI; monocanalicular had fewer complications. Silicone tube intubation did better in terms of surgical success than probing, especially in children over 12 months, suggesting that it is the preferred intervention for older patients with CNLDO.}, }
@article {pmid38727014, year = {2024}, author = {Zhao, C and Jiang, R and Bustillo, J and Kochunov, P and Turner, JA and Liang, C and Fu, Z and Zhang, D and Qi, S and Calhoun, VD}, title = {Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia.}, journal = {Human brain mapping}, volume = {45}, number = {7}, pages = {e26694}, pmid = {38727014}, issn = {1097-0193}, support = {R01 MH118695/MH/NIMH NIH HHS/United States ; R01MH118695/NH/NIH HHS/United States ; }, mesh = {Humans ; *Schizophrenia/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging ; Male ; Adult ; Female ; *Connectome/methods ; *Cognitive Dysfunction/diagnostic imaging/physiopathology ; Cohort Studies ; *Nerve Net/diagnostic imaging/physiopathology ; Young Adult ; Middle Aged ; }, abstract = {Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.}, }
@article {pmid38726180, year = {2024}, author = {Al-Quraishi, MS and Tan, WH and Elamvazuthi, I and Ooi, CP and Saad, NM and Al-Hiyali, MI and Karim, HA and Azhar Ali, SS}, title = {Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.}, journal = {Heliyon}, volume = {10}, number = {9}, pages = {e30406}, pmid = {38726180}, issn = {2405-8440}, abstract = {Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.}, }
@article {pmid38724769, year = {2024}, author = {Li, Z and Tan, X and Li, X and Yin, L}, title = {Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {10}, pages = {2961-2973}, pmid = {38724769}, issn = {1741-0444}, support = {61971374//National Natural Science Foundation of China/ ; 62073280//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology/diagnostic imaging ; }, abstract = {Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.}, }
@article {pmid38724576, year = {2024}, author = {Saeedinia, SA and Jahed-Motlagh, MR and Tafakhori, A and Kasabov, NK}, title = {Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {10667}, pmid = {38724576}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; *Epilepsy/diagnosis/physiopathology ; *Biomarkers/analysis ; Pilot Projects ; *Migraine Disorders/diagnosis/physiopathology ; *Brain/physiopathology ; *Neural Networks, Computer ; Deep Learning ; Algorithms ; Male ; Adult ; Female ; }, abstract = {The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.}, }
@article {pmid38723895, year = {2024}, author = {Sayah Ben Aissa, NEH and Korichi, A and Lakas, A and Kerrache, CA and Calafate, CT}, title = {Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification.}, journal = {SLAS technology}, volume = {29}, number = {4}, pages = {100142}, doi = {10.1016/j.slast.2024.100142}, pmid = {38723895}, issn = {2472-6311}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; Attention/physiology ; Neural Networks, Computer ; Deep Learning ; }, abstract = {The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored. This paper aims to fill this gap by investigating the robustness of ATNs in adversarial contexts. We propose a high-performing attention-based deep learning model specifically designed for classifying Motor Imagery (MI) brain signals extracted from EEG data. Subsequently, we conduct a thorough series of experiments to assess various attack strategies targeting ATNs employed in EEG-based BCI tasks. Our analysis utilizes the widely recognized BCI Competition 2a dataset to demonstrate the effectiveness of attention mechanisms in BCI endeavors. Despite achieving commendable classification results in terms of accuracy (87.15%) and kappa score (0.8287), our findings reveal the vulnerability of attention-based models to adversarial manipulations (accuracy: 9.07%, kappa score: -0.21), highlighting the imperative for bolstering the robustness of attention architectures for EEG classification tasks.}, }
@article {pmid38723868, year = {2024}, author = {Lamba, K and Rani, S}, title = {A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare.}, journal = {Journal of neuroscience methods}, volume = {408}, number = {}, pages = {110159}, doi = {10.1016/j.jneumeth.2024.110159}, pmid = {38723868}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Magnetic Resonance Imaging/methods ; *Artificial Intelligence ; *Support Vector Machine ; *Brain/diagnostic imaging/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain.<br><br>NEW METHOD: A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions.<br><br>RESULTS: Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network.<br><br>The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare.}, }
@article {pmid38723406, year = {2024}, author = {Gong, M and Pan, C and Pan, R and Wang, X and Wang, J and Xu, H and Hu, Y and Wang, J and Jia, K and Chen, Q}, title = {Distinct patterns of monocular advantage for facial emotions in social anxiety.}, journal = {Journal of anxiety disorders}, volume = {104}, number = {}, pages = {102871}, doi = {10.1016/j.janxdis.2024.102871}, pmid = {38723406}, issn = {1873-7897}, mesh = {Humans ; Female ; Male ; *Facial Expression ; Adult ; *Emotions/physiology ; *Phobia, Social/physiopathology/psychology ; *Facial Recognition/physiology ; Young Adult ; }, abstract = {Individuals with social anxiety often exhibit atypical processing of facial expressions. Previous research in social anxiety has primarily emphasized cognitive bias associated with face processing and the corresponding abnormalities in cortico-limbic circuitry, yet whether social anxiety influences early perceptual processing of emotional faces remains largely unknown. We used a psychophysical method to investigate the monocular advantage for face perception (i.e., face stimuli are better recognized when presented to the same eye compared to different eyes), an effect that is indicative of early, subcortical processing of face stimuli. We compared the monocular advantage for different emotional expressions (neutral, angry and sad) in three groups (N = 24 per group): individuals clinically diagnosed with social anxiety disorder (SAD), individuals with high social anxiety in subclinical populations (SSA), and a healthy control (HC) group of individuals matched for age and gender. Compared to SSA and HC groups, we found that individuals with SAD exhibited a greater monocular advantage when processing neutral and sad faces. While the magnitudes of monocular advantages were similar across three groups when processing angry faces, individuals with SAD performed better in this condition when the faces were presented to different eye. The former findings suggest that social anxiety leads to an enhanced role of subcortical structures in processing nonthreatening expressions. The latter findings, on the other hand, likely reflect an enhanced cortical processing of threatening expressions in SAD group. These distinct patterns of monocular advantage indicate that social anxiety altered representation of emotional faces at various stages of information processing, starting at an early stage of the visual system.}, }
@article {pmid38722315, year = {2024}, author = {Bi, J and Gao, Y and Peng, Z and Ma, Y}, title = {Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad4914}, pmid = {38722315}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Entropy ; *Brain-Computer Interfaces ; Nonlinear Dynamics ; Algorithms ; Support Vector Machine ; Movement/physiology ; Reproducibility of Results ; }, abstract = {Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.}, }
@article {pmid38721102, year = {2024}, author = {Parikh, PM and Venniyoor, A}, title = {Neuralink and Brain-Computer Interface-Exciting Times for Artificial Intelligence.}, journal = {South Asian journal of cancer}, volume = {13}, number = {1}, pages = {63-65}, pmid = {38721102}, issn = {2278-330X}, abstract = {Purvish Mahendra ParikhBrain-computer interfaces are becoming a tangible reality, capable of significantly aiding patients in real-world scenarios. The recent approval by the U.S. Food and Drug Administration for clinical human trials of Neuralink marks a monumental stride, comparable to Mr. Armstrong's moonwalk. Numerous other companies are also pioneering innovative solutions in this domain. Presently, over 150,000 patients in the United States possess brain implants. As technology advances, it holds the potential to alleviate various conditions, notably motor paralysis, cerebral palsy, and involuntary movements.}, }
@article {pmid38718901, year = {2024}, author = {Ajrawi, SA and Rao, R and Sarkar, M}, title = {A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making.}, journal = {Journal of neuroscience methods}, volume = {408}, number = {}, pages = {110161}, doi = {10.1016/j.jneumeth.2024.110161}, pmid = {38718901}, issn = {1872-678X}, mesh = {*Brain-Computer Interfaces ; Humans ; *Decision Making/physiology ; *Brain/physiology ; Algorithms ; Electrocorticography/methods ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles.<br><br>NEW METHOD: Due to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets-specifically, dataset I and BCI contests III-using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database.<br><br>RESULTS: Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection.<br><br>The simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.}, }
@article {pmid38718788, year = {2024}, author = {Wimpff, M and Gizzi, L and Zerfowski, J and Yang, B}, title = {EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad48b9}, pmid = {38718788}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Attention/physiology ; Movement/physiology ; }, abstract = {Objective.The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact.Approach.We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms.Results.Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture.Significance.Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.}, }
@article {pmid38718785, year = {2024}, author = {Wang, X and Zhang, J and Wu, X}, title = {A feature enhanced EEG compression model using asymmetric encoding-decoding network.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad48ba}, pmid = {38718785}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Data Compression/methods ; Humans ; Wearable Electronic Devices ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {Objective.Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.Approach.Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.Main results.On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.Significance.This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.}, }
@article {pmid38716555, year = {2024}, author = {Zhang, X and Meesen, R and Swinnen, SP and Feys, H and Woolley, DG and Cheng, HJ and Wenderoth, N}, title = {Combining muscle-computer interface guided training with bihemispheric tDCS improves upper limb function in patients with chronic stroke.}, journal = {Journal of neurophysiology}, volume = {131}, number = {6}, pages = {1286-1298}, doi = {10.1152/jn.00316.2023}, pmid = {38716555}, issn = {1522-1598}, support = {G.0758.10//Vlaamse Overheid (Government of Flanders)/ ; //National Research Foundation Singapore (NRF)/ ; }, mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Stroke Rehabilitation/methods ; Middle Aged ; Aged ; *Stroke/physiopathology/therapy ; Double-Blind Method ; Upper Extremity/physiopathology ; Chronic Disease ; Cross-Over Studies ; Adult ; Recovery of Function/physiology ; }, abstract = {Transcranial direct current stimulation (tDCS) may facilitate neuroplasticity but with a limited effect when administered while patients with stroke are at rest. Muscle-computer interface (MCI) training is a promising approach for training patients with stroke even if they cannot produce overt movements. However, using tDCS to enhance MCI training has not been investigated. We combined bihemispheric tDCS with MCI training of the paretic wrist and examined the effect of this intervention in patients with chronic stroke. A crossover, double-blind, randomized trial was conducted. Twenty-six patients with chronic stroke performed MCI wrist training for three consecutive days at home while receiving either real tDCS or sham tDCS in counterbalanced order and separated by at least 8 mo. The primary outcome measure was the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) that was measured 1 wk before training, on the first training day, on the last training day, and 1 wk after training. There was neither a significant difference in the baseline FMA-UE score between groups nor between intervention periods. Patients improved 3.9 ± 0.6 points in FMA-UE score when receiving real tDCS, and 1.0 ± 0.7 points when receiving sham tDCS (P = 0.003). In addition, patients also showed continuous improvement in their motor control of the MCI tasks over the training days. Our study showed that the training paradigm could lead to functional improvement in patients with chronic stroke. We argue that appropriate MCI training in combination with bihemispheric tDCS could be a useful adjuvant for neurorehabilitation in patients with stroke.NEW &amp; NOTEWORTHY Bihemispheric tDCS combined with a novel MCI training for motor control of wrist extensor can improve upper limb function especially a training-specific effect on the wrist movement in patients with chronic stroke. The training regimen can be personalized with adjustments made daily to accommodate the functional change throughout the intervention. This demonstrates that bihemispheric tDCS with MCI training could complement conventional poststroke neurorehabilitation.}, }
@article {pmid38716289, year = {2023}, author = {Ghorbani, H and AfzalAghai, M and Soltani, S and Mottaghi, M and Tavakkoli, M and Lotfi, A}, title = {Translation, Linguistic Validation, and Cultural Adaptation of the Bladder Cancer Index (BCI) Questionnaire Into the Persian (Farsi) Language and Comparing it With WHO Quality of Life Questionnaire: An Observational Study.}, journal = {Journal of family &amp; reproductive health}, volume = {17}, number = {3}, pages = {128-135}, pmid = {38716289}, issn = {1735-8949}, abstract = {OBJECTIVE: Whether ileal conduit diversion (ICD) or orthotopic neobladder (ONB) urinary diversion provides better quality of life (QoL) is still under debate. The Bladder Cancer Index (BCI) is a specific tool for bladder cancer (BCa) patients, providing reliable results in previous studies. A validated Farsi version of the BCI concerning cultural aspects could help Farsi-speaking clinicians gain more reliable feedback on QoL following urinary diversion.<br><br>MATERIALS AND METHODS: Based on WHO suggestions, we translated the BCI questionnaire into the Persian language. Then, we performed a cross-sectional study on BCa patients who underwent ICD or ONB urinary diversion. We compared their QoL via BCI and WHO questionnaires. Chi-square and independent t-tests were used where appropriate.<br><br>RESULTS: The content validity ratio and the content validity indexes were 1 and 0.8-1.0, respectively. Of 57 participants, six patients (10.5%) were women. The ICD was performed for 38 (66.7%) and ONB diversion for 19 (33.3) participants. The mean age of ICD and ONB was 68.71 &#xb1; 7.40 and 64.28 &#xb1; 8.34 years, respectively (p-value: 0.055). In all sub-domains of BCI, except bowel habits, the mean scores were higher in the ICD group. A significant difference between ICD and ONB groups was found regarding urinary function (p-value<0.001). There was no significant difference between ICD and ONB groups in none of the domains of the WHO questionnaire.<br><br>CONCLUSION: The QoL of ICD and ONB patients did not differ significantly. Even ICD may be superior in ritual purification, while the psychological status of ONB patients was better.}, }
@article {pmid38714865, year = {2024}, author = {Webster, P}, title = {The future of brain-computer interfaces in medicine.}, journal = {Nature medicine}, volume = {30}, number = {6}, pages = {1508-1509}, doi = {10.1038/d41591-024-00031-3}, pmid = {38714865}, issn = {1546-170X}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; Brain/physiology ; }, }
@article {pmid38713969, year = {2024}, author = {Wang, Z and Li, S and Luo, J and Liu, J and Wu, D}, title = {Channel reflection: Knowledge-driven data augmentation for EEG-based brain-computer interfaces.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {176}, number = {}, pages = {106351}, doi = {10.1016/j.neunet.2024.106351}, pmid = {38713969}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Brain/physiology ; Evoked Potentials, Visual/physiology ; Event-Related Potentials, P300/physiology ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.}, }
@article {pmid38713574, year = {2024}, author = {Jin, J and Xu, R and Daly, I and Zhao, X and Wang, X and Cichocki, A}, title = {MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.}, journal = {IEEE transactions on cybernetics}, volume = {54}, number = {9}, pages = {5565-5576}, doi = {10.1109/TCYB.2024.3390805}, pmid = {38713574}, issn = {2168-2275}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Evoked Potentials/physiology ; Deep Learning ; Brain/physiology ; Algorithms ; }, abstract = {Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.}, }
@article {pmid38713331, year = {2024}, author = {Cao, HL and Meng, YJ and Wei, W and Li, T and Li, ML and Guo, WJ}, title = {Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.}, journal = {Brain imaging and behavior}, volume = {18}, number = {5}, pages = {951-960}, pmid = {38713331}, issn = {1931-7565}, support = {81571305//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Alcoholism/diagnostic imaging/pathology ; Male ; *Gray Matter/diagnostic imaging/pathology ; Adult ; Female ; *Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging/pathology ; Alcohol Abstinence ; Nerve Net/diagnostic imaging ; Middle Aged ; Neural Pathways/diagnostic imaging ; Cerebral Cortex/diagnostic imaging/pathology ; }, abstract = {While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.}, }
@article {pmid38712193, year = {2025}, author = {Menéndez, JA and Hennig, JA and Golub, MD and Oby, ER and Sadtler, PT and Batista, AP and Chase, SM and Yu, BM and Latham, PE}, title = {A theory of brain-computer interface learning via low-dimensional control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38712193}, issn = {2692-8205}, support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH121533/MH/NIMH NIH HHS/United States ; R00 MH121533/MH/NIMH NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; }, abstract = {A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.}, }
@article {pmid38712189, year = {2024}, author = {Shah, NP and Willsey, MS and Hahn, N and Kamdar, F and Avansino, DT and Fan, C and Hochberg, LR and Willett, FR and Henderson, JM}, title = {A flexible intracortical brain-computer interface for typing using finger movements.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38712189}, issn = {2692-8205}, support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; }, abstract = {Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.}, }
@article {pmid38712177, year = {2024}, author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL}, title = {A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {38712177}, support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; }, abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.}, }
@article {pmid38711277, year = {2024}, author = {Vakilipour, P and Fekrvand, S}, title = {Brain-to-brain interface technology: A brief history, current state, and future goals.}, journal = {International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience}, volume = {84}, number = {5}, pages = {351-367}, doi = {10.1002/jdn.10334}, pmid = {38711277}, issn = {1873-474X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; History, 20th Century ; History, 21st Century ; }, abstract = {A brain-to-brain interface (BBI), defined as a combination of neuroimaging and neurostimulation methods to extract and deliver information between brains directly without the need for the peripheral nervous system, is a budding communication technique. A BBI system is made up of two parts known as the brain-computer interface part, which reads a sender's brain activity and digitalizes it, and the computer-brain interface part, which writes the delivered brain activity to a receiving brain. As with other technologies, BBI systems have gone through an evolutionary process since they first appeared. The BBI systems have been employed for numerous purposes, including rehabilitation for post-stroke patients, communicating with patients suffering from amyotrophic lateral sclerosis, locked-in syndrome and speech problems following stroke. Also, it has been proposed that a BBI system could play an important role on future battlefields. This technology was not only employed for communicating between two human brains but also for making a direct communication path among different species through which motor or sensory commands could be sent and received. However, the application of BBI systems has provoked significant challenges to human rights principles due to their ability to access and manipulate human brain information. In this study, we aimed to review the brain-computer interface and computer-brain interface technologies as components of BBI systems, the development of BBI systems, applications of this technology, arising ethical issues and expectations for future use.}, }
@article {pmid38711213, year = {2024}, author = {Zhou, H and Gong, L and Su, C and Teng, B and Xi, W and Li, X and Geng, F and Hu, Y}, title = {White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers.}, journal = {Addiction biology}, volume = {29}, number = {5}, pages = {e13399}, pmid = {38711213}, issn = {1369-1600}, support = {81971245//National Natural Science Foundation of China/ ; 62077042//National Natural Science Foundation of China/ ; 2021ZD0200409//STI 2030-Major Projects/ ; //MOE Frontiers Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; //Zhejiang Province "Qianjiang Talent Program," Research of Basic Discipline for the 2.0 Base of Top-notch Students Training Program/ ; 20211033//Ministry of Education of China/ ; }, mesh = {Humans ; *White Matter/diagnostic imaging/pathology ; Male ; *Internet Addiction Disorder/diagnostic imaging/physiopathology ; Female ; *Diffusion Tensor Imaging/methods ; Adult ; Young Adult ; *Self-Control ; *Gray Matter/diagnostic imaging/pathology ; Ventral Striatum/diagnostic imaging/physiopathology/pathology ; Severity of Illness Index ; Neural Pathways/diagnostic imaging/physiopathology ; Corpus Striatum/diagnostic imaging/pathology/physiopathology ; Internet ; Frontal Lobe/diagnostic imaging/pathology/physiopathology ; }, abstract = {Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.}, }
@article {pmid38709613, year = {2024}, author = {Yang, K and Li, R and Xu, J and Zhu, L and Kong, W and Zhang, J}, title = {DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {8}, pages = {4625-4635}, doi = {10.1109/JBHI.2024.3395910}, pmid = {38709613}, issn = {2168-2208}, mesh = {Humans ; *Electroencephalography/methods ; *Fingers/physiology ; *Signal Processing, Computer-Assisted ; *Machine Learning ; Imagination/physiology ; Algorithms ; Brain-Computer Interfaces ; Adult ; Male ; Female ; }, abstract = {Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of feature-dependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.}, }
@article {pmid38709472, year = {2024}, author = {Chen, S and Liu, YJ}, title = {Microglia Suppresses Breast Cancer Brain Metastasis via a Pro-inflammatory Response.}, journal = {Neuroscience bulletin}, volume = {40}, number = {7}, pages = {1034-1036}, pmid = {38709472}, issn = {1995-8218}, mesh = {*Microglia/pathology ; *Brain Neoplasms/secondary/pathology ; *Breast Neoplasms/pathology ; Female ; Animals ; Humans ; Mice ; Inflammation/pathology ; }, }
@article {pmid38707591, year = {2024}, author = {Bridges, NR and Stickle, M and Moxon, KA}, title = {Transitioning from global to local computational strategies during brain-machine interface learning.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1371107}, pmid = {38707591}, issn = {1662-4548}, support = {R01 NS096971/NS/NINDS NIH HHS/United States ; }, abstract = {When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.}, }
@article {pmid38707359, year = {2024}, author = {Li, X and Tan, Y and Song, J and Lu, H and Bian, Y and Cai, W}, title = {Combined intravenous and intra-arterial thrombolysis in hyperacute cerebral ischemia without significant corresponding vascular occlusion/stenosis: A Preliminary investigation.}, journal = {Heliyon}, volume = {10}, number = {9}, pages = {e29998}, pmid = {38707359}, issn = {2405-8440}, abstract = {OBJECTIVE: In this study, we assessed the efficacy and safety of various thrombolytic treatment protocols in patients with hyperacute cerebral infarction.<br><br>METHODS: Patients diagnosed with acute ischemic stroke within 6&#xa0;h of symptom onset and with brain computer tomography angiography confirming the absence of major vessel stenosis or occlusion were eligible for this study. The enrolled patients were subsequently randomized into two groups: all the groups received the standard intravenous thrombolysis treatment with rt-PA (0.9&#xa0;mg/kg), and the experimental group underwent sequential intra-arterial thrombolysis treatment with alteplase (0.3&#xa0;mg/kg, with a maximum dose of 22&#xa0;mg), administered directly into the target vessel via a microcatheter. Both groups were closely monitored for changes in their National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin scale score, hemorrhage rate, all-cause mortality rate, and the rate of favorable outcomes at 90&#xa0;&#xb1;&#xa0;7 days.<br><br>RESULTS: Ninety-four participants were enrolled in this study, with both the control and experimental groups initiating intravenous injection of rt-PA at a median time of 29&#xa0;min. For the experimental group, the median time for arterial puncture was 123&#xa0;min. Baseline data for both groups were similar (P&#xa0;>&#xa0;0.05). Hemorrhagic transformation occurred in 24.47&#xa0;% (23 patients), with a lower intracranial hemorrhage rate observed in the experimental group compared to the control group (15.2&#xa0;% vs 33.3&#xa0;%, P&#xa0;<&#xa0;0.05). Asymptomatic hemorrhage rates were 8.7&#xa0;% for the experimental group and 12.5&#xa0;% for the control group, with no hemorrhage detected in other locations. Post-treatment median NIHSS scores were lower in the experimental group than in the control group (7 vs 9, P&#xa0;<&#xa0;0.05), but short-term NIHSS scores were similar (P&#xa0;>&#xa0;0.05). A higher proportion of patients in the experimental group achieved favorable outcomes compared to the control group (87.0&#xa0;% vs 43.8&#xa0;%, P&#xa0;<&#xa0;0.05).<br><br>CONCLUSION: In patients with acute ischemic stroke with an onset time of &#x2264;6&#xa0;h and no major intracranial vessel occlusion, combining rt-PA intravenous thrombolysis with intra-arterial thrombolysis via a microcatheter might yield superior functional outcomes.}, }
@article {pmid38703311, year = {2024}, author = {Soler, A and Giraldo, E and Molinas, M}, title = {EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.}, journal = {Brain informatics}, volume = {11}, number = {1}, pages = {11}, pmid = {38703311}, issn = {2198-4018}, abstract = {The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.}, }
@article {pmid38701593, year = {2024}, author = {Ma, X and Chen, W and Pei, Z and Zhang, Y and Chen, J}, title = {Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.}, journal = {Computers in biology and medicine}, volume = {175}, number = {}, pages = {108504}, doi = {10.1016/j.compbiomed.2024.108504}, pmid = {38701593}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; }, abstract = {Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.}, }
@article {pmid38700963, year = {2025}, author = {Valencia, D and Mercier, PP and Alimohammad, A}, title = {An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {19}, number = {1}, pages = {130-141}, doi = {10.1109/TBCAS.2024.3396115}, pmid = {38700963}, issn = {1940-9990}, mesh = {*Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted/instrumentation ; Neural Networks, Computer ; Humans ; *Brain/physiology ; Action Potentials/physiology ; Electroencephalography ; Animals ; }, abstract = {Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, "brain-switches" based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.}, }
@article {pmid38700806, year = {2024}, author = {Li, Y and Fang, Y and Li, K and Yang, H and Duan, S and Sun, L}, title = {Morphological Tracing and Functional Identification of Monosynaptic Connections in the Brain: A Comprehensive Guide.}, journal = {Neuroscience bulletin}, volume = {40}, number = {9}, pages = {1364-1378}, pmid = {38700806}, issn = {1995-8218}, mesh = {Animals ; *Brain/physiology ; *Synapses/physiology ; *Optogenetics/methods ; *Neural Pathways/physiology ; Humans ; Neuroanatomical Tract-Tracing Techniques/methods ; Neurons/physiology ; }, abstract = {Behavioral studies play a crucial role in unraveling the mechanisms underlying brain function. Recent advances in optogenetics, neuronal typing and labeling, and circuit tracing have facilitated the dissection of the neural circuitry involved in various important behaviors. The identification of monosynaptic connections, both upstream and downstream of specific neurons, serves as the foundation for understanding complex neural circuits and studying behavioral mechanisms. However, the practical implementation and mechanistic understanding of monosynaptic connection tracing techniques and functional identification remain challenging, particularly for inexperienced researchers. Improper application of these methods and misinterpretation of results can impede experimental progress and lead to erroneous conclusions. In this paper, we present a comprehensive description of the principles, specific operational details, and key steps involved in tracing anterograde and retrograde monosynaptic connections. We outline the process of functionally identifying monosynaptic connections through the integration of optogenetics and electrophysiological techniques, providing practical guidance for researchers.}, }
@article {pmid38700614, year = {2024}, author = {Pang, B and Peng, Y and Gao, J and Kong, W}, title = {Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {9}, pages = {2805-2824}, pmid = {38700614}, issn = {1741-0444}, support = {61971173//National Natural Science Foundation of China/ ; 2023YFE0114900//Key Technologies Research and Development Program/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix.}, }
@article {pmid38700460, year = {2024}, author = {Huang, X and Xue, Z and Zhang, D and Lee, HJ}, title = {Pinpointing Fat Molecules: Advances in Coherent Raman Scattering Microscopy for Lipid Metabolism.}, journal = {Analytical chemistry}, volume = {96}, number = {20}, pages = {7945-7958}, doi = {10.1021/acs.analchem.4c01398}, pmid = {38700460}, issn = {1520-6882}, mesh = {*Spectrum Analysis, Raman/methods ; *Lipid Metabolism ; Humans ; Lipids/chemistry/analysis ; Animals ; Microscopy/methods ; }, }
@article {pmid38699918, year = {2024}, author = {Rybář, M and Poli, R and Daly, I}, title = {Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS (2021J. Neural Eng. 18 046035).}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad417b}, pmid = {38699918}, issn = {1741-2552}, }
@article {pmid38698495, year = {2024}, author = {Eldawlatly, S}, title = {On the role of generative artificial intelligence in the development of brain-computer interfaces.}, journal = {BMC biomedical engineering}, volume = {6}, number = {1}, pages = {4}, pmid = {38698495}, issn = {2524-4426}, abstract = {Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.}, }
@article {pmid38697881, year = {2024}, author = {Chaudhary, P and Dhankhar, N and Singhal, A and Rana, KPS}, title = {A two-stage transformer based network for motor imagery classification.}, journal = {Medical engineering &amp; physics}, volume = {128}, number = {}, pages = {104154}, doi = {10.1016/j.medengphy.2024.104154}, pmid = {38697881}, issn = {1873-4030}, mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; Motor Activity/physiology ; Movement ; Neural Networks, Computer ; }, abstract = {Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.}, }
@article {pmid38697588, year = {2024}, author = {Zhang, W and Jiang, M and Teo, KAC and Bhuvanakantham, R and Fong, L and Sim, WKJ and Guo, Z and Foo, CHV and Chua, RHJ and Padmanabhan, P and Leong, V and Lu, J and Gulyás, B and Guan, C}, title = {Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.}, journal = {NeuroImage}, volume = {293}, number = {}, pages = {120629}, doi = {10.1016/j.neuroimage.2024.120629}, pmid = {38697588}, issn = {1095-9572}, mesh = {Humans ; Male ; *Magnetic Resonance Imaging/methods ; Female ; *Speech/physiology ; Adult ; *Electroencephalography/methods ; Young Adult ; Brain/physiology/diagnostic imaging ; Brain Mapping/methods ; }, abstract = {Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.}, }
@article {pmid38697587, year = {2024}, author = {Wen, X and Yang, M and Qi, S and Wu, X and Zhang, D}, title = {Automated individual cortical parcellation via consensus graph representation learning.}, journal = {NeuroImage}, volume = {293}, number = {}, pages = {120616}, doi = {10.1016/j.neuroimage.2024.120616}, pmid = {38697587}, issn = {1095-9572}, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Connectome/methods ; *Cerebral Cortex/diagnostic imaging/physiology/anatomy &amp; histology ; Machine Learning ; Female ; Male ; Image Processing, Computer-Assisted/methods ; Adult ; Algorithms ; Reproducibility of Results ; }, abstract = {Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.}, }
@article {pmid38694882, year = {2024}, author = {Polyakov, D and Robinson, PA and Muller, EJ and Shriki, O}, title = {Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.}, journal = {Frontiers in robotics and AI}, volume = {11}, number = {}, pages = {1362735}, pmid = {38694882}, issn = {2296-9144}, abstract = {We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.}, }
@article {pmid38694459, year = {2024}, author = {Uszko, JM and Eichhorn, SJ and Patil, AJ and Hall, SR}, title = {Detonation of fulminating gold produces heterogeneous gold nanoparticles.}, journal = {Nanoscale advances}, volume = {6}, number = {9}, pages = {2231-2233}, pmid = {38694459}, issn = {2516-0230}, abstract = {Fulminating gold, the first high-explosive compound to be discovered, disintegrates into a mysterious cloud of purple smoke, the nature of which has been speculated upon since its discovery in the 15th century. In this work, we show that the colour of the smoke is due to the presence of gold nanoparticles.}, }
@article {pmid38694204, year = {2024}, author = {Yao, X and Li, M and He, S and Jing, L and Li, C and Tao, J and Hui, X and Gao, F and Song, J and Chen, H and Wang, Z}, title = {Kirigami-Triggered Spoof Plasmonic Interconnects for Radiofrequency Elastronics.}, journal = {Research (Washington, D.C.)}, volume = {7}, number = {}, pages = {0367}, pmid = {38694204}, issn = {2639-5274}, abstract = {The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.}, }
@article {pmid38693175, year = {2024}, author = {Lotun, S and Lamarche, VM and Matran-Fernandez, A and Sandstrom, GM}, title = {Author Correction: People perceive parasocial relationships to be effective at fulfilling emotional needs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {9986}, doi = {10.1038/s41598-024-60558-w}, pmid = {38693175}, issn = {2045-2322}, }
@article {pmid38692704, year = {2024}, author = {Xu, K and Yang, Y and Ding, J and Wang, J and Fang, Y and Tian, H}, title = {Spatially Precise Genetic Engineering at the Electrode-Tissue Interface.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {36}, number = {29}, pages = {e2401327}, doi = {10.1002/adma.202401327}, pmid = {38692704}, issn = {1521-4095}, support = {2021ZD0202200//National Key Research and Development Program of China/ ; 2021ZD0202201//National Key Research and Development Program of China/ ; 22102040//National Natural Science Foundation of China/ ; 62371150//National Natural Science Foundation of China/ ; }, mesh = {Animals ; *PTEN Phosphohydrolase/genetics/metabolism ; Mice ; *Neurons/metabolism ; *Genetic Engineering ; RNA, Small Interfering/metabolism/genetics/chemistry ; Electrodes ; Parkinson Disease/genetics/metabolism/therapy ; Humans ; Gene Knockdown Techniques ; }, abstract = {The interface between electrodes and neural tissues plays a pivotal role in determining the efficacy and fidelity of neural activity recording and modulation. While considerable efforts have been made to improve the electrode-tissue interface, the majority of studies have primarily concentrated on the development of biocompatible neural electrodes through abiotic materials and structural engineering. In this study, an approach is presented that seamlessly integrates abiotic and biotic engineering principles into the electrode-tissue interface. Specifically, ultraflexible neural electrodes with short hairpin RNAs (shRNAs) designed to silence the expression of endogenous genes within neural tissues are combined. The system facilitates shRNA-mediated knockdown of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) and polypyrimidine tract-binding protein 1 (PTBP1), two essential genes associated in neural survival/growth and neurogenesis, within specific cell populations located at the electrode-tissue interface. Additionally, it is demonstrated that the downregulation of PTEN in neurons can result in an enlargement of neuronal cell bodies at the electrode-tissue interface. Furthermore, the system enables long-term monitoring of neuronal activities following PTEN knockdown in a mouse model of Parkinson's disease and traumatic brain injury. The system provides a versatile approach for genetically engineering the electrode-tissue interface with unparalleled precision, paving the way for the development of regenerative electronics and next-generation brain-machine interfaces.}, }
@article {pmid38692190, year = {2024}, author = {Duan, T and Wang, Z and Li, F and Doretto, G and Adjeroh, DA and Yin, Y and Tao, C}, title = {Online continual decoding of streaming EEG signal with a balanced and informative memory buffer.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {176}, number = {}, pages = {106338}, doi = {10.1016/j.neunet.2024.106338}, pmid = {38692190}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Memory/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; Algorithms ; }, abstract = {Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.}, }
@article {pmid38689713, year = {2024}, author = {Ma, D and Jin, X and Sun, S and Li, Y and Wu, X and Hu, Y and Yang, F and Tang, H and Zhu, X and Lin, P and Pan, G}, title = {Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.}, journal = {National science review}, volume = {11}, number = {5}, pages = {nwae102}, pmid = {38689713}, issn = {2053-714X}, abstract = {Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.}, }
@article {pmid38689706, year = {2024}, author = {Forenzo, D and Zhu, H and Shanahan, J and Lim, J and He, B}, title = {Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.}, journal = {PNAS nexus}, volume = {3}, number = {4}, pages = {pgae145}, pmid = {38689706}, issn = {2752-6542}, support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; T32 EB029365/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.}, }
@article {pmid38686423, year = {2024}, author = {Li, X and Wang, D and Zhang, B and Fan, C and Chen, J and Xu, M and Chen, Y}, title = {[A review on electroencephalogram based channel selection].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {2}, pages = {398-405}, pmid = {38686423}, issn = {1001-5515}, mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; *Algorithms ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Electrodes ; Event-Related Potentials, P300/physiology ; Imagination/physiology ; }, abstract = {The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.}, }
@article {pmid38686330, year = {2024}, author = {Liu, X and Gong, Y and Jiang, Z and Stevens, T and Li, W}, title = {Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1348434}, pmid = {38686330}, issn = {1662-4548}, abstract = {Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.}, }
@article {pmid38686326, year = {2024}, author = {Liu, Y and Wang, H and Sha, G and Cao, Y and Chen, Y and Chen, Y and Zhang, J and Chai, C and Fan, Q and Xia, S}, title = {The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1374948}, pmid = {38686326}, issn = {1662-4548}, abstract = {INTRODUCTION: Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear.<br><br>METHODS: In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins.<br><br>RESULTS: Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p&#x2009;<&#x2009;0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.}, }
@article {pmid38686177, year = {2024}, author = {Zhao, H and Liu, J and Shao, Y and Feng, X and Zhao, B and Sun, L and Liu, Y and Zeng, L and Li, XM and Yang, H and Duan, S and Yu, YQ}, title = {Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons.}, journal = {National science review}, volume = {11}, number = {4}, pages = {nwae082}, pmid = {38686177}, issn = {2053-714X}, abstract = {The nucleus of Darkschewitsch (ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons (NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock. Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior. Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray (lPAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part (GiV). Together, these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the lPAG to GiV, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.}, }
@article {pmid38683717, year = {2024}, author = {Lee, M and Park, HY and Park, W and Kim, KT and Kim, YH and Jeong, JH}, title = {Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1767-1778}, doi = {10.1109/TNSRE.2024.3395133}, pmid = {38683717}, issn = {1558-0210}, mesh = {Humans ; Male ; Female ; Middle Aged ; *Electroencephalography/methods ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Aged ; *Algorithms ; *Machine Learning ; Imagination/physiology ; Stroke/physiopathology/complications ; Robotics ; Adult ; Psychomotor Performance ; Ischemic Stroke/physiopathology/rehabilitation ; Imagery, Psychotherapy/methods ; }, abstract = {Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.}, }
@article {pmid38682423, year = {2024}, author = {Dawit, H and Zhao, Y and Wang, J and Pei, R}, title = {Advances in conductive hydrogels for neural recording and stimulation.}, journal = {Biomaterials science}, volume = {12}, number = {11}, pages = {2786-2800}, doi = {10.1039/d4bm00048j}, pmid = {38682423}, issn = {2047-4849}, mesh = {*Hydrogels/chemistry ; Humans ; *Electric Conductivity ; Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; Biocompatible Materials/chemistry ; Brain/physiology ; Neurons/physiology ; }, abstract = {The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.}, }
@article {pmid38682224, year = {2024}, author = {Sadeghi, S and Maleki, A}, title = {A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and Electromyogram.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {4}, pages = {73}, doi = {10.31083/j.jin2304073}, pmid = {38682224}, issn = {0219-6352}, mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual ; *Electromyography ; Humans ; *Word Processing/methods ; }, abstract = {BACKGROUND: To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR.<br><br>METHODS: In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects.<br><br>RESULTS: The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects.<br><br>CONCLUSIONS: Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.}, }
@article {pmid38682219, year = {2024}, author = {Yang, L and Ma, E and Yang, L and Li, M and Shang, Z and Wang, L and Ma, Z and Li, J}, title = {Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {4}, pages = {72}, doi = {10.31083/j.jin2304072}, pmid = {38682219}, issn = {0219-6352}, support = {GZC20232447//National Postdoctoral Researcher Program/ ; 62301496//National Natural Science Foundation of China/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; 222102310223//Key Scientific and Technological Projects of Henan Province/ ; }, mesh = {Animals ; *Columbidae/physiology ; *Flight, Animal/physiology ; Support Vector Machine ; Gamma Rhythm/physiology ; Midbrain Reticular Formation/physiology ; Male ; Behavior, Animal/physiology ; Mesencephalon/physiology ; }, abstract = {BACKGROUND: Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight.<br><br>METHODS: We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states.<br><br>RESULTS: Our findings indicate that the gamma band (80-150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80-150 Hz band and 0.89 using the fused features for state decoding.<br><br>CONCLUSIONS: Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.}, }
@article {pmid38681960, year = {2024}, author = {Sarasola-Sanz, A and Ray, AM and Insausti-Delgado, A and Irastorza-Landa, N and Mahmoud, WJ and Brötz, D and Bibián-Nogueras, C and Helmhold, F and Zrenner, C and Ziemann, U and López-Larraz, E and Ramos-Murguialday, A}, title = {A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {12}, number = {}, pages = {1330330}, pmid = {38681960}, issn = {2296-4185}, abstract = {Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control. Nevertheless, determining the distribution of control between the brain and muscles is a complex task, particularly when applied to exoskeletons with multiple degrees of freedom (DoFs). Here we present a feasibility, usability and functionality study of a bio-inspired hybrid brain-muscle machine interface to continuously control an upper limb exoskeleton with 7 DoFs. Methods: The system implements a hierarchical control strategy that follows the biologically natural motor command pathway from the brain to the muscles. Additionally, it employs an innovative mirror myoelectric decoder, offering patients a reference model to assist them in relearning healthy muscle activation patterns during training. Furthermore, the multi-DoF exoskeleton enables the practice of coordinated arm and hand movements, which may facilitate the early use of the affected arm in daily life activities. In this pilot trial six chronic and severely paralyzed patients controlled the multi-DoF exoskeleton using their brain and muscle activity. The intervention consisted of 2 weeks of hBMI training of functional tasks with the system followed by physiotherapy. Patients' feedback was collected during and after the trial by means of several feedback questionnaires. Assessment sessions comprised clinical scales and neurophysiological measurements, conducted prior to, immediately following the intervention, and at a 2-week follow-up. Results: Patients' feedback indicates a great adoption of the technology and their confidence in its rehabilitation potential. Half of the patients showed improvements in their arm function and 83% improved their hand function. Furthermore, we found improved patterns of muscle activation as well as increased motor evoked potentials after the intervention. Discussion: This underscores the significant potential of bio-inspired interfaces that engage the entire nervous system, spanning from the brain to the muscles, for the rehabilitation of stroke patients, even those who are severely paralyzed and in the chronic phase.}, }
@article {pmid38681523, year = {2024}, author = {Wu, J and Zhao, Y}, title = {Single cocaine exposure attenuates the intrinsic excitability of CRH neurons in the ventral BNST via Sigma-1 receptors.}, journal = {Translational neuroscience}, volume = {15}, number = {1}, pages = {20220339}, pmid = {38681523}, issn = {2081-3856}, abstract = {The ventral bed nucleus of the stria terminalis (vBNST) plays a key role in cocaine addiction, especially relapse. However, the direct effects of cocaine on corticotropin-releasing hormone (CRH) neurons in the vBNST remain unclear. Here, we identify that cocaine exposure can remarkably attenuate the intrinsic excitability of CRH neurons in the vBNST in vitro. Accumulating studies reveal the crucial role of Sigma-1 receptors (Sig-1Rs) in modulating cocaine addiction. However, to the authors' best knowledge no investigations have explored the role of Sig-1Rs in the vBNST, let alone CRH neurons. Given that cocaine acts as a type of Sig-1Rs agonist, and the dramatic role of Sig-1Rs played in intrinsic excitability of neurons as well as cocaine addiction, we employ BD1063 a canonical Sig-1Rs antagonist to block the effects of cocaine, and significantly recover the excitability of CRH neurons. Together, we suggest that cocaine exposure leads to the firing rate depression of CRH neurons in the vBNST via binding to Sig-1Rs.}, }
@article {pmid38680535, year = {2024}, author = {Jia, T and Sun, J and McGeady, C and Ji, L and Li, C}, title = {Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {5}, number = {}, pages = {0116}, pmid = {38680535}, issn = {2692-7632}, abstract = {Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.}, }
@article {pmid38679478, year = {2024}, author = {Tao, R and Zhao, H and Zhang, C and Xu, S}, title = {Distinct neural dynamics of the observed ostracism effect in decision-making under risk and ambiguity.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {4}, pages = {}, doi = {10.1093/cercor/bhae171}, pmid = {38679478}, issn = {1460-2199}, support = {72171151//National Natural Science Foundation of China/ ; 21ZR1461600//Natural Science Foundation of Shanghai/ ; 2021114003//Fundamental Research Funds for the Central Universities/ ; BHA230145//General Education Project of the National Social Science Fund/ ; 23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; 2021WSYS002//Neuroeconomics Laboratory of Guangzhou Huashang College/ ; }, mesh = {Humans ; Male ; Female ; *Decision Making/physiology ; Young Adult ; *Electroencephalography ; *Risk-Taking ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Social Isolation/psychology ; Cues ; }, abstract = {Observational ostracism, as a form of social exclusion, can significantly affect human behavior. However, the effects of observed ostracism on risky and ambiguous decision-making and the underlying neural mechanisms remain unclear. This event-related potential study investigated these issues by involving participants in a wheel-of- fortune task, considering observed ostracism and inclusion contexts. The results showed that the cue-P3 component was more enhanced during the choice phase for risky decisions than for ambiguous decisions in the observed inclusion contexts but not in the observed ostracism contexts. During the outcome evaluation phase, feedback-related negativity amplitudes following both risky and ambiguous decisions were higher in the no-gain condition than in the gain condition in the observed inclusion context. In contrast, this effect was only observed following risky decisions in the observed ostracism context. The feedback-P3 component did not exhibit an observed ostracism effect in risky and ambiguous decision-making tasks. Risk levels further modulated the cue-P3 and feedback-related negativity components, while ambiguity levels further modulated the feedback-P3 components. These findings demonstrate a neural dissociation between risk and ambiguity decision-making during observed ostracism that unfolds from the choice phase to the outcome evaluation phase.}, }
@article {pmid38678941, year = {2024}, author = {Zong, F and Wang, L and Liu, H and Xue, B and Bai, R and Liu, Y}, title = {A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI.}, journal = {Computers in biology and medicine}, volume = {175}, number = {}, pages = {108508}, doi = {10.1016/j.compbiomed.2024.108508}, pmid = {38678941}, issn = {1879-0534}, mesh = {Humans ; *Algorithms ; *Brain/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Diffusion Magnetic Resonance Imaging/methods ; Monte Carlo Method ; }, abstract = {Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.}, }
@article {pmid38677220, year = {2024}, author = {Shah, DD and Carter, P and Shivdasani, MN and Fong, N and Duan, W and Esrafilzadeh, D and Poole-Warren, LA and Aregueta Robles, UA}, title = {Deciphering platinum dissolution in neural stimulation electrodes: Electrochemistry or biology?.}, journal = {Biomaterials}, volume = {309}, number = {}, pages = {122575}, doi = {10.1016/j.biomaterials.2024.122575}, pmid = {38677220}, issn = {1878-5905}, mesh = {*Platinum/chemistry ; Humans ; Animals ; Electrodes, Implanted ; Electric Stimulation ; Electrochemistry/methods ; Electrodes ; }, abstract = {Platinum (Pt) is the metal of choice for electrodes in implantable neural prostheses like the cochlear implants, deep brain stimulating devices, and brain-computer interfacing technologies. However, it is well known since the 1970s that Pt dissolution occurs with electrical stimulation. More recent clinical and in vivo studies have shown signs of corrosion in explanted electrode arrays and the presence of Pt-containing particulates in tissue samples. The process of degradation and release of metallic ions and particles can significantly impact on device performance. Moreover, the effects of Pt dissolution products on tissue health and function are still largely unknown. This is due to the highly complex chemistry underlying the dissolution process and the difficulty in decoupling electrical and chemical effects on biological responses. Understanding the mechanisms and effects of Pt dissolution proves challenging as the dissolution process can be influenced by electrical, chemical, physical, and biological factors, all of them highly variable between experimental settings. By evaluating comprehensive findings on Pt dissolution mechanisms reported in the fuel cell field, this review presents a critical analysis of the possible mechanisms that drive Pt dissolution in neural stimulation in vitro and in vivo. Stimulation parameters, such as aggregate charge, charge density, and electrochemical potential can all impact the levels of dissolved Pt. However, chemical factors such as electrolyte types, dissolved gases, and pH can all influence dissolution, confounding the findings of in vitro studies with multiple variables. Biological factors, such as proteins, have been documented to exhibit a mitigating effect on the dissolution process. Other biological factors like cells and fibro-proliferative responses, such as fibrosis and gliosis, impact on electrode properties and are suspected to impact on Pt dissolution. However, the relationship between electrical properties of stimulating electrodes and Pt dissolution remains contentious. Host responses to Pt degradation products are also controversial due to the unknown chemistry of Pt compounds formed and the lack of understanding of Pt distribution in clinical scenarios. The cytotoxicity of Pt produced via electrical stimulation appears similar to Pt-based compounds, including hexachloroplatinates and chemotherapeutic agents like cisplatin. While the levels of Pt produced under clinical and acute stimulation regimes were typically an order of magnitude lower than toxic concentrations observed in vitro, further research is needed to accurately assess the mass balance and type of Pt produced during long-term stimulation and its impact on tissue response. Finally, approaches to mitigating the dissolution process are reviewed. A wide variety of approaches, including stimulation strategies, coating electrode materials, and surface modification techniques to avoid excess charge during stimulation and minimise tissue response, may ultimately support long-term and safe operation of neural stimulating devices.}, }
@article {pmid38675259, year = {2024}, author = {Wang, X and Jiang, W and Yang, H and Ye, Y and Zhou, Z and Sun, L and Nie, Y and Tao, TH and Wei, X}, title = {Ultraflexible PEDOT:PSS/IrOx-Modified Electrodes: Applications in Behavioral Modulation and Neural Signal Recording in Mice.}, journal = {Micromachines}, volume = {15}, number = {4}, pages = {}, pmid = {38675259}, issn = {2072-666X}, support = {2019YFA0905200//National Key R &amp; D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; }, abstract = {Recent advancements in neural probe technology have become pivotal in both neuroscience research and the clinical management of neurological disorders. State-of-the-art developments have led to the advent of multichannel, high-density bidirectional neural interfaces that are adept at both recording and modulating neuronal activity within the central nervous system. Despite this progress, extant bidirectional probes designed for simultaneous recording and stimulation are beset with limitations, including elicitation of inflammatory responses and insufficient charge injection capacity. In this paper, we delineate the design and application of an innovative ultraflexible bidirectional neural probe engineered from polyimide. This probe is distinguished by its ability to facilitate high-resolution recordings and precise stimulation control in deep brain regions. Electrodes enhanced with a PEDOT:PSS/IrOx composite exhibit a substantial increase in charge storage capacity, escalating from 0.14 ± 0.01 mC/cm[2] to an impressive 24.75 ± 0.18 mC/cm[2]. This augmentation significantly bolsters the electrodes' charge transfer efficacy. In tandem, we observed a notable reduction in electrode impedance, from 3.47 ± 1.77 MΩ to a mere 41.88 ± 4.04 kΩ, while the phase angle exhibited a positive shift from -72.61 ± 1.84° to -34.17 ± 0.42°. To substantiate the electrodes' functional prowess, we conducted in vivo experiments, where the probes were surgically implanted into the bilateral motor cortex of mice. These experiments involved the synchronous recording and meticulous analysis of neural signal fluctuations during stimulation and an assessment of the probes' proficiency in modulating directional turning behaviors in the subjects. The empirical evidence corroborates that targeted stimulation within the bilateral motor cortex of mice can modulate the intensity of neural signals in the stimulated locale, enabling the directional control of the mice's turning behavior to the contralateral side of the stimulation site.}, }
@article {pmid38672024, year = {2024}, author = {Du, X and Ding, X and Xi, M and Lv, Y and Qiu, S and Liu, Q}, title = {A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.}, journal = {Brain sciences}, volume = {14}, number = {4}, pages = {}, pmid = {38672024}, issn = {2076-3425}, support = {JYTMS20230377//Educational Department of Liaoning Province/ ; }, abstract = {Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.}, }
@article {pmid38672017, year = {2024}, author = {Du, X and Wang, X and Zhu, L and Ding, X and Lv, Y and Qiu, S and Liu, Q}, title = {Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.}, journal = {Brain sciences}, volume = {14}, number = {4}, pages = {}, pmid = {38672017}, issn = {2076-3425}, support = {JYTMS20230377//Liaoning Provincial Education Department/ ; }, abstract = {EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals' compressed sensing.}, }
@article {pmid38671991, year = {2024}, author = {Li, M and Yang, L and Wang, Z and Liu, Y and Wan, H and Shang, Z}, title = {Progress of Micro-Stimulation Techniques to Alter Pigeons' Motor Behavior: A Review from the Perspectives of the Neural Basis and Neuro-Devices.}, journal = {Brain sciences}, volume = {14}, number = {4}, pages = {}, pmid = {38671991}, issn = {2076-3425}, support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 2022ZD0208500//STI 2030-Major Project/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; }, abstract = {Pigeons have natural advantages in robotics research, including a wide range of activities, low energy consumption, good concealment performance, strong long-distance weight bearing and continuous flight ability, excellent navigation, and spatial cognitive ability, etc. They are typical model animals in the field of animal robot research and have important application value. A hot interdisciplinary research topic and the core content of pigeon robot research, altering pigeon motor behavior using brain stimulation involves multiple disciplines including animal ethology, neuroscience, electronic information technology and artificial intelligence technology, etc. In this paper, we review the progress of altering pigeon motor behavior using brain stimulation from the perspectives of the neural basis and neuro-devices. The recent literature on altering pigeon motor behavior using brain stimulation was investigated first. The neural basis, structure and function of a system to alter pigeon motor behavior using brain stimulation are briefly introduced below. Furthermore, a classified review was carried out based on the representative research achievements in this field in recent years. Our summary and discussion of the related research progress cover five aspects including the control targets, control parameters, control environment, control objectives, and control system. Future directions that need to be further studied are discussed, and the development trend in altering pigeon motor behavior using brain stimulation is projected.}, }
@article {pmid38671977, year = {2024}, author = {Zhang, C and Su, L and Li, S and Fu, Y}, title = {Differential Brain Activation for Four Emotions in VR-2D and VR-3D Modes.}, journal = {Brain sciences}, volume = {14}, number = {4}, pages = {}, pmid = {38671977}, issn = {2076-3425}, support = {62166021//National Natural Science Foundation of China/ ; 82172058//National Natural Science Foundation of China/ ; 81771926//National Natural Science Foundation of China/ ; }, abstract = {Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal-high pleasure (HAHV), high arousal-low pleasure (HALV), low arousal-low pleasure (LALV), and low arousal-high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation.}, }
@article {pmid38671798, year = {2024}, author = {Chen, Q and Dong, Y and Gai, Y}, title = {Tactile Location Perception Encoded by Gamma-Band Power.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {4}, pages = {}, pmid = {38671798}, issn = {2306-5354}, support = {2217032//National Science Foundation/ ; }, abstract = {BACKGROUND: The perception of tactile-stimulation locations is an important function of the human somatosensory system during body movements and its interactions with the surroundings. Previous psychophysical and neurophysiological studies have focused on spatial location perception of the upper body. In this study, we recorded single-trial electroencephalography (EEG) responses evoked by four vibrotactile stimulators placed on the buttocks and thighs while the human subject was sitting in a chair with a cushion.<br><br>METHODS: Briefly, 14 human subjects were instructed to sit in a chair for a duration of 1 h or 1 h and 45 min. Two types of cushions were tested with each subject: a foam cushion and an air-cell-based cushion dedicated for wheelchair users to alleviate tissue stress. Vibrotactile stimulations were applied to the sitting interface at the beginning and end of the sitting period. Somatosensory-evoked potentials were obtained using a 32-channel EEG. An artificial neural net was used to predict the tactile locations based on the evoked EEG power.<br><br>RESULTS: We found that single-trial beta (13-30 Hz) and gamma (30-50 Hz) waves can best predict the tactor locations with an accuracy of up to 65%. Female subjects showed the highest performances, while males' sensitivity tended to degrade after the sitting period. A three-way ANOVA analysis indicated that the air-cell cushion maintained location sensitivity better than the foam cushion.<br><br>CONCLUSION: Our finding shows that tactile location information is encoded in EEG responses and provides insights on the fundamental mechanisms of the tactile system, as well as applications in brain-computer interfaces that rely on tactile stimulation.}, }
@article {pmid38671769, year = {2024}, author = {Zhang, B and Xu, M and Zhang, Y and Ye, S and Chen, Y}, title = {Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain-Computer Interface.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {4}, pages = {}, pmid = {38671769}, issn = {2306-5354}, support = {2023M740171//China Postdoctoral Science Foundation/ ; 0420239352KF001-07//Talent Grant of Beijing Academy of Science and Technology/ ; }, abstract = {The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.}, }
@article {pmid38671062, year = {2024}, author = {Angrick, M and Luo, S and Rabbani, Q and Candrea, DN and Shah, S and Milsap, GW and Anderson, WS and Gordon, CR and Rosenblatt, KR and Clawson, L and Tippett, DC and Maragakis, N and Tenore, FV and Fifer, MS and Hermansky, H and Ramsey, NF and Crone, NE}, title = {Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {9617}, pmid = {38671062}, issn = {2045-2322}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; NS114439/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Amyotrophic Lateral Sclerosis/physiopathology/therapy ; Male ; *Speech/physiology ; Middle Aged ; Electrodes, Implanted ; Electrocorticography ; }, abstract = {Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant's voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.}, }
@article {pmid38670338, year = {2024}, author = {Donlon, JD and McAloon, CG and Mee, JF}, title = {Performance of various interpretations of clinical scoring systems for diagnosis of respiratory disease in dairy calves in a temperate climate using Bayesian latent class analysis.}, journal = {Journal of dairy science}, volume = {107}, number = {9}, pages = {7138-7152}, doi = {10.3168/jds.2023-24321}, pmid = {38670338}, issn = {1525-3198}, mesh = {Animals ; Cattle ; *Bayes Theorem ; *Latent Class Analysis ; *Cattle Diseases/diagnosis ; Prospective Studies ; Climate ; Dairying ; Sensitivity and Specificity ; Bovine Respiratory Disease Complex/diagnosis ; }, abstract = {Bovine respiratory disease (BRD) presents a challenge to farmers all over the globe, not only because it can have significant impacts on welfare and productivity, but also because diagnosis can prove challenging. Several clinical scoring systems have been developed to aid farmers in making consistent early diagnosis, 2 examples being the Wisconsin (WCS) and the California (CALIF) systems. Neither of these systems were developed in or for use in a temperate environment. As environment may lead to changes in BRD presentation, the weightings and cutoffs designed for one environmental presentation of BRD may not be appropriate when used in a temperate climate. Additionally, the interpretation of the scores recorded varies between studies; this may also influence conclusions. Hence, the objective of this work was to investigate the sensitivity (Se) and specificity (Sp) of these tests in a temperate climate and investigate the influence of varying the interpretation on the performance of the WCS. In this prospective study, 98 commercial spring-calving dairy farms were recruited (40 randomly, 58 targeted) and visited. Thoracic ultrasound and WCS were performed on 20 randomly sampled calves between 4 and 6 wk of age on each farm. On a subset of 32 farms, the CALIF score was also undertaken. The data were then used in a hierarchical Bayesian latent class model to estimate the Se and Sp of 5 different interpretations of the Wisconsin clinical score and 1 interpretation of the California clinical score. In total, 1,936 calves were examined. The Se of the Wisconsin score varied from 0.336 to 0.577 depending on the interpretation used, and the Sp varied from 0.943 to 0.977. The Se of the California score was 0.563 (95% Bayesian credible interval [BCI]: 0.452, 0.681) and the Sp was 0.919 (95% BCI: 0.899, 0.937). In conclusion, the performances of the clinical scores in a temperate environment were similar to previously published work from more extreme climates; however, the performance varied widely depending on the score interpretation. Authors should justify their use of a particular clinical score interpretation to improve clarity in publications.}, }
@article {pmid38669494, year = {2024}, author = {Heo, SP and Choi, H and Yang, YM}, title = {Novel stability approach using Routh-Hurwitz criterion for brain computer interface applications.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {32}, number = {S1}, pages = {17-25}, pmid = {38669494}, issn = {1878-7401}, mesh = {Humans ; *Brain-Computer Interfaces ; Computer Simulation ; Algorithms ; }, abstract = {BACKGROUND: The stability criterion approach is very important for estimating precise behavior before or after fabricating brain computer interface system applications.<br><br>OBJECTIVE: A novel approach using the Routh-Hurwitz standard criterion method is proposed to easily determine and analyze the stability of brain computer interface system applications. Using this developed approach, we were able to easily test the stability of technical issue using simple programmed codes before or after brain computer interfaces fabrication applications.<br><br>METHODS: Using a MATLAB simulation program package, we are able to provide two different special case examples such as a first zero element and a row of zeros to verify the capability of our proposed Routh-Hurwitz method.<br><br>RESULTS: The MATLAB simulation program provided efficient Routh-Hurwitz standard criterion results by differentiating the highest coefficients of the s and a.<br><br>CONCLUSION: This technical paper explains how to use our proposed new Routh-Hurwitz standard condition to simply ascertain and determine the brain computer interface system stability without customized commercial simulation tools.}, }
@article {pmid38666542, year = {2024}, author = {Li, P and Liu, J and Yuan, JH and Guo, Y and Wang, S and Zhang, P and Wang, W}, title = {Artificial Funnel Nanochannel Device Emulates Synaptic Behavior.}, journal = {Nano letters}, volume = {24}, number = {20}, pages = {6192-6200}, doi = {10.1021/acs.nanolett.3c05079}, pmid = {38666542}, issn = {1530-6992}, mesh = {*Synapses/physiology ; *Nanotechnology/instrumentation ; Electrolytes/chemistry ; Nanostructures/chemistry ; Neurons/physiology ; Electric Conductivity ; }, abstract = {Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.}, }
@article {pmid38666139, year = {2024}, author = {Hu, Y and Pan, Y and Yue, L and Gao, X}, title = {Self-objectification and eating disorders: the psychopathological and neural processes from psychological distortion to psychosomatic illness.}, journal = {Psychoradiology}, volume = {4}, number = {}, pages = {kkae003}, pmid = {38666139}, issn = {2634-4416}, }
@article {pmid38666121, year = {2023}, author = {Huang, Y and Weng, Y and Lan, L and Zhu, C and Shen, T and Tang, W and Lai, HY}, title = {Insight in obsessive-compulsive disorder: conception, clinical characteristics, neuroimaging, and treatment.}, journal = {Psychoradiology}, volume = {3}, number = {}, pages = {kkad025}, pmid = {38666121}, issn = {2634-4416}, abstract = {Obsessive-compulsive disorder (OCD) is a chronic disabling disease with often unsatisfactory therapeutic outcomes. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has broadened the diagnostic criteria for OCD, acknowledging that some OCD patients may lack insight into their symptoms. Previous studies have demonstrated that insight can impact therapeutic efficacy and prognosis, underscoring its importance in the treatment of mental disorders, including OCD. In recent years, there has been a growing interest in understanding the influence of insight on mental disorders, leading to advancements in related research. However, to the best of our knowledge, there is dearth of comprehensive reviews on the topic of insight in OCD. In this review article, we aim to fill this gap by providing a concise overview of the concept of insight and its multifaceted role in clinical characteristics, neuroimaging mechanisms, and treatment for OCD.}, }
@article {pmid38665897, year = {2024}, author = {Herbert, C}, title = {Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1305445}, pmid = {38665897}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.}, }
@article {pmid38664240, year = {2024}, author = {Armocida, D and Garbossa, D and Cofano, F}, title = {Letter: Ethical concerns and scientific communication on neuralink device.}, journal = {Neurosurgical review}, volume = {47}, number = {1}, pages = {194}, pmid = {38664240}, issn = {1437-2320}, }
@article {pmid38663401, year = {2024}, author = {Ha, LJ and Yeo, HG and Kim, YG and Baek, I and Baeg, E and Lee, YH and Won, J and Jung, Y and Park, J and Jeon, CY and Kim, K and Min, J and Song, Y and Park, JH and Nam, KR and Son, S and Yoo, SBM and Park, SH and Choi, WS and Lim, KS and Choi, JY and Cho, JH and Lee, Y and Choi, HJ}, title = {Hypothalamic neuronal activation in non-human primates drives naturalistic goal-directed eating behavior.}, journal = {Neuron}, volume = {112}, number = {13}, pages = {2218-2230.e6}, doi = {10.1016/j.neuron.2024.03.029}, pmid = {38663401}, issn = {1097-4199}, mesh = {Animals ; *Goals ; *Feeding Behavior/physiology ; Male ; *Magnetic Resonance Imaging ; Hypothalamic Area, Lateral/physiology ; GABAergic Neurons/physiology ; Positron-Emission Tomography ; Macaca mulatta ; Hypothalamus/physiology/diagnostic imaging ; Neurons/physiology ; Female ; }, abstract = {Maladaptive feeding behavior is the primary cause of modern obesity. While the causal influence of the lateral hypothalamic area (LHA) on eating behavior has been established in rodents, there is currently no primate-based evidence available on naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHA[GABA]) neurons in eating using chemogenetics in three macaques. LHA[GABA] neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, predominantly for palatable food. Positron emission tomography and magnetic resonance spectroscopy validated chemogenetic activation. Resting-state functional magnetic resonance imaging revealed that the functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after LHA[GABA] neuron activation. Thus, our study elucidates the role of LHA[GABA] neurons in eating and obesity therapeutics for primates and humans.}, }
@article {pmid38660590, year = {2024}, author = {Demirezen, G and Taşkaya Temizel, T and Brouwer, AM}, title = {Reproducible machine learning research in mental workload classification using EEG.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1346794}, pmid = {38660590}, issn = {2673-6195}, abstract = {This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.}, }
@article {pmid38660227, year = {2024}, author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H}, title = {A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1367932}, pmid = {38660227}, issn = {1662-4548}, abstract = {Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays. This is not only due to the fact that screen-based SSVEP generally operates under static, stable conditions with simple and unvaried visual stimuli but also because conventional luminance-modulated stimuli can quickly induce visual fatigue. This study attempts to address these research gaps by designing SSVEP paradigms with stereo-related attributes and conducting a comparative analysis with the traditional 2D planar paradigm under the same VR environment. This study proposed two new paradigms: the 3D paradigm and the 3D-Blink paradigm. The 3D paradigm induces SSVEP by modulating the luminance of spherical targets, while the 3D-Blink paradigm employs modulation of the spheres' opacity instead. The results of offline 4-object selection experiments showed that the accuracy of 3D and 2D paradigm was 85.67 and 86.17% with canonical correlation analysis (CCA) and 86.17 and 91.73% with filter bank canonical correlation analysis (FBCCA), which is consistent with the reduction in the signal-to-noise ratio (SNR) of SSVEP harmonics for the 3D paradigm observed in the frequency-domain analysis. The 3D-Blink paradigm achieved 75.00% of detection accuracy and 27.02 bits/min of ITR with 0.8 seconds of stimulus time and task-related component analysis (TRCA) algorithm, demonstrating its effectiveness. These findings demonstrate that the 3D and 3D-Blink paradigms supported by VR can achieve improved user comfort and satisfactory performance, while further algorithmic optimization and feature analysis are required for the stereo-related paradigms. In conclusion, this study contributes to a deeper understanding of the impact of binocular stereoscopic vision mechanisms on SSVEP paradigms and promotes the application of SSVEP-BCI in diverse VR environments.}, }
@article {pmid38658998, year = {2024}, author = {Keough, JR and Irvine, B and Kelly, D and Wrightson, J and Comaduran Marquez, D and Kinney-Lang, E and Kirton, A}, title = {Fatigue in children using motor imagery and P300 brain-computer interfaces.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {61}, pmid = {38658998}, issn = {1743-0003}, support = {1052360//Alberta Children's Hospital Foundation/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Electroencephalography ; *Fatigue/physiopathology/psychology ; *Imagination/physiology ; Cross-Over Studies ; Adolescent ; Prospective Studies ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children.<br><br>METHODS: Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3&#x2009;&#xd7;&#x2009;3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12&#xa0;Hz of the EEG spectrum.<br><br>RESULTS: Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155)&#x2009;=&#x2009;33.9, p&#x2009;<&#x2009;0.001; F&#x2009;=&#x2009;5.0(1,149), p&#x2009;=&#x2009;0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power.<br><br>CONCLUSION: Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.}, }
@article {pmid38657615, year = {2024}, author = {Song, M and Gwon, D and Jun, SC and Ahn, M}, title = {Signal alignment for cross-datasets in P300 brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad430d}, pmid = {38657615}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Algorithms ; }, abstract = {Objective.Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment (SA) for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning.Approach.We proposed a linear SA that uses the P300's latency, amplitude scale, and reverse factor to transform signals. For evaluation, four datasets were introduced (two from conventional P300 Speller BCIs, one from a P300 Speller with face stimuli, and the last from a standard auditory oddball paradigm).Results.Although the standard approach without SA had an average precision (AP) score of 25.5%, the approach demonstrated a 35.8% AP score, and we observed that the number of subjects showing improvement was 36.0% on average. Particularly, we confirmed that the Speller dataset with face stimuli was more comparable with other datasets.Significance.We proposed a simple and intuitive way to align ERP signals that uses the characteristics of ERP signals. The results demonstrated the feasibility of cross-dataset transfer learning even between datasets with different paradigms.}, }
@article {pmid38656833, year = {2024}, author = {Bottosso, M and Miglietta, F and Vernaci, GM and Giarratano, T and Dieci, MV and Guarneri, V and Griguolo, G}, title = {Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer.}, journal = {Clinical cancer research : an official journal of the American Association for Cancer Research}, volume = {30}, number = {14}, pages = {2884-2894}, pmid = {38656833}, issn = {1557-3265}, support = {Ricerca Corrente funding//Istituto Oncologico Veneto (IOV)/ ; DOR funding//Universit&#xe0; degli Studi di Padova (UNIPD)/ ; }, mesh = {Humans ; *Breast Neoplasms/drug therapy/genetics/pathology/metabolism ; Female ; Chemotherapy, Adjuvant/methods ; *Receptor, ErbB-2/genetics/metabolism ; *Antineoplastic Agents, Hormonal/therapeutic use/pharmacology ; *Biomarkers, Tumor/genetics ; Gene Expression Profiling ; Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Gene Expression Regulation, Neoplastic/drug effects ; Precision Medicine/methods ; }, abstract = {Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.}, }
@article {pmid38656638, year = {2024}, author = {Waisberg, E and Ong, J and Lee, AG}, title = {Correction: Ethical Considerations of Neuralink and Brain-Computer Interfaces.}, journal = {Annals of biomedical engineering}, volume = {52}, number = {8}, pages = {1940}, doi = {10.1007/s10439-024-03524-x}, pmid = {38656638}, issn = {1573-9686}, }
@article {pmid38654411, year = {2024}, author = {Romanchek, BAH and Uetz, G and Scheifele, PM}, title = {Characterization of sound production by the pot-bellied seahorse (Hippocampus abdominalis) during feeding.}, journal = {Journal of fish biology}, volume = {105}, number = {1}, pages = {124-128}, doi = {10.1111/jfb.15747}, pmid = {38654411}, issn = {1095-8649}, mesh = {Animals ; *Smegmamorpha/physiology ; Female ; Male ; *Feeding Behavior ; Vocalization, Animal ; Body Size ; }, abstract = {Sound production during feeding by the pot-bellied seahorse, Hippocampus abdominalis, was quantified with an observation of clicks (acoustic signal) and snicks (visual behavior). Female, male, and juvenile seahorses had feeding sounds characterized for peak (dominant) frequency (Hz), sound pressure level (SPL), and duration (ms). Subject body size and condition was estimated by standard length (SL, cm), to determine an estimate of body condition index (BCI). An inverse correlation between mean peak frequency (Hz) of clicks and SL was found for females. A negative correlation between peak frequency (Hz) of clicks and a residual BCI was determined for both males and females, suggesting that acoustic signals may contain information regarding fitness.}, }
@article {pmid38654008, year = {2024}, author = {Łabęcki, M and Nowicka, MM and Wróbel, A and Suffczynski, P}, title = {Frequency-dependent dynamics of steady-state visual evoked potentials under sustained flicker stimulation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {9281}, pmid = {38654008}, issn = {2045-2322}, mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Photic Stimulation ; Male ; *Electroencephalography ; Female ; Adult ; Young Adult ; Brain-Computer Interfaces ; Visual Cortex/physiology ; }, abstract = {Steady-state visual evoked potentials (SSVEP) are electroencephalographic signals elicited when the brain is exposed to a visual stimulus with a steady frequency. We analyzed the temporal dynamics of SSVEP during sustained flicker stimulation at 5, 10, 15, 20 and 40 Hz. We found that the amplitudes of the responses were not stable over time. For a 5 Hz stimulus, the responses progressively increased, while, for higher flicker frequencies, the amplitude increased during the first few seconds and often showed a continuous decline afterward. We hypothesize that these two distinct sets of frequency-dependent SSVEP signal properties reflect the contribution of parvocellular and magnocellular visual pathways generating sustained and transient responses, respectively. These results may have important applications for SSVEP signals used in research and brain-computer interface technology and may contribute to a better understanding of the frequency-dependent temporal mechanisms involved in the processing of prolonged periodic visual stimuli.}, }
@article {pmid38653131, year = {2024}, author = {Yang, Y and Luo, S and Wang, W and Gao, X and Yao, X and Wu, T}, title = {From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications.}, journal = {NeuroImage. Clinical}, volume = {42}, number = {}, pages = {103608}, pmid = {38653131}, issn = {2213-1582}, mesh = {*Magnetoencephalography/methods ; Humans ; Brain/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain Mapping/methods ; Nervous System Diseases/physiopathology/diagnosis ; }, abstract = {Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.}, }
@article {pmid38651553, year = {2024}, author = {Canale, A and Urbanelli, A and Albera, R and Gragnano, M and Bordino, V and Riva, G and Sportoletti Baduel, E and Albera, A}, title = {Binaural hearing in monaural conductive or mixed hearing loss fitted with unilateral Bonebridge.}, journal = {Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale}, volume = {44}, number = {2}, pages = {113-119}, pmid = {38651553}, issn = {1827-675X}, mesh = {Humans ; Female ; Male ; Middle Aged ; *Hearing Loss, Mixed Conductive-Sensorineural/rehabilitation/surgery ; Adult ; *Hearing Loss, Conductive/rehabilitation/surgery/physiopathology ; *Bone Conduction ; Hearing Aids ; Aged ; }, abstract = {OBJECTIVE: To determine the benefits of binaural hearing rehabilitation in patients with monaural conductive or mixed hearing loss treated with a unilateral bone conduction implant (BCI).<br><br>METHODS: This monocentric study includes 7 patients with monaural conductive or mixed hearing loss who underwent surgical implantation of a unilateral BCI (Bonebridge, Med-El). An ITA Matrix test was performed by each patient included in the study - without and with the BCI and in three different settings - to determine the summation effect, squelch effect and head shadow effect. Subjective hearing benefits were assessed using the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire.<br><br>RESULTS: The difference in signal to noise ratio of patients without and with BCI was 0.79 dB in the summation setting (p < 0.05), 4.62 dB in the head shadow setting (p < 0.05) and 1.53 dB (p = 0.063) in the squelch setting. The APHAB questionnaire revealed a subjective discomfort in the presence of unexpected sounds in patients using a unilateral BCI (aversiveness score) compared to the same environmental situations without BCI, with a mean discomfort score of 69.00% (SD &#xb1; 21.24%) with monaural BCI versus 25.67% (SD &#xb1; 16.70%) without BCI (difference: -43.33%, p < 0.05). In terms of global score, patients wearing a unilateral Bonebridge implant did not show any significant differences compared to those without hearing aid (difference: -4.00%, p = 0.310).<br><br>CONCLUSIONS: Our study shows that the use of a unilateral BCI in patients affected by monaural conductive or mixed hearing loss can improve speech perception under noise conditions due to the summation effect and to the decrease of the head shadow effect. However, since monaural BCIs might lead to discomfort under noise conditions in some subjects, a pre-operative assessment of the possible individual benefit of a monaural BCI should be carried out in patients affected by unilateral conductive or mixed hearing loss in order to investigate the possible additional effect of the fitting of hearing aids.}, }
@article {pmid38649681, year = {2024}, author = {Wimmer, M and Weidinger, N and Veas, E and Müller-Putz, GR}, title = {Multimodal decoding of error processing in a virtual reality flight simulation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {9221}, pmid = {38649681}, issn = {2045-2322}, mesh = {Humans ; *Virtual Reality ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; Male ; *Pupil/physiology ; Female ; Young Adult ; Computer Simulation ; Brain/physiology ; Heart Rate/physiology ; }, abstract = {Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only. We examined the feasibility of decoding errors solely with pupil size data and proposed a hybrid decoding approach combining electroencephalographic (EEG) and pupillometric signals. Moreover, we analyzed if hybrid approaches can improve existing EEG-based classification approaches and focused on setups that offer increased usability for practical applications, such as the presented game-like virtual reality flight simulation. Our results indicate that classifiers trained with pupil size data can decode errors above chance. Moreover, hybrid approaches yielded improved performance compared to EEG-based decoders in setups with a reduced number of channels, which is crucial for many out-of-the-lab scenarios. These findings contribute to the development of hybrid brain-computer interfaces, particularly in combination with wearable devices, which allow for easy acquisition of additional physiological data.}, }
@article {pmid38649503, year = {2024}, author = {Zhang, X and Lian, J and Yu, Z and Tang, H and Liang, D and Liu, J and Liu, JK}, title = {Revealing the mechanisms of semantic satiation with deep learning models.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {487}, pmid = {38649503}, issn = {2399-3642}, support = {21JR7RA510//Gansu Science and Technology Department (Science and Technology Department of Gansu Province)/ ; 62236007//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 20230484362//Beijing Nova Program/ ; }, mesh = {*Deep Learning ; *Semantics ; Humans ; Neural Networks, Computer ; Models, Neurological ; }, abstract = {The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.}, }
@article {pmid38648783, year = {2024}, author = {Tankus, A and Rosenberg, N and Ben-Hamo, O and Stern, E and Strauss, I}, title = {Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad4179}, pmid = {38648783}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Neurons/physiology ; Male ; *Machine Learning ; Female ; Middle Aged ; *Speech/physiology ; Adult ; *Thalamus/physiology ; Deep Brain Stimulation/methods ; Aged ; Speech Perception/physiology ; }, abstract = {Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.}, }
@article {pmid38648782, year = {2024}, author = {Guerreiro Fernandes, F and Raemaekers, M and Freudenburg, Z and Ramsey, N}, title = {Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad4178}, pmid = {38648782}, issn = {1741-2552}, mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; Data Analysis ; Electrodes, Implanted ; *Magnetic Resonance Imaging ; Motion ; Paralysis ; Prostheses and Implants ; *Sensorimotor Cortex/anatomy &amp; histology/physiology ; *Speech/physiology ; Support Vector Machine ; Temporal Lobe/anatomy &amp; histology/physiology ; Brain Mapping ; }, abstract = {Objective.Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis.Approach.Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM).Main results.Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC.Significance.The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.}, }
@article {pmid38648781, year = {2024}, author = {Ikegawa, Y and Fukuma, R and Sugano, H and Oshino, S and Tani, N and Tamura, K and Iimura, Y and Suzuki, H and Yamamoto, S and Fujita, Y and Nishimoto, S and Kishima, H and Yanagisawa, T}, title = {Text and image generation from intracranial electroencephalography using an embedding space for text and images.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad417a}, pmid = {38648781}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electrocorticography/methods ; Adult ; Electroencephalography/methods ; Middle Aged ; Electrodes, Implanted ; Young Adult ; Photic Stimulation/methods ; }, abstract = {Objective.Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions.Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γpower of the iEEG signals recorded while viewing the images.Main results.Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy.Significance.The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.}, }
@article {pmid38648154, year = {2024}, author = {Chen, X and Wang, Z and Wu, D}, title = {Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1703-1714}, doi = {10.1109/TNSRE.2024.3391936}, pmid = {38648154}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Machine Learning ; *Imagination/physiology ; *Algorithms ; Evoked Potentials/physiology ; }, abstract = {Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness.}, }
@article {pmid38648145, year = {2024}, author = {Zhang, R and Feng, S and Hu, N and Low, S and Li, M and Chen, X and Cui, H}, title = {Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {7}, pages = {4194-4203}, doi = {10.1109/JBHI.2024.3392412}, pmid = {38648145}, issn = {2168-2208}, mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; *Robotics/instrumentation/methods ; Female ; Adult ; Middle Aged ; Electroencephalography/methods/instrumentation ; Evoked Potentials, Visual/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Aged ; Young Adult ; Stroke/physiopathology ; Equipment Design ; Hand/physiology/physiopathology ; }, abstract = {Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.}, }
@article {pmid38647635, year = {2024}, author = {Juyal, R and Muthusamy, H and Kumar, N and Tiwari, A}, title = {Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.}, journal = {Physical and engineering sciences in medicine}, volume = {47}, number = {3}, pages = {939-954}, pmid = {38647635}, issn = {2662-4737}, support = {CRG/2021/007147//Core Research Grant of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (CRG/2021/007147)/ ; }, mesh = {Humans ; *Electroencephalography ; *Brain/physiology/diagnostic imaging ; Male ; Female ; Adult ; *Imagination/physiology ; Language ; Young Adult ; Brain-Computer Interfaces ; Rest ; Phonetics ; Brain Mapping ; Speech/physiology ; }, abstract = {Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers. However, more research needs to be conducted on how subject-specific characteristics such as mental state, age, handedness, nativeness and resting state activity affects the brain's output during imagined speech. In an overt speech, it is evident that native and non-native speakers' brains function differently. Therefore, this paper explores how nativeness to language affects EEG signals while imagining vowel phonemes, using brain-map analysis and scalogram and also investigates the inclusion of features extracted from resting state EEG with imagined state EEG. The Fourteen-channel EEG for Imagined Speech (FEIS) dataset was used to analyse the EEG signals recorded while imagining vowel phonemes for 16 subjects (nine native English and seven non-native Chinese). For the classification of vowel phonemes, different connectivity measures such as covariance, coherence, and Phase Synchronous Index-PSI were extracted and analysed using statistics based Multivariate Analysis of Variance (MANOVA) approach. Different fusion strategies (difference, concatenation, Common Spatial Pattern-CSP and Canonical Correlation Analysis-CCA) were carried out to incorporate resting state EEG connectivity measures with imagined state connectivity measures for enhancing the accuracy of imagined vowel phoneme recognition. Simulation results revealed that concatenating imagined state and rest state covariance and PSI features provided the maximum accuracy of 92.78% for native speakers and 94.07% for non-native speakers.}, }
@article {pmid38647423, year = {2024}, author = {Shen, Q and Tang, X and Wen, X and Cheng, S and Xiao, P and Zang, SK and Shen, DD and Jiang, L and Zheng, Y and Zhang, H and Xu, H and Mao, C and Zhang, M and Hu, W and Sun, JP and Zhang, Y and Chen, Z}, title = {Molecular Determinant Underlying Selective Coupling of Primary G-Protein by Class A GPCRs.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {23}, pages = {e2310120}, pmid = {38647423}, issn = {2198-3844}, support = {92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 32000845//National Natural Science Foundation of China/ ; U21A20418//National Natural Science Foundation of China/ ; 81973302//National Natural Science Foundation of China/ ; 62202426//National Natural Science Foundation of China/ ; 81825022//National Science Fund for Distinguished Young Scholars/ ; 2021C03039//Key R&amp;D Projects of Zhejiang Province/ ; //Fundamental Research Funds for the Central Universities/ ; 2019YFA050880//National Key Research and Development Program of China/ ; 2022ZD0205400//STI2030-Major Projects/ ; 2024C03147//"'Pioneer"' and "'Leading Goose"' R&amp;D Program of Zhejiang/ ; 2020R01006//Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; }, mesh = {*Receptors, G-Protein-Coupled/metabolism/chemistry/genetics ; Humans ; *Cryoelectron Microscopy/methods ; GTP-Binding Proteins/metabolism/chemistry/genetics ; Histamine/metabolism/chemistry ; Receptors, Histamine H2/metabolism/genetics/chemistry ; Receptors, Histamine H3/metabolism/chemistry/genetics ; Signal Transduction ; }, abstract = {G-protein-coupled receptors (GPCRs) transmit downstream signals predominantly via G-protein pathways. However, the conformational basis of selective coupling of primary G-protein remains elusive. Histamine receptors H2R and H3R couple with Gs- or Gi-proteins respectively. Here, three cryo-EM structures of H2R-Gs and H3R-Gi complexes are presented at a global resolution of 2.6-2.7 Å. These structures reveal the unique binding pose for endogenous histamine in H3R, wherein the amino group interacts with E206[5.46] of H3R instead of the conserved D114[3.32] of other aminergic receptors. Furthermore, comparative analysis of the H2R-Gs and H3R-Gi complexes reveals that the structural geometry of TM5/TM6 determines the primary G-protein selectivity in histamine receptors. Machine learning (ML)-based structuromic profiling and functional analysis of class A GPCR-G-protein complexes illustrate that TM5 length, TM5 tilt, and TM6 outward movement are key determinants of the Gs and Gi/o selectivity among the whole Class A family. Collectively, the findings uncover the common structural geometry within class A GPCRs that determines the primary Gs- and Gi/o-coupling selectivity.}, }
@article {pmid38647399, year = {2024}, author = {Lin, RR and Jin, LL and Xue, YY and Zhang, ZS and Huang, HF and Chen, DF and Liu, Q and Mao, ZW and Wu, ZY and Tao, QQ}, title = {Hybrid Membrane-Coated Nanoparticles for Precise Targeting and Synergistic Therapy in Alzheimer's Disease.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {24}, pages = {e2306675}, pmid = {38647399}, issn = {2198-3844}, support = {2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2024SSYS0018//Key R&amp;D Program of Zhejiang Province/ ; LBY21H090003//Natural Science Foundation of Zhejiang Province/ ; 2022ZDYF22//Key Science &amp; Technologies&#x2002;R&amp;D&#x2002;Program&#x2002;of Lishui City/ ; }, mesh = {*Alzheimer Disease/drug therapy/metabolism ; Animals ; Mice ; *Nanoparticles/chemistry ; *Disease Models, Animal ; *Mice, Transgenic ; *Blood-Brain Barrier/metabolism/drug effects ; *Liposomes ; Drug Delivery Systems/methods ; Cell Membrane/metabolism/drug effects ; Humans ; }, abstract = {The blood brain barrier (BBB) limits the application of most therapeutic drugs for neurological diseases (NDs). Hybrid cell membrane-coated nanoparticles derived from different cell types can mimic the surface properties and functionalities of the source cells, further enhancing their targeting precision and therapeutic efficacy. Neuroinflammation has been increasingly recognized as a critical factor in the pathogenesis of various NDs, especially Alzheimer's disease (AD). In this study, a novel cell membrane coating is designed by hybridizing the membrane from platelets and chemokine (C-C motif) receptor 2 (CCR2) cells are overexpressed to cross the BBB and target neuroinflammatory lesions. Past unsuccessful endeavors in AD drug development underscore the challenge of achieving favorable outcomes when utilizing single-mechanism drugs.Two drugs with different mechanisms of actions into liposomes are successfully loaded to realize multitargeting treatment. In a transgenic mouse model for familial AD (5xFAD), the administration of these drug-loaded hybrid cell membrane liposomes results in a significant reduction in amyloid plaque deposition, neuroinflammation, and cognitive impairments. Collectively, the hybrid cell membrane-coated nanomaterials offer new opportunities for precise drug delivery and disease-specific targeting, which represent a versatile platform for targeted therapy in AD.}, }
@article {pmid38646579, year = {2024}, author = {Denis-Robichaud, J and Nicola, I and Chupin, H and Roy, JP and Buczinski, S and Fauteux, V and Picard-Hagen, N and Cue, R and Dubuc, J}, title = {Herd-level associations between the proportion of elevated prepartum nonesterified fatty acid concentrations and postpartum diseases, reproduction, or culling on dairy farms.}, journal = {JDS communications}, volume = {5}, number = {3}, pages = {210-214}, pmid = {38646579}, issn = {2666-9102}, abstract = {The objectives of this herd-level prospective observational cohort study were to describe the proportion of cows with elevated prepartum nonesterified fatty acid concentrations (PropElevNEFA) in dairy herds and to assess the herd-level associations between PropElevNEFA and postpartum diseases, reproductive performance, and culling. From November 2018 to December 2020, a convenience sample of 49 herds was enrolled in this study. Blood sampling (16 to 29 cows per herd) was performed during the week before and during the 2 wk following calving to quantify the concentration of nonesterified fatty acids (NEFA) and β-hydroxybutyrate acids (BHBA), respectively. Elevated NEFA was defined as ≥280 µmol/L and hyperketonemia as BHBA ≥1.4 mmol/L. Retained placenta, metritis, purulent vaginal discharge, endometritis, and mastitis were diagnosed on-farm following standardized definitions, and success at first artificial insemination (AI) and culling events were recorded. The associations between PropElevNEFA and each individual disease, success at first AI, and culling were evaluated using Bayesian aggregated binomial regression models with weakly informative priors, from the which odds ratio (OR) and the 95% credible intervals (BCI) were obtained. A total of 981 cows were included in the statistical analyses representing 16 to 29 (median = 19) cows per herd. Cows were enrolled in the prepartum period of their first to tenth (median = third) lactation, and 41% of them had an elevated prepartum NEFA concentration. At the herd level, PropElevNEFA varied between 11% and 78% (median = 39%). The odds of metritis (OR = 1.37, 95% BCI = 1.13-1.67) increased for every 10-point increase in PropElevNEFA, whereas the odds of success at first AI decreased (OR = 0.69, 95% BCI = 0.59-0.80). The PropElevNEFA was not associated with the other tested diseases or culling. Our results suggest that the herd-level proportion of cows having elevated prepartum NEFA concentrations is associated with metritis and poor success at first AI in dairy herds.}, }
@article {pmid38645586, year = {2024}, author = {Kim, H and Won, K and Ahn, M and Jun, SC}, title = {Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.}, journal = {Biomedical engineering letters}, volume = {14}, number = {3}, pages = {617-630}, pmid = {38645586}, issn = {2093-985X}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.}, }
@article {pmid38645254, year = {2024}, author = {Card, NS and Wairagkar, M and Iacobacci, C and Hou, X and Singer-Clark, T and Willett, FR and Kunz, EM and Fan, C and Nia, MV and Deo, DR and Srinivasan, A and Choi, EY and Glasser, MF and Hochberg, LR and Henderson, JM and Shahlaie, K and Brandman, DM and Stavisky, SD}, title = {An accurate and rapidly calibrating speech neuroprosthesis.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {38645254}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 DC019430/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40's with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.}, }
@article {pmid38642806, year = {2024}, author = {Guan, S and Cong, L and Wang, F and Dong, T}, title = {A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.}, journal = {Journal of neuroscience methods}, volume = {407}, number = {}, pages = {110136}, doi = {10.1016/j.jneumeth.2024.110136}, pmid = {38642806}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Imagination/physiology ; *Wavelet Analysis ; *Brain-Computer Interfaces ; Adult ; Young Adult ; Male ; Signal Processing, Computer-Assisted ; Brain/physiology ; Female ; Motor Activity/physiology ; Wrist/physiology ; }, abstract = {BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution.<br><br>NEW METHOD: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM).<br><br>RESULTS: After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93&#x202f;%.<br><br>We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.}, }
@article {pmid38642555, year = {2024}, author = {Wang, A and Tian, X and Jiang, D and Yang, C and Xu, Q and Zhang, Y and Zhao, S and Zhang, X and Jing, J and Wei, N and Wu, Y and Lv, W and Yang, B and Zang, D and Wang, Y and Zhang, Y and Wang, Y and Meng, X}, title = {Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial.}, journal = {Med (New York, N.Y.)}, volume = {5}, number = {6}, pages = {559-569.e4}, doi = {10.1016/j.medj.2024.02.014}, pmid = {38642555}, issn = {2666-6340}, mesh = {Humans ; Male ; *Stroke Rehabilitation/methods ; Female ; *Brain-Computer Interfaces ; Middle Aged ; *Upper Extremity/physiopathology ; *Ischemic Stroke/rehabilitation/physiopathology ; Aged ; China ; Recovery of Function/physiology ; Treatment Outcome ; Adult ; }, abstract = {BACKGROUND: Upper limb motor dysfunction is a major problem in the rehabilitation of patients with stroke. Brain-computer interface (BCI) is a kind of communication system that converts the "ideas" in the brain into instructions and has been used in stroke rehabilitation. This study aimed to investigate the efficacy and safety of BCI in rehabilitation training on upper limb motor function among patients with ischemic stroke.<br><br>METHODS: This was an investigator-initiated, multicenter, randomized, open-label, blank-controlled clinical trial with blinded outcome assessment conducted at 17 centers in China. Patients were assigned in a 1:1 ratio to the BCI group or the control group based on traditional rehabilitation training. The primary efficacy outcome is the difference in improvement of the Fugl-Meyer Assessment upper extremity (FMA-UE) score between two groups at month 1 after randomization. The safety outcomes were any adverse events within 3&#xa0;months.<br><br>FINDINGS: A total of 296 patients with ischemic stroke were enrolled and randomly allocated to the BCI group (n&#xa0;= 150) and the control group (n&#xa0;= 146). The primary efficacy outcomes of FMA-UE score change from baseline to 1&#xa0;month were 13.17 (95% confidence interval [CI], 11.56-14.79) in the BCI group and 9.83 (95% CI, 8.19-11.47) in the control group (mean difference between groups was 3.35; 95% CI, 1.05-5.65; p&#xa0;= 0.0045). Adverse events occurred in 33 patients (22.00%) in the BCI group and in 31 patients (21.23%) in the control group.<br><br>CONCLUSIONS: BCI rehabilitation training can further improve upper limb motor function based on traditional rehabilitation training in patients with ischemic stroke. This study was registered at ClinicalTrials.gov: NCT04387474.<br><br>FUNDING: This work was supported by the National Key R&amp;D Program of China (2018YFC1312903), the National Key Research and Development Program of China (2022YFC3600600), the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), the Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), the Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).}, }
@article {pmid38642251, year = {2024}, author = {Youn, KI and Lee, JW and Song, Y and Lee, SY and Song, KH}, title = {Development of Cell Culture Platforms for Study of Trabecular Meshwork Cells and Glaucoma Development.}, journal = {Tissue engineering and regenerative medicine}, volume = {21}, number = {5}, pages = {695-710}, pmid = {38642251}, issn = {2212-5469}, support = {Incheon National University (International Cooperative) Research Grant in 2019//Incheon National University/ ; }, mesh = {*Trabecular Meshwork/cytology ; *Glaucoma ; Humans ; *Cell Culture Techniques/methods ; Intraocular Pressure ; Aqueous Humor ; Animals ; }, abstract = {BACKGROUND: Various cell culture platforms that could display native environmental cue-mimicking stimuli were developed, and effects of environmental cues on cell behaviors were studied with the cell culture platforms. Likewise, various cell culture platforms mimicking native trabecular meshwork (TM) composed of juxtacanalicular, corneoscleral and uveal meshwork located in internal scleral sulcus were used to study effects of environmental cues and/or drug treatments on TM cells and glaucoma development. Glaucoma is a disease that could cause blindness, and cause of glaucoma is not clearly identified yet. It appears that aqueous humor (AH) outflow resistance increased by damages on pathway of AH outflow can elevate intraocular pressure (IOP). These overall possibly contribute to development of glaucoma.<br><br>METHODS: For the study of glaucoma, static and dynamic cell culture platforms were developed. Particularly, the dynamic platforms exploiting AH outflow-mimicking perfusion or increased IOP-mimicking increased pressure were used to study how perfusion or increased pressure could affect TM cells. Overall, potential mechanisms of glaucoma development, TM structures and compositions, TM cell culture platform types and researches on TM cells and glaucoma development with the platforms were described in this review.<br><br>RESULTS AND CONCLUSION: This will be useful to improve researches on TM cells and develop enhanced therapies targeting glaucoma.}, }
@article {pmid38641040, year = {2024}, author = {Sun, R and Tang, MY and Yang, D and Zhang, YY and Xu, YH and Qiao, Y and Yu, B and Cao, SX and Wang, H and Huang, HQ and Zhang, H and Li, XM and Lian, H}, title = {C3aR in the medial prefrontal cortex modulates the susceptibility to LPS-induced depressive-like behaviors through glutamatergic neuronal excitability.}, journal = {Progress in neurobiology}, volume = {236}, number = {}, pages = {102614}, doi = {10.1016/j.pneurobio.2024.102614}, pmid = {38641040}, issn = {1873-5118}, mesh = {Animals ; *Prefrontal Cortex/metabolism/drug effects ; *Lipopolysaccharides/pharmacology ; *Neurons/metabolism/drug effects ; Mice ; *Mice, Knockout ; *Depression/metabolism/chemically induced ; Receptors, Complement/metabolism ; Mice, Inbred C57BL ; Male ; Glutamic Acid/metabolism ; }, abstract = {Complement activation and prefrontal cortical dysfunction both contribute to the pathogenesis of major depressive disorder (MDD), but their interplay in MDD is unclear. We here studied the role of complement C3a receptor (C3aR) in the medial prefrontal cortex (mPFC) and its influence on depressive-like behaviors induced by systematic lipopolysaccharides (LPS) administration. C3aR knockout (KO) or intra-mPFC C3aR antagonism confers resilience, whereas C3aR expression in mPFC neurons makes KO mice susceptible to LPS-induced depressive-like behaviors. Importantly, the excitation and inhibition of mPFC neurons have opposing effects on depressive-like behaviors, aligning with increased and decreased excitability by C3aR deletion and activation in cortical neurons. In particular, inhibiting mPFC glutamatergic (mPFC[Glu]) neurons, the main neuronal subpopulation expresses C3aR, induces depressive-like behaviors in saline-treated WT and KO mice, but not in LPS-treated KO mice. Compared to hypoexcitable mPFC[Glu] neurons in LPS-treated WT mice, C3aR-null mPFC[Glu] neurons display hyperexcitability upon LPS treatment, and enhanced excitation of mPFC[Glu] neurons is anti-depressant, suggesting a protective role of C3aR deficiency in these circumstances. In conclusion, C3aR modulates susceptibility to LPS-induced depressive-like behaviors through mPFC[Glu] neuronal excitability. This study identifies C3aR as a pivotal intersection of complement activation, mPFC dysfunction, and depression and a promising therapeutic target for MDD.}, }
@article {pmid38640695, year = {2024}, author = {Zhou, Y and Yang, B and Wang, C}, title = {Multiband task related components enhance rapid cognition decoding for both small and similar objects.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {175}, number = {}, pages = {106313}, doi = {10.1016/j.neunet.2024.106313}, pmid = {38640695}, issn = {1879-2782}, mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; *Evoked Potentials/physiology ; Photic Stimulation/methods ; Brain/physiology ; }, abstract = {The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.}, }
@article {pmid38639058, year = {2024}, author = {Liu, Y and Dai, W and Liu, Y and Hu, D and Yang, B and Zhou, Z}, title = {An SSVEP-based BCI with 112 targets using frequency spatial multiplexing.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad4091}, pmid = {38639058}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Photic Stimulation/methods ; Female ; Adult ; Young Adult ; Algorithms ; }, abstract = {Objective.Brain-computer interface (BCI) systems with large directly accessible instruction sets are one of the difficulties in BCI research. Research to achieve high target resolution (⩾100) has not yet entered a rapid development stage, which contradicts the application requirements. Steady-state visual evoked potential (SSVEP) based BCIs have an advantage in terms of the number of targets, but the competitive mechanism between the target stimulus and its neighboring stimuli is a key challenge that prevents the target resolution from being improved significantly.Approach.In this paper, we reverse the competitive mechanism and propose a frequency spatial multiplexing method to produce more targets with limited frequencies. In the proposed paradigm, we replicated each flicker stimulus as a 2 × 2 matrix and arrange the matrices of all frequencies in a tiled fashion to form the interaction interface. With different arrangements, we designed and tested three example paradigms with different layouts. Further we designed a graph neural network that distinguishes between targets of the same frequency by recognizing the different electroencephalography (EEG) response distribution patterns evoked by each target and its neighboring targets.Main results.Extensive experiment studies employing eleven subjects have been performed to verify the validity of the proposed method. The average classification accuracies in the offline validation experiments for the three paradigms are 89.16%, 91.38%, and 87.90%, with information transfer rates (ITR) of 51.66, 53.96, and 50.55 bits/min, respectively.Significance.This study utilized the positional relationship between stimuli and did not circumvent the competing response problem. Therefore, other state-of-the-art methods focusing on enhancing the efficiency of SSVEP detection can be used as a basis for the present method to achieve very promising improvements.}, }
@article {pmid38638416, year = {2024}, author = {Proverbio, AM and Cesati, F}, title = {Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.}, journal = {Frontiers in psychiatry}, volume = {15}, number = {}, pages = {1357770}, pmid = {38638416}, issn = {1664-0640}, abstract = {INTRODUCTION: The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification.<br><br>METHODS: The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs).<br><br>RESULTS: Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states.<br><br>DISCUSSION: In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.}, }
@article {pmid38636376, year = {2024}, author = {Yang, T and Zhang, P and Hu, J and Xu, W and Jiang, W and Feng, R and Lou, Y and Jin, X and Qian, Z and Gao, F and Gao, K and Liu, R and Yang, Y}, title = {Exploring the neural correlates of fat taste perception and discrimination: Insights from electroencephalogram analysis.}, journal = {Food chemistry}, volume = {450}, number = {}, pages = {139353}, doi = {10.1016/j.foodchem.2024.139353}, pmid = {38636376}, issn = {1873-7072}, mesh = {Humans ; *Taste Perception ; Female ; Young Adult ; Adult ; Male ; *Electroencephalography ; *Brain/physiology ; *Dietary Fats/metabolism/analysis ; Taste ; Fatty Acids/chemistry/metabolism ; }, abstract = {Understanding neural pathways and cognitive processes involved in the transformation of dietary fats into sensory experiences has profound implications for nutritional well-being. This study presents an efficient approach to comprehending the neural perception of fat taste using electroencephalogram (EEG). Through the examination of neural responses to different types of fatty acids (FAs) in 45 participants, we discerned distinct neural activation patterns associated with saturated versus unsaturated fatty acids. The spectrum analysis of averaged EEG signals revealed notable variations in δ and α-frequency bands across FA types. The topographical distribution and source localization results suggested that the brain encodes fat taste with specific activation timings in primary and secondary gustatory cortices. Saturated FAs elicited higher activation in cortical associated with emotion and reward processing. This electrophysiological evidence enhances our understanding of fundamental mechanisms behind fat perception, which is helpful for guiding strategies to manage hedonic eating and promote balanced fat consumption.}, }
@article {pmid38635379, year = {2024}, author = {Danesh, AR and Pu, H and Safiallah, M and Do, AH and Nenadic, Z and Heydari, P}, title = {A CMOS BD-BCI: Neural Recorder With Two-Step Time-Domain Quantizer and Multipolar Stimulator With Dual-Mode Charge Balancing.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {18}, number = {6}, pages = {1354-1370}, doi = {10.1109/TBCAS.2024.3391190}, pmid = {38635379}, issn = {1940-9990}, mesh = {*Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted/instrumentation ; Equipment Design ; Electroencephalography/instrumentation ; Electric Stimulation/instrumentation ; Semiconductors ; }, abstract = {This work presents a bi-directional brain-computer interface (BD-BCI) including a high-dynamic-range (HDR) two-step time-domain neural acquisition (TTNA) system and a high-voltage (HV) multipolar neural stimulation system incorporating dual-mode time-based charge balancing (DTCB) technique. The proposed TTNA includes four independent recording modules that can sense microvolt neural signals while tolerating large stimulation artifacts. In addition, it exhibits an integrated input-referred noise of 2.3 Vrms from 0.1- to 250-Hz and can handle a linear input-signal swing of up to 340 mVPP. The multipolar stimulator is composed of four standalone stimulators each with a maximum current of up to 14 mA (20-V of voltage compliance) and 8-bit resolution. An inter-channel interference cancellation circuitry is introduced to preserve the accuracy and effectiveness of the DTCB method in the multipolar-stimulation configuration. Fabricated in an HV 180-nm CMOS technology, the BD-BCI chipset undergoes extensive in-vitro and in-vivo evaluations. The recording system achieves a measured SNDR, SFDR, and CMRR of 84.8 dB, 89.6 dB, and 105 dB, respectively. The measurement results verify that the stimulation system is capable of performing high-precision charge balancing with 2 mV and 7.5 mV accuracy in the interpulse-bounded time-based charge balancing (TCB) and artifactless TCB modes, respectively.}, }
@article {pmid38635296, year = {2024}, author = {Li, X and Wei, W and Wang, Q and Deng, W and Li, M and Ma, X and Zeng, J and Zhao, L and Guo, W and Hall, MH and Li, T}, title = {Identify Potential Causal Relationships Between Cortical Thickness, Mismatch Negativity, Neurocognition, and Psychosocial Functioning in Drug-Naïve First-Episode Psychosis Patients.}, journal = {Schizophrenia bulletin}, volume = {50}, number = {4}, pages = {827-838}, pmid = {38635296}, issn = {1745-1701}, support = {81920108018//National Natural Science Foundation of China/ ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence and Key Project for Hangzhou Medical Disciplines/ ; 401343//McLean Foundation Award/ ; }, mesh = {Humans ; Male ; *Psychotic Disorders/physiopathology/diagnostic imaging/pathology ; Female ; Adult ; Young Adult ; *Magnetic Resonance Imaging ; *Cognitive Dysfunction/physiopathology/diagnostic imaging/etiology ; *Psychosocial Functioning ; Temporal Lobe/physiopathology/diagnostic imaging/pathology ; Evoked Potentials, Auditory/physiology ; Electroencephalography ; Cerebral Cortex/diagnostic imaging/physiopathology/pathology ; Brain Cortical Thickness ; Adolescent ; Auditory Perception/physiology ; Mediation Analysis ; }, abstract = {BACKGROUND: Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP.<br><br>METHODS: We enrolled 28 drug-na&#xef;ve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region.<br><br>RESULTS: We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r&#x2005;=&#x2005;-.709, P&#x2005;<&#x2005;.001; right: r&#x2005;=&#x2005;-.612, P&#x2005;=&#x2005;.008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways.<br><br>CONCLUSIONS: Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP.}, }
@article {pmid38635054, year = {2024}, author = {Yan, Y and An, X and Ren, H and Luo, B and Jin, S and Liu, L and Di, Y and Li, T and Huang, Y}, title = {Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.}, journal = {Neurosurgical review}, volume = {47}, number = {1}, pages = {169}, pmid = {38635054}, issn = {1437-2320}, support = {20JCZDJC00620//Tianjin Science and Technology Program/ ; 20JCZDJC00620//Tianjin Science and Technology Program/ ; }, mesh = {Humans ; *Intracranial Aneurysm ; Nomograms ; Retrospective Studies ; Angiography ; Hemodynamics ; }, abstract = {Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.}, }
@article {pmid38633751, year = {2024}, author = {Behboodi, A and Kline, J and Gravunder, A and Phillips, C and Parker, SM and Damiano, DL}, title = {Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1346050}, pmid = {38633751}, issn = {1662-5161}, abstract = {In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.}, }
@article {pmid38632207, year = {2024}, author = {Hossain, A and Khan, P and Kader, MF}, title = {Imagined speech classification exploiting EEG power spectrum features.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {8}, pages = {2529-2544}, pmid = {38632207}, issn = {1741-0444}, mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; Male ; Female ; *Support Vector Machine ; Adult ; Imagination/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; }, abstract = {Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.}, }
@article {pmid38630669, year = {2024}, author = {Zhu, HY and Chen, HT and Lin, CT}, title = {Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure.}, journal = {PloS one}, volume = {19}, number = {4}, pages = {e0301052}, pmid = {38630669}, issn = {1932-6203}, mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain/physiology ; Computer Simulation ; Alpha Rhythm ; Head ; Electroencephalography ; }, abstract = {Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup. Our study examined the effects of stress on the human brain's P300 behaviour through a height exposure experiment that incorporates complex visual, vestibular, and proprioceptive stimuli. A more complex sensory environment could produce translatable findings toward real-world behaviour and benefit emerging technologies such as brain-computer interfaces. Seventeen participants experienced our experiment that elicited the stress response through physical and virtual height exposure. We found two unique groups within our participants that exhibited contrasting behavioural performance and P300 target reaction response when exposed to stressors (from walking at heights). One group performed worse when exposed to heights and exhibited a significant decrease in parietal P300 peak amplitude and increased beta and gamma power. On the other hand, the group less affected by stress exhibited a change in their N170 peak amplitude and alpha/mu rhythm desynchronisation. The findings of our study suggest that a more individualised approach to assessing a person's behaviour performance under stress can aid in understanding P300 performance when experiencing stress.}, }
@article {pmid38628700, year = {2024}, author = {Tao, G and Yang, S and Xu, J and Wang, L and Yang, B}, title = {Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1361235}, pmid = {38628700}, issn = {1664-2295}, abstract = {BACKGROUND: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.<br><br>METHODS: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.<br><br>RESULTS: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF&#x2009;=&#x2009;4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.<br><br>CONCLUSION: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.}, }
@article {pmid38626760, year = {2024}, author = {Lopez-Bernal, D and Balderas, D and Ponce, P and Molina, A}, title = {Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad3f50}, pmid = {38626760}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods/classification ; Male ; *Speech/physiology ; Female ; Adult ; Support Vector Machine ; Young Adult ; Reproducibility of Results ; Algorithms ; }, abstract = {Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.}, }
@article {pmid38625770, year = {2024}, author = {Zhang, S and Cui, H and Li, Y and Chen, X and Gao, X and Guan, C}, title = {Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1647-1656}, doi = {10.1109/TNSRE.2024.3389051}, pmid = {38625770}, issn = {1558-0210}, mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Electroencephalography ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; *Fractals ; Arousal/physiology ; Brain/physiology ; Healthy Volunteers ; Algorithms ; }, abstract = {This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI performance, and concurrently authenticates two potent electrophysiological markers for multifaceted characterization of brain states.}, }
@article {pmid38625520, year = {2024}, author = {Della Vedova, G and Proverbio, AM}, title = {Neural signatures of imaginary motivational states: desire for music, movement and social play.}, journal = {Brain topography}, volume = {37}, number = {5}, pages = {806-825}, pmid = {38625520}, issn = {1573-6792}, mesh = {Humans ; *Motivation/physiology ; Male ; *Music ; Female ; *Electroencephalography/methods ; Adult ; Young Adult ; *Brain/physiology/diagnostic imaging ; *Brain Mapping/methods ; *Imagination/physiology ; *Movement/physiology ; Evoked Potentials/physiology ; }, abstract = {The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400-600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.}, }
@article {pmid38624364, year = {2024}, author = {Wang, Z and Xiang, L and Zhang, R}, title = {P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network.}, journal = {The Review of scientific instruments}, volume = {95}, number = {4}, pages = {}, doi = {10.1063/5.0202770}, pmid = {38624364}, issn = {1089-7623}, mesh = {Humans ; *Intention ; *Brain ; Electrodes ; Support Vector Machine ; }, abstract = {Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.}, }
@article {pmid38621380, year = {2024}, author = {Barmpas, K and Panagakis, Y and Zoumpourlis, G and Adamos, DA and Laskaris, N and Zafeiriou, S}, title = {A causal perspective on brainwave modeling for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {3}, pages = {}, doi = {10.1088/1741-2552/ad3eb5}, pmid = {38621380}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Machine Learning ; Electroencephalography/methods ; Brain Waves/physiology ; Brain/physiology ; Algorithms ; }, abstract = {Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.}, }
@article {pmid38619940, year = {2024}, author = {Pang, M and Wang, H and Huang, J and Vong, CM and Zeng, Z and Chen, C}, title = {Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1637-1646}, doi = {10.1109/TNSRE.2024.3389037}, pmid = {38619940}, issn = {1558-0210}, mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; Female ; Male ; Machine Learning ; Artifacts ; Adult ; Neural Networks, Computer ; }, abstract = {Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.}, }
@article {pmid38618604, year = {2024}, author = {Di Gregorio, MF and Der, C and Bravo-Torres, S and Zernotti, ME}, title = {Active Bone Conduction Implant and Adhesive Bone Conduction Device: A Comparison of Audiological Performance and Subjective Satisfaction.}, journal = {International archives of otorhinolaryngology}, volume = {28}, number = {2}, pages = {e332-e338}, pmid = {38618604}, issn = {1809-9777}, abstract = {Introduction Atresia of the external auditory canal affects 1 in every 10 thousand to 20 thousand live births, with a much higher prevalence in Latin America, at 5 to 21 out of every 10 thousand newborns. The treatment involves esthetic and functional aspects. Regarding the functional treatment, there are surgical and nonsurgical alternatives like spectacle frames and rigid and softband systems. Active transcutaneous bone conduction implants (BCIs) achieve good sound transmission and directly stimulate the bone. Objective To assess the audiological performance and subjective satisfaction of children implanted with an active transcutaneous BCI for more than one year and to compare the outcomes with a nonsurgical adhesive bone conduction device (aBCD) in the same users. Methods The present is a prospective, multicentric study. The audiological performance was evaluated at 1, 6, and 12 months postactivation, and after a 1-month trial with the nonsurgical device. Results Ten patients completed all tests. The 4-frequency pure-tone average (4PTA) in the unaided condition was of 65 dB HL, which improved significantly to 20 dB HL after using the BCI for 12 months. The speech recognition in quiet in the unaided condition was of 33% on average, which improved significantly, to 99% with the BCI, and to 91% with the aBCD. Conclusion The aBCD demonstrated sufficient hearing improvement and subjective satisfaction; thus, it is a good solution for hearing rehabilitation if surgery is not desired or not possible. If surgery is an option, the BCI is the superior device in terms of hearing outcomes, particularly background noise and subjective satisfaction.}, }
@article {pmid38618207, year = {2024}, author = {Kong, L and Wang, H and Yan, N and Xu, C and Chen, Y and Zeng, Y and Guo, X and Lu, J and Hu, S}, title = {Effect of antipsychotics and mood stabilisers on metabolism in bipolar disorder: a network meta-analysis of randomised-controlled trials.}, journal = {EClinicalMedicine}, volume = {71}, number = {}, pages = {102581}, pmid = {38618207}, issn = {2589-5370}, abstract = {BACKGROUND: Antipsychotics and mood stabilisers are gathering attention for the disturbance of metabolism. This network meta-analysis aims to evaluate and rank the metabolic effects of the commonly used antipsychotics and mood stabilisers in treating bipolar disorder (BD).<br><br>METHODS: Registries including PubMed, Embase, Cochrane Library, Web of Science, Ovid, and Google Scholar were searched before February 15th, 2024, for randomised controlled trials (RCTs) applying antipsychotics or mood stabilisers for BD treatment. The observed outcomes were twelve metabolic indicators. The data were extracted by two reviewers independently, and confirmed by another four reviewers and a corresponding author. The above six reviewers all participated in data analyses. Data extraction was based on PRISMA guidelines, and quality assessment was conducted according to the Cochrane Handbook. Use a random effects model for data pooling. The PROSPERO registration number is CRD42023466669.<br><br>FINDINGS: Together, 5421 records were identified, and 41 publications with 11,678 complete-trial participants were confirmed eligible. After eliminating possible sensitivity, risperidone ranked 1st in elevating fasting serum glucose (SUCRA&#xa0;=&#xa0;90.7%) and serum insulin (SUCRA&#xa0;=&#xa0;96.6%). Lurasidone was most likely to elevate HbA1c (SUCRA&#xa0;=&#xa0;82.1%). Olanzapine ranked 1st in elevating serum TC (SUCRA&#xa0;=&#xa0;93.3%), TG (SUCRA&#xa0;=&#xa0;89.6%), and LDL (SUCRA&#xa0;=&#xa0;94.7%). Lamotrigine ranked 1st in reducing HDL (SUCRA&#xa0;=&#xa0;82.6%). Amisulpride ranked 1st in elevating body weight (SUCRA&#xa0;=&#xa0;100.0%). For subgroup analyses, quetiapine is more likely to affect indicators of glucose metabolism among male adult patients with bipolar mania, while long-term lurasidone tended to affect glucose metabolism among female patients with bipolar depression. Among patients under 18, divalproex tended to affect glucose metabolism, with lithium affecting lipid metabolism. In addition, most observed antipsychotics performed higher response and remission rates than placebo, and displayed a similar dropout rate with placebo, while no between-group significance of rate was observed among mood stabilisers.<br><br>INTERPRETATION: Our findings suggest that overall, antipsychotics are effective in treating BD, while they are also more likely to disturb metabolism than mood stabilisers. Attention should be paid to individual applicability in clinical practice. The results put forward evidence-based information and clinical inspiration for drug compatibility and further research of the BD mechanism.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200), and the Research Project of Jinan Microecological Biomedicine Shandong Laboratory (No. JNL-2023001B).}, }
@article {pmid38617349, year = {2024}, author = {Sharma, D and Lupkin, SM and McGinty, VB}, title = {Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38617349}, issn = {2692-8205}, support = {K01 DA036659/DA/NIDA NIH HHS/United States ; }, abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.}, }
@article {pmid38617132, year = {2024}, author = {Qiao, Y and Mu, J and Xie, J and Hu, B and Liu, G}, title = {Music emotion recognition based on temporal convolutional attention network using EEG.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1324897}, pmid = {38617132}, issn = {1662-5161}, abstract = {Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.}, }
@article {pmid38616969, year = {2024}, author = {Zhang, Y and Zheng, Y and Ni, P and Liang, S and Li, X and Yu, H and Wei, W and Qi, X and Yu, X and Xue, R and Zhao, L and Deng, W and Wang, Q and Guo, W and Li, T}, title = {New role of platelets in schizophrenia: predicting drug response.}, journal = {General psychiatry}, volume = {37}, number = {2}, pages = {e101347}, pmid = {38616969}, issn = {2517-729X}, abstract = {BACKGROUND: Elevated platelet count (PLTc) is associated with first-episode schizophrenia and adverse outcomes in individuals with precursory psychosis. However, the impact of antipsychotic medications on PLTc and its association with symptom improvement remain unclear.<br><br>AIMS: We aimed to investigate changes in PLTc levels following antipsychotic treatment and assess whether PLTc can predict antipsychotic responses and metabolic changes after accounting for other related variables.<br><br>METHODS: A total of 2985 patients with schizophrenia were randomised into seven groups. Each group received one of seven antipsychotic treatments and was assessed at 2, 4 and 6 weeks. Clinical symptoms were evaluated using the positive and negative syndrome scale (PANSS). Additionally, we measured blood cell counts and metabolic parameters, such as blood lipids. Repeated measures analysis of variance was used to examine the effect of antipsychotics on PLTc changes, while structural equation modelling was used to assess the predictive value of PLTc on PANSS changes.<br><br>RESULTS: PLTc significantly increased in patients treated with aripiprazole (F=6.00, p=0.003), ziprasidone (F=7.10, p<0.001) and haloperidol (F=3.59, p=0.029). It exhibited a positive association with white blood cell count and metabolic indicators. Higher baseline PLTc was observed in non-responders, particularly in those defined by the PANSS-negative subscale. In the structural equation model, PLTc, white blood cell count and a latent metabolic variable predicted the rate of change in the PANSS-negative subscale scores. Moreover, higher baseline PLTc was observed in individuals with less metabolic change, although this association was no longer significant after accounting for baseline metabolic values.<br><br>CONCLUSIONS: Platelet parameters, specifically PLTc, are influenced by antipsychotic treatment and could potentially elevate the risk of venous thromboembolism in patients with schizophrenia. Elevated PLTc levels and associated factors may impede symptom improvement by promoting inflammation. Given PLTc's easy measurement and clinical relevance, it warrants increased attention from psychiatrists.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR-TRC-10000934.}, }
@article {pmid38616204, year = {2024}, author = {Lei, D and Dong, C and Guo, H and Ma, P and Liu, H and Bao, N and Kang, H and Chen, X and Wu, Y}, title = {A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {8616}, pmid = {38616204}, issn = {2045-2322}, support = {S20231148Z//Inner Mongolia Autonomous Region Graduate Research Innovation Project/ ; 61364018//the National Natural Science Foundation of China/ ; 61863029//the National Natural Science Foundation of China/ ; 2016JQ07//Inner Mongolia Natural Science Foundation/ ; 2020MS06020//Inner Mongolia Natural Science Foundation/ ; 2021MS06017//Inner Mongolia Natural Science Foundation/ ; CGZH2018129//Inner Mongolia Scientific and Technological Achievements Transformation Project/ ; 2023JSYD01006//Industrial Technology Innovation Program of IMAST/ ; 2021GG0264//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; 2020GG0268//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; }, abstract = {For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2 s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.}, }
@article {pmid38615362, year = {2024}, author = {Qi, X and Yu, X and Wei, L and Jiang, H and Dong, J and Li, H and Wei, Y and Zhao, L and Deng, W and Guo, W and Hu, X and Li, T}, title = {Novel α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator LT-102: A promising therapeutic agent for treating cognitive impairment associated with schizophrenia.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {4}, pages = {e14713}, pmid = {38615362}, issn = {1755-5949}, support = {81920108018//National Natural Science Foundation of China/ ; 82371524//National Natural Science Foundation of China/ ; 82371503//National Natural Science Foundation of China/ ; 2022C03096//Key R&amp;D Program of Zhejiang Province/ ; 2018B030334001//Special Foundation for Brain Research from Science and Technology Program of Guangdong Province/ ; LY22H090009//Natural Science Foundation of Zhejiang Province/ ; 2019HXCX02//Clinical Research Innovation Project, West China Hospital, Sichuan University/ ; //Project for Hangzhou Medical Disciplines of Excellence &amp; Key Project for Hangzhou Medical Disciplines/ ; }, mesh = {Animals ; Mice ; Rats ; Phencyclidine ; *Schizophrenia/complications/drug therapy ; *Cognitive Dysfunction/drug therapy ; Isoxazoles ; *Propionates ; }, abstract = {AIMS: We aimed to evaluate the potential of a novel selective α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator, LT-102, in treating cognitive impairments associated with schizophrenia (CIAS) and elucidating its mechanism of action.<br><br>METHODS: The activity of LT-102 was examined by Ca[2+] influx assays and patch-clamp in rat primary hippocampal neurons. The structure of the complex was determined by X-ray crystallography. The selectivity of LT-102 was evaluated by hERG tail current recording and kinase-inhibition assays. The electrophysiological characterization of LT-102 was characterized by patch-clamp recording in mouse hippocampal slices. The expression and phosphorylation levels of proteins were examined by Western blotting. Cognitive function was assessed using the Morris water maze and novel object recognition tests.<br><br>RESULTS: LT-102 is a novel and selective AMPAR potentiator with little agonistic effect, which binds to the allosteric site formed by the intradimer interface of AMPAR's GluA2 subunit. Treatment with LT-102 facilitated long-term potentiation in mouse hippocampal slices and reversed cognitive deficits in a phencyclidine-induced mouse model. Additionally, LT-102 treatment increased the protein level of brain-derived neurotrophic factor and the phosphorylation of GluA1 in primary neurons and hippocampal tissues.<br><br>CONCLUSION: We conclude that LT-102 ameliorates cognitive impairments in a phencyclidine-induced model of schizophrenia by enhancing synaptic function, which could make it a potential therapeutic candidate for CIAS.}, }
@article {pmid38614892, year = {2024}, author = {He, X and Li, W and Ma, H}, title = {Orchestrating neuronal activity-dependent translation via the integrated stress response protein GADD34.}, journal = {Trends in neurosciences}, volume = {47}, number = {5}, pages = {319-321}, doi = {10.1016/j.tins.2024.03.008}, pmid = {38614892}, issn = {1878-108X}, mesh = {Animals ; Humans ; Brain-Derived Neurotrophic Factor/metabolism ; *Neuronal Plasticity/physiology ; *Neurons/metabolism/physiology ; *Protein Biosynthesis/physiology ; *Protein Phosphatase 1/metabolism ; Stress, Physiological/physiology ; }, abstract = {In a recent study, Oliveira and colleagues revealed how growth arrest and DNA damage-inducible protein 34 (GADD34), an effector of the integrated stress response, initiates the translation of synaptic plasticity-related mRNAs following brain-derived neurotrophic factor (BDNF) stimulation. This work suggests that GADD34 may link transcriptional products with translation control upon neuronal activation, illuminating how protein synthesis is orchestrated in neuronal plasticity.}, }
@article {pmid38612334, year = {2024}, author = {Knozowski, P and Nowakowski, JJ and Stawicka, AM and Dulisz, B and Górski, A}, title = {Effect of Management of Grassland on Prey Availability and Physiological Condition of Nestling of Red-Backed Shrike Lanius collurio.}, journal = {Animals : an open access journal from MDPI}, volume = {14}, number = {7}, pages = {}, pmid = {38612334}, issn = {2076-2615}, abstract = {The study aimed to determine the influence of grassland management on the potential food base of the red-backed shrike Lanius collurio and the condition of chicks in the population inhabiting semi-natural grasslands in the Narew floodplain. The grassland area was divided into three groups: extensively used meadows, intensively used meadows fertilised with mineral fertilisers, and intensively used meadows fertilised with liquid manure, and selected environmental factors that may influence food availability were determined. Using Barber traps, 1825 samples containing 53,739 arthropods were collected, and the diversity, abundance, and proportion of large arthropods in the samples were analysed depending on the grassland use type. In the bird population, the condition of the chicks was characterised by the BCI (Body Condition Index) and haematological parameters (glucose level, haemoglobin level, haematocrit, and H:L ratio). The diversity of arthropods was highest in extensively used meadows. Still, the mean abundance and proportion of arthropods over 1 cm in length differed significantly for Orthoptera, Hymenoptera, Arachne, and Carabidae between grassland use types, with the highest proportion of large arthropods and the highest abundance recorded in manure-fertilised meadows. The highest Body Condition Indexes and blood glucose levels of nestlings indicating good nestling nutrition were recorded in nests of birds associated with extensive land use. The H:L ratio as an indicator of the physiological condition of nestlings was high on manure-fertilised and extensively managed meadows, indicating stress factors associated with these environments. This suggests that consideration should be given to the effects of chemicals, such as pesticides or drug residues, that may come from slurry poured onto fields on the fitness of red-backed shrike chicks.}, }
@article {pmid38610540, year = {2024}, author = {Clemente, L and La Rocca, M and Paparella, G and Delussi, M and Tancredi, G and Ricci, K and Procida, G and Introna, A and Brunetti, A and Taurisano, P and Bevilacqua, V and de Tommaso, M}, title = {Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {7}, pages = {}, pmid = {38610540}, issn = {1424-8220}, support = {POR Puglia FESR-FSE 2014-2020 - Asse I - Azione 1.4.b//Regione Puglia/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Aging ; Brain ; Esthetics ; Perception ; }, abstract = {In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.}, }
@article {pmid38609642, year = {2023}, author = {Zeng, J and Zhang, Y and Xiang, Y and Liang, S and Xue, C and Zhang, J and Ran, Y and Cao, M and Huang, F and Huang, S and Deng, W and Li, T}, title = {Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.}, journal = {Npj mental health research}, volume = {2}, number = {1}, pages = {4}, pmid = {38609642}, issn = {2731-4251}, abstract = {There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.}, }
@article {pmid38608677, year = {2024}, author = {Wang, Y and Wang, X and Wang, L and Zheng, L and Meng, S and Zhu, N and An, X and Wang, L and Yang, J and Zheng, C and Ming, D}, title = {Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.}, journal = {Current biology : CB}, volume = {34}, number = {9}, pages = {1866-1879.e6}, doi = {10.1016/j.cub.2024.03.032}, pmid = {38608677}, issn = {1879-0445}, mesh = {*Prefrontal Cortex/physiology ; *Theta Rhythm/physiology ; Animals ; *Hippocampus/physiology ; Male ; *Spatial Memory/physiology ; *Goals ; Neurons/physiology ; Mice ; }, abstract = {Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aβ) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aβ accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.}, }
@article {pmid38608331, year = {2024}, author = {Inguscio, BMS and Cartocci, G and Sciaraffa, N and Nicastri, M and Giallini, I and Aricò, P and Greco, A and Babiloni, F and Mancini, P}, title = {Two are better than one: Differences in cortical EEG patterns during auditory and visual verbal working memory processing between Unilateral and Bilateral Cochlear Implanted children.}, journal = {Hearing research}, volume = {446}, number = {}, pages = {109007}, doi = {10.1016/j.heares.2024.109007}, pmid = {38608331}, issn = {1878-5891}, mesh = {Humans ; *Cochlear Implants ; *Memory, Short-Term ; Child ; Female ; *Cochlear Implantation/instrumentation ; Male ; *Acoustic Stimulation ; *Electroencephalography ; *Persons with Hearing Disabilities/rehabilitation/psychology ; *Visual Perception ; *Auditory Perception ; Case-Control Studies ; Theta Rhythm ; Photic Stimulation ; Gamma Rhythm ; Adolescent ; Speech Perception ; Correction of Hearing Impairment/instrumentation ; Cerebral Cortex/physiopathology/physiology ; Deafness/physiopathology/rehabilitation/surgery ; Hearing ; }, abstract = {Despite the proven effectiveness of cochlear implant (CI) in the hearing restoration of deaf or hard-of-hearing (DHH) children, to date, extreme variability in verbal working memory (VWM) abilities is observed in both unilateral and bilateral CI user children (CIs). Although clinical experience has long observed deficits in this fundamental executive function in CIs, the cause to date is still unknown. Here, we have set out to investigate differences in brain functioning regarding the impact of monaural and binaural listening in CIs compared with normal hearing (NH) peers during a three-level difficulty n-back task undertaken in two sensory modalities (auditory and visual). The objective of this pioneering study was to identify electroencephalographic (EEG) marker pattern differences in visual and auditory VWM performances in CIs compared to NH peers and possible differences between unilateral cochlear implant (UCI) and bilateral cochlear implant (BCI) users. The main results revealed differences in theta and gamma EEG bands. Compared with hearing controls and BCIs, UCIs showed hypoactivation of theta in the frontal area during the most complex condition of the auditory task and a correlation of the same activation with VWM performance. Hypoactivation in theta was also observed, again for UCIs, in the left hemisphere when compared to BCIs and in the gamma band in UCIs compared to both BCIs and NHs. For the latter two, a correlation was found between left hemispheric gamma oscillation and performance in the audio task. These findings, discussed in the light of recent research, suggest that unilateral CI is deficient in supporting auditory VWM in DHH. At the same time, bilateral CI would allow the DHH child to approach the VWM benchmark for NH children. The present study suggests the possible effectiveness of EEG in supporting, through a targeted approach, the diagnosis and rehabilitation of VWM in DHH children.}, }
@article {pmid38608024, year = {2024}, author = {Abbasi, A and Rangwani, R and Bowen, DW and Fealy, AW and Danielsen, NP and Gulati, T}, title = {Cortico-cerebellar coordination facilitates neuroprosthetic control.}, journal = {Science advances}, volume = {10}, number = {15}, pages = {eadm8246}, pmid = {38608024}, issn = {2375-2548}, support = {R00 NS097620/NS/NINDS NIH HHS/United States ; R01 NS128469/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Rats ; *Cerebellum ; *Brain-Computer Interfaces ; Cell Nucleus ; Learning ; Movement ; }, abstract = {Temporally coordinated neural activity is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence demonstrating how this coordinated activity within cortical and subcortical regions governs behavior. We investigated this between the primary motor (M1) and contralateral cerebellar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task. In neuroprosthetic task, actuator movements are causally linked to M1 "direct" neurons that drive the decoder for successful task execution. However, it is unknown how task-related M1 activity interacts with the cerebellum. We observed a notable 3 to 6 hertz coherence that emerged between these regions' local field potentials (LFPs) with learning that also modulated task-related spiking. We identified robust task-related indirect modulation in the cerebellum, which developed a preferential relationship with M1 task-related activity. Inhibiting cerebellar cortical and deep nuclei activity through optogenetics led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that cerebellar influence is necessary for M1-driven neuroprosthetic control.}, }
@article {pmid38607193, year = {2025}, author = {Kong, D and Chen, Y and Wang, L and Lu, Y and Luo, S and Chai, H and Chen, L}, title = {Adoption of Rehabilitation Climbing Wall Combined with Brain-computer Fusion Interface in Adolescent Idiopathic Scoliosis.}, journal = {Alternative therapies in health and medicine}, volume = {31}, number = {1}, pages = {208-215}, pmid = {38607193}, issn = {1078-6791}, mesh = {Humans ; *Scoliosis/rehabilitation ; Adolescent ; Female ; Male ; Quality of Life ; *Brain-Computer Interfaces ; *Exercise Therapy/methods ; Child ; Range of Motion, Articular/physiology ; }, abstract = {BACKGROUND: As the adoption of brain-computer interface (BCI) technology in rehabilitation training is gradually maturing, the rehabilitation climbing walls combined with BCI technology are applied in adolescent idiopathic scoliosis (AIS) adoption research.<br><br>METHODS: From January 2022 to January 2023, a total of 100 AIS patients were assigned into a control group (group C, rehabilitation climbing wall training) and an observation group (group B, rehabilitation climbing wall training based on BCI technology) equally and randomly. The therapeutic effects of the patients were analyzed, including the Cobb angle, waist range of motion, and quality of life.<br><br>RESULTS: The Cobb angles of all patients after three months of treatment were obviously smaller than those preoperatively, and the Cobb angle of patients in group B was smaller than that of group C. The improvement rate of the Cobb angle of patients in group B was substantially superior to that in group C (95%CI&#xa0;17.8-42.6, P = .034). Moreover, patients in groups C and B had more extensive waist flexion, tension, and left ranges. Suitable lateral regions after three months of treatment than before and lower lumbar dysfunction scores, and group B was significantly better than group C (95%CI&#xa0;20.3-35.4, P = .042). After three months of treatment, all patients' general condition, physical pain, physiological function, and mental health scores were higher than those preoperatively, and the scores in group B were substantially superior to those in group C (95%CI&#xa0;51.3-84.2, P = .022). Furthermore, the total effective rate of patients in group B after three months was markedly superior to that in group C (96% vs. 82%) (95%CI&#xa0;79.3-97.2, P = .014).<br><br>CONCLUSION: The results of the study suggest that the rehabilitation climbing wall training method combined with brain-computer interface (BCI) technology has significant therapeutic effects on adolescent idiopathic scoliosis (AIS) patients. The intervention was found to effectively reduce the Cobb angle, increase the lumbar range of motion, improve lumbar function, and enhance the quality of life of the patients. These findings indicate that the adoption of rehabilitation climbing walls combined with BCI technology can be clinically valuable in the treatment of AIS. This approach holds promise in improving the rehabilitation outcomes for AIS patients, providing a non-invasive alternative to surgical interventions.}, }
@article {pmid38606614, year = {2024}, author = {Xu, S and Xiao, X and Manshaii, F and Chen, J}, title = {Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging.}, journal = {Nano letters}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.nanolett.4c00815}, pmid = {38606614}, issn = {1530-6992}, abstract = {Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage in traditional optogenetics. By focusing on calcium fluorescence for imaging rather than stimulation, injectable fluorescent neural interfaces significantly minimize photothermal damage and improve the accuracy of neuronal imaging. Key advancements including the use of injectable microelectronics for targeted electrical stimulation and their integration with cell-specific genetically encoded calcium indicators have been discussed. These injectable electronics that allow for post-treatment retrieval offer a minimally invasive solution, enhancing both usability and reliability. Furthermore, the integration of genetically encoded fluorescent calcium indicators with injectable bioelectronics enables precise neuronal recording and imaging of individual neurons. This shift not only minimizes risks such as photothermal conversion but also boosts safety, specificity, and effectiveness of neural imaging. Embracing these advancements represents a significant leap forward in biomedical engineering and neuroscience, paving the way for advanced brain-machine interfaces.}, }
@article {pmid38606309, year = {2024}, author = {Jeong, CH and Lim, H and Lee, J and Lee, HS and Ku, J and Kang, YJ}, title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1373589}, pmid = {38606309}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity.<br><br>MATERIALS AND METHODS: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.<br><br>RESULTS: The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.<br><br>CONCLUSION: The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.}, }
@article {pmid38606308, year = {2024}, author = {Xue, Q and Song, Y and Wu, H and Cheng, Y and Pan, H}, title = {Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1309594}, pmid = {38606308}, issn = {1662-4548}, abstract = {INTRODUCTION: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.<br><br>METHODS: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.<br><br>RESULTS AND DISCUSSION: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.}, }
@article {pmid38604523, year = {2024}, author = {Liu, L and Li, J and Ouyang, R and Zhou, D and Fan, C and Liang, W and Li, F and Lv, Z and Wu, X}, title = {Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.}, journal = {Journal of neuroscience methods}, volume = {406}, number = {}, pages = {110132}, doi = {10.1016/j.jneumeth.2024.110132}, pmid = {38604523}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Exoskeleton Device ; Male ; *Neurofeedback/methods/instrumentation ; Evoked Potentials, Visual/physiology ; Adult ; Brain/physiology/physiopathology ; Female ; Young Adult ; Imagination/physiology ; Imagery, Psychotherapy/methods ; }, abstract = {BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling.<br><br>NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients.<br><br>In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system.<br><br>RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively.<br><br>CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.}, }
@article {pmid38603901, year = {2024}, author = {Shi, X and She, Q and Fang, F and Meng, M and Tan, T and Zhang, Y}, title = {Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.}, journal = {Computers in biology and medicine}, volume = {174}, number = {}, pages = {108445}, doi = {10.1016/j.compbiomed.2024.108445}, pmid = {38603901}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Algorithms ; *Brain-Computer Interfaces ; *Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Brain/physiology ; Female ; }, abstract = {Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL. Specifically, we first selected the source domain based on Mahalanobis distance to enhance the quality of the source domains and then used manifold feature mapping approach to map the source and target domains on the Grassmann manifold to mitigate data drift between domains. In this newly established shared space, we optimized the Mahalanobis metric by maximizing the inter-class distances while minimizing the intra-class distances in the target domain. Recognizing that significant distribution discrepancies might persist across different domains even on the manifold, to ensure similar distributions between the source and target domains, we further imposed constraints on both domains under the Mahalanobis metric. This approach aims to reduce distributional disparities and enhance the electroencephalogram (EEG) emotion recognition performance. In cross-subject experiments, the MSMMTL model exhibits average classification accuracies of 88.83 % and 65.04 % for SEED and DEAP, respectively, underscoring the superiority of our proposed MSMMTL over other state-of-the-art methods. MSMMTL can effectively solve the problem of individual differences in EEG-based affective computing.}, }
@article {pmid38603841, year = {2024}, author = {De Rubis, G and Paudel, KR and Yeung, S and Mohamad, S and Sudhakar, S and Singh, SK and Gupta, G and Hansbro, PM and Chellappan, DK and Oliver, BGG and Dua, K}, title = {18-β-glycyrrhetinic acid-loaded polymeric nanoparticles attenuate cigarette smoke-induced markers of impaired antiviral response in vitro.}, journal = {Pathology, research and practice}, volume = {257}, number = {}, pages = {155295}, doi = {10.1016/j.prp.2024.155295}, pmid = {38603841}, issn = {1618-0631}, mesh = {Humans ; *Glycyrrhetinic Acid/pharmacology/analogs &amp; derivatives ; *Nanoparticles ; Antiviral Agents/pharmacology ; Smoke/adverse effects ; Polylactic Acid-Polyglycolic Acid Copolymer/chemistry ; Cell Line ; Pulmonary Disease, Chronic Obstructive/drug therapy/immunology ; Epithelial Cells/drug effects/virology ; Cigarette Smoking/adverse effects ; }, abstract = {Tobacco smoking is a leading cause of preventable mortality, and it is the major contributor to diseases such as COPD and lung cancer. Cigarette smoke compromises the pulmonary antiviral immune response, increasing susceptibility to viral infections. There is currently no therapy that specifically addresses the problem of impaired antiviral response in cigarette smokers and COPD patients, highlighting the necessity to develop novel treatment strategies. 18-β-glycyrrhetinic acid (18-β-gly) is a phytoceutical derived from licorice with promising anti-inflammatory, antioxidant, and antiviral activities whose clinical application is hampered by poor solubility. This study explores the therapeutic potential of an advanced drug delivery system encapsulating 18-β-gly in poly lactic-co-glycolic acid (PLGA) nanoparticles in addressing the impaired antiviral immunity observed in smokers and COPD patients. Exposure of BCi-NS1.1 human bronchial epithelial cells to cigarette smoke extract (CSE) resulted in reduced expression of critical antiviral chemokines (IP-10, I-TAC, MIP-1α/1β), mimicking what happens in smokers and COPD patients. Treatment with 18-β-gly-PLGA nanoparticles partially restored the expression of these chemokines, demonstrating promising therapeutic impact. The nanoparticles increased IP-10, I-TAC, and MIP-1α/1β levels, exhibiting potential in attenuating the negative effects of cigarette smoke on the antiviral response. This study provides a novel approach to address the impaired antiviral immune response in vulnerable populations, offering a foundation for further investigations and potential therapeutic interventions. Further studies, including a comprehensive in vitro characterization and in vivo testing, are warranted to validate the therapeutic efficacy of 18-β-gly-PLGA nanoparticles in respiratory disorders associated with compromised antiviral immunity.}, }
@article {pmid38602850, year = {2024}, author = {Wang, Z and Hu, H and Zhou, T and Xu, T and Zhao, X}, title = {Average Time Consumption per Character: A Practical Performance Metric for Generic Synchronous BCI Spellers.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {9}, pages = {2684-2698}, doi = {10.1109/TBME.2024.3387469}, pmid = {38602850}, issn = {1558-2531}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Communication Devices for People with Disabilities ; }, abstract = {OBJECTIVE: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed.<br><br>METHODS: In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR.<br><br>RESULTS: Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic.<br><br>CONCLUSION: The proposed ATCPC metric is valid.<br><br>SIGNIFICANCE: ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.}, }
@article {pmid38602573, year = {2024}, author = {Waisberg, E and Ong, J and Lee, AG}, title = {Ethical Considerations of Neuralink and Brain-Computer Interfaces.}, journal = {Annals of biomedical engineering}, volume = {52}, number = {8}, pages = {1937-1939}, pmid = {38602573}, issn = {1573-9686}, mesh = {Humans ; Brain/physiology ; *Brain-Computer Interfaces/ethics ; }, abstract = {Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.}, }
@article {pmid38601924, year = {2024}, author = {Anger, JT and Case, LK and Baranowski, AP and Berger, A and Craft, RM and Damitz, LA and Gabriel, R and Harrison, T and Kaptein, K and Lee, S and Murphy, AZ and Said, E and Smith, SA and Thomas, DA and Valdés Hernández, MDC and Trasvina, V and Wesselmann, U and Yaksh, TL}, title = {Pain mechanisms in the transgender individual: a review.}, journal = {Frontiers in pain research (Lausanne, Switzerland)}, volume = {5}, number = {}, pages = {1241015}, pmid = {38601924}, issn = {2673-561X}, abstract = {SPECIFIC AIM: Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.<br><br>METHODS: A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.<br><br>RESULTS: While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.<br><br>CONCLUSIONS: While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.}, }
@article {pmid38601801, year = {2024}, author = {Li, H and Li, H and Ma, L and Polina, D}, title = {Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1377233}, pmid = {38601801}, issn = {1662-5161}, abstract = {INTRODUCTION: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.<br><br>METHODS: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.<br><br>RESULTS: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.<br><br>DISCUSSION: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.}, }
@article {pmid38601800, year = {2024}, author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y}, title = {Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1391550}, pmid = {38601800}, issn = {1662-5161}, abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.}, }
@article {pmid38598676, year = {2024}, author = {Gancio, J and Masoller, C and Tirabassi, G}, title = {Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.}, journal = {Chaos (Woodbury, N.Y.)}, volume = {34}, number = {4}, pages = {}, doi = {10.1063/5.0200029}, pmid = {38598676}, issn = {1089-7682}, mesh = {Humans ; Entropy ; *Brain ; *Electroencephalography/methods ; Brain Mapping/methods ; Signal Processing, Computer-Assisted ; }, abstract = {Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.}, }
@article {pmid38598403, year = {2024}, author = {Li, D and Wang, X and Dou, M and Zhao, Y and Cui, X and Xiang, J and Wang, B}, title = {Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1606-1615}, doi = {10.1109/TNSRE.2024.3387283}, pmid = {38598403}, issn = {1558-0210}, mesh = {Humans ; *Evoked Potentials, Visual ; Calibration ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Algorithms ; }, abstract = {UNLABELLED: Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA).<br><br>METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial.<br><br>RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01&#xb1;10.10 bits/min, 172.31&#xb1;7.26 bits/min, and 139.04&#xb1;14.90 bits/min for all three datasets, respectively.<br><br>CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.}, }
@article {pmid38598402, year = {2024}, author = {Qin, K and Xu, R and Li, S and Wang, X and Cichocki, A and Jin, J}, title = {A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1596-1605}, doi = {10.1109/TNSRE.2024.3386763}, pmid = {38598402}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Pattern Recognition, Automated/methods ; Recognition, Psychology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; }, abstract = {Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.}, }
@article {pmid38593021, year = {2024}, author = {Niu, X and Lu, N and Yan, R and Luo, H}, title = {Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {6}, pages = {3434-3445}, doi = {10.1109/JBHI.2024.3386565}, pmid = {38593021}, issn = {2168-2208}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Algorithms ; Deep Learning ; Imagination/physiology ; Brain/physiology ; }, abstract = {Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement of the training samples makes real-time MI BCI systems impossible to implement based on deep networks, which restricts the related researches within laboratory and makes practical application hard to accomplish. To address this issue, a double-point observation deep network (DoNet) is developed to classify ultra-short samples buried in noise. First, an analytical solution is developed theoretically to perform ultra-short signal classification based on double-point couples. Then, a signal-noise model is constructed to study the interference of noise on classification based on double-point couples. Based on which, an independent identical distribution condition is utilized to improve the classification accuracy in a data-driven manner. Combining the theoretical model and data-driven mechanism, DoNet can construct a steady data-distribution for the double-point couples of the samples with the same label. Therefore, the conditional probability of each double-point couple of a test sample can be obtained. With a voting strategy, the samples can be accurately classified by fusing these conditional probabilities. Meanwhile, the noise interference can be suppressed. DoNet has been evaluated on two public EEG datasets. Compared to most state-of-the-art methods, the 1s-long EEG signal classification accuracy has been improved by more than 3%.}, }
@article {pmid38592090, year = {2024}, author = {Ille, N}, title = {Orthogonal extended infomax algorithm.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad38db}, pmid = {38592090}, issn = {1741-2552}, mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Normal Distribution ; }, abstract = {Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.}, }
@article {pmid38590363, year = {2024}, author = {Osuna-Orozco, R and Zhao, Y and Stealey, HM and Lu, HY and Contreras-Hernandez, E and Santacruz, SR}, title = {Adaptation and learning as strategies to maximize reward in neurofeedback tasks.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1368115}, pmid = {38590363}, issn = {1662-5161}, abstract = {INTRODUCTION: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.<br><br>METHODS: Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent.<br><br>RESULTS AND DISCUSSION: Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.}, }
@article {pmid38589428, year = {2024}, author = {Lotun, S and Lamarche, VM and Matran-Fernandez, A and Sandstrom, GM}, title = {People perceive parasocial relationships to be effective at fulfilling emotional needs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {8185}, pmid = {38589428}, issn = {2045-2322}, mesh = {Humans ; Emotions ; Loneliness ; *Emotional Regulation ; Friends ; *Social Media ; }, abstract = {People regularly form one-sided, "parasocial" relationships (PSRs) with targets incapable of returning the sentiment. Past work has shown that people engage with PSRs to support complex psychological needs (e.g., feeling less lonely after watching a favorite movie). However, we do not know how people rate these relationships relative to traditional two-sided relationships in terms of their effectiveness in supporting psychological needs. The current research (Ntotal = 3085) examined how PSRs help people fulfil emotion regulation needs. In Studies 1 and 2, participants felt that both their YouTube creator and non-YouTube creator PSRs were more effective at fulfilling their emotional needs than in-person acquaintances, albeit less effective than close others. In Study 3, people with high self-esteem thought PSRs would be responsive to their needs when their sociometer was activated, just as they do with two-sided relationships.}, }
@article {pmid38589229, year = {2024}, author = {Falaki, A and Quessy, S and Dancause, N}, title = {Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {21}, pages = {}, pmid = {38589229}, issn = {1529-2401}, mesh = {Animals ; *Motor Cortex/physiology ; *Hand Strength/physiology ; Male ; *Macaca mulatta ; *Psychomotor Performance/physiology ; *Functional Laterality/physiology ; Movement/physiology ; Hand/physiology ; }, abstract = {Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and β activity increased during contralateral grasp. In contrast, β oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.}, }
@article {pmid38587944, year = {2024}, author = {Carrara, I and Papadopoulo, T}, title = {Classification of BCI-EEG Based on the Augmented Covariance Matrix.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {9}, pages = {2651-2662}, doi = {10.1109/TBME.2024.3386219}, pmid = {38587944}, issn = {1558-2531}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {OBJECTIVE: Electroencephalography signals are recorded as multidimensional datasets. We propose a new framework based on the augmented covariance that stems from an autoregressive model to improve motor imagery classification.<br><br>METHODS: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix. The state-of the art for classifying covariance matrices is based on Riemannian Geometry. A fairly natural idea is therefore to apply this Riemannian Geometry based approach to these augmented covariance matrices. The methodology for creating the augmented covariance matrix shows a natural connection with the delay embedding theorem proposed by Takens for dynamical systems. Such an embedding method is based on the knowledge of two parameters: the delay and the embedding dimension, respectively related to the lag and the order of the autoregressive model. This approach provides new methods to compute the hyper-parameters in addition to standard grid search.<br><br>RESULTS: The augmented covariance matrix performed ACMs better than any state-of-the-art methods. We will test our approach on several datasets and several subjects using the MOABB framework, using both within-session and cross-session evaluation.<br><br>CONCLUSION: The improvement in results is due to the fact that the augmented covariance matrix incorporates not only spatial but also temporal information. As such, it contains information on the nonlinear components of the signal through the embedding procedure, which allows the leveraging of dynamical systems algorithms.<br><br>SIGNIFICANCE: These results extend the concepts and the results of the Riemannian distance based classification algorithm.}, }
@article {pmid38586334, year = {2024}, author = {Curà, F and Sesana, R and Corsaro, L and Dugand, MM}, title = {An Active Thermography approach for materials characterisation of thermal management systems for Lithium-ion batteries.}, journal = {Heliyon}, volume = {10}, number = {7}, pages = {e28587}, pmid = {38586334}, issn = {2405-8440}, abstract = {The aim of this work is an alternative non destructive technique for estimating the thermal properties of four different Thermal Management System (TMS) materials. More in detail, a thermographic setup realized with the Active Thermography approach (AT) is utilized for the purpose and the data elaboration follows the ISO 18755 Standard. As well known, Phase Changes Materials (PCMs) represent an innovative solution in the Thermal Management System (TMS) of Lithium-Ion batteries and, during the years, many solutions were developed to improve its thermal properties. As a matter of fact, parameters such as the internal temperature or heat exchanges impact on both efficiency and safety of the whole battery system. Consequently, the thermal conductivity was often chosen as a performance indicator of Thermal Management System (TMS) materials. In this work, both thermal diffusivity and thermal conductivity were estimated in two different testing conditions, respectively at room temperature and higher temperature conditions. The Active Thermography (AT) technique proposed in this activity has satisfactory estimated both thermal diffusivity and thermal conductivity of Thermal Management System (TMS) materials. An analytical model was also developed to reproduce the temperature experimental profiles. Finally, results obtained with AT approach were compared to those available from commercial datasheet and literature.}, }
@article {pmid38586195, year = {2024}, author = {Ma, P and Dong, C and Lin, R and Liu, H and Lei, D and Chen, X and Liu, H}, title = {A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1306283}, pmid = {38586195}, issn = {1662-4548}, abstract = {BACKGROUND: The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.<br><br>OBJECTIVE: This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.<br><br>METHODS: The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.<br><br>RESULTS: For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal.<br><br>CONCLUSION: The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.}, }
@article {pmid38586146, year = {2024}, author = {Shuqfa, Z and Lakas, A and Belkacem, AN}, title = {Increasing accessibility to a large brain-computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification.}, journal = {Data in brief}, volume = {54}, number = {}, pages = {110181}, pmid = {38586146}, issn = {2352-3409}, abstract = {A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.}, }
@article {pmid38586082, year = {2024}, author = {Mueller, NN and Kim, Y and Ocoko, MYM and Dernelle, P and Kale, I and Patwa, S and Hermoso, AC and Chirra, D and Capadona, JR and Hess-Dunning, A}, title = {Effects of Micromachining on Anti-oxidant Elution from a Mechanically-Adaptive Polymer.}, journal = {Journal of micromechanics and microengineering : structures, devices, and systems}, volume = {34}, number = {3}, pages = {}, pmid = {38586082}, issn = {0960-1317}, support = {I01 RX003083/RX/RRD VA/United States ; IK6 RX003077/RX/RRD VA/United States ; }, abstract = {Intracortical microelectrodes (IMEs) can be used to restore motor and sensory function as a part of brain-computer interfaces in individuals with neuromusculoskeletal disorders. However, the neuroinflammatory response to IMEs can result in their premature failure, leading to reduced therapeutic efficacy. Mechanically-adaptive, resveratrol-eluting (MARE) neural probes target two mechanisms believed to contribute to the neuroinflammatory response by reducing the mechanical mismatch between the brain tissue and device, as well as locally delivering an antioxidant therapeutic. To create the mechanically-adaptive substrate, a dispersion, casting, and evaporation method is used, followed by a microfabrication process to integrate functional recording electrodes on the material. Resveratrol release experiments were completed to generate a resveratrol release profile and demonstrated that the MARE probes are capable of long-term controlled release. Additionally, our results showed that resveratrol can be degraded by laser-micromachining, an important consideration for future device fabrication. Finally, the electrodes were shown to have a suitable impedance for single-unit neural recording and could record single units in vivo.}, }
@article {pmid38585364, year = {2024}, author = {Ben Pazi, H and Jahashan, S and Har Nof, S and Zibman, S and Yanai-Kohelet, O and Prigan, L and Intrator, N and Bornstein, NM and Ribo, M}, title = {Pre-hospital stroke monitoring past, present, and future: a perspective.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1341170}, pmid = {38585364}, issn = {1664-2295}, abstract = {Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.}, }
@article {pmid38585226, year = {2023}, author = {Yuvaraj, M and Raja, P and David, A and Burdet, E and Skm, V and Balasubramanian, S}, title = {A systematic investigation of detectors for low signal-to-noise ratio EMG signals.}, journal = {F1000Research}, volume = {12}, number = {}, pages = {429}, pmid = {38585226}, issn = {2046-1402}, mesh = {*Electromyography/methods ; Humans ; *Signal-To-Noise Ratio ; *Algorithms ; *Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.<br><br>METHODS: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.<br><br>RESULTS: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.<br><br>CONCLUSIONS: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.}, }
@article {pmid38584867, year = {2024}, author = {Khan, AYY and Anjum, A and Qadri, HM}, title = {Ethical tightrope: Navigating neuro-ethics in brain computer interface (BCI) technology.}, journal = {Brain &amp; spine}, volume = {4}, number = {}, pages = {102800}, pmid = {38584867}, issn = {2772-5294}, }
@article {pmid38581031, year = {2024}, author = {Ferrero, L and Soriano-Segura, P and Navarro, J and Jones, O and Ortiz, M and Iáñez, E and Azorín, JM and Contreras-Vidal, JL}, title = {Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {48}, pmid = {38581031}, issn = {1743-0003}, support = {FPU19/03165//Ministry of Science, Innovation and Universities through the Aid for the Training of University Teachers/ ; PID2021-124111OB-C31//MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe/ ; }, mesh = {Humans ; *Deep Learning ; *Exoskeleton Device ; *Brain-Computer Interfaces ; Algorithms ; Lower Extremity ; Electroencephalography/methods ; }, abstract = {BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.<br><br>METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding.<br><br>RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.<br><br>CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.}, }
@article {pmid38580923, year = {2024}, author = {Allonen, S and Aittoniemi, J and Vuorialho, M and Närhi, L and Panula, K and Vuento, R and Honkaniemi, J}, title = {Streptococcus intermedius causing primary bacterial ventriculitis in a patient with severe periodontitis - a case report.}, journal = {BMC neurology}, volume = {24}, number = {1}, pages = {112}, pmid = {38580923}, issn = {1471-2377}, support = {EVO; 1326/2010//the State Research Funding of Vaasa Hospital District/ ; }, mesh = {Male ; Humans ; Middle Aged ; Streptococcus intermedius ; *Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy ; Anti-Bacterial Agents/therapeutic use ; *Meningitis/diagnosis ; *Central Nervous System Bacterial Infections ; *Periodontitis/complications/drug therapy ; }, abstract = {BACKGROUND: Streptococcus intermedius is a member of the S. anginosus group and is part of the normal oral microbiota. It can cause pyogenic infections in various organs, primarily in the head and neck area, including brain abscesses and meningitis. However, ventriculitis due to periodontitis has not been reported previously.<br><br>CASE PRESENTATION: A 64-year-old male was admitted to the hospital with a headache, fever and later imbalance, blurred vision, and general slowness. Neurological examination revealed nuchal rigidity and general clumsiness. Meningitis was suspected, and the patient was treated with dexamethasone, ceftriaxone and acyclovir. A brain computer tomography (CT) scan was normal, and cerebrospinal fluid (CSF) Gram staining and bacterial cultures remained negative, so the antibacterial treatment was discontinued. Nine days after admission, the patient's condition deteriorated. The antibacterial treatment was restarted, and a brain magnetic resonance imaging revealed ventriculitis. A subsequent CT scan showed hydrocephalus, so a ventriculostomy was performed. In CSF Gram staining, chains of gram-positive cocci were observed. Bacterial cultures remained negative, but a bacterial PCR detected Streptococcus intermedius. An orthopantomography revealed advanced periodontal destruction in several teeth and periapical abscesses, which were subsequently operated on. The patient was discharged in good condition after one month.<br><br>CONCLUSIONS: Poor dental health can lead to life-threatening infections in the central nervous system, even in a completely healthy individual. Primary bacterial ventriculitis is a diagnostic challenge, which may result in delayed treatment and increased mortality.}, }
@article {pmid38580626, year = {2024}, author = {Chen, F and Zheng, J and Wang, L and Krajbich, I}, title = {Attribute latencies causally shape intertemporal decisions.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {2948}, pmid = {38580626}, issn = {2041-1723}, support = {2148982//National Science Foundation (NSF)/ ; 72002195//National Natural Science Foundation of China (National Science Foundation of China)/ ; 71871199//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72371226//National Natural Science Foundation of China (National Science Foundation of China)/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; }, mesh = {Humans ; Animals ; Mice ; Time Factors ; *Delay Discounting ; Reward ; Reaction Time ; Choice Behavior/physiology ; }, abstract = {Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.}, }
@article {pmid38580047, year = {2024}, author = {Wang, C and Sun, Y and Xing, Y and Liu, K and Xu, K}, title = {Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model.}, journal = {Brain research}, volume = {1835}, number = {}, pages = {148914}, doi = {10.1016/j.brainres.2024.148914}, pmid = {38580047}, issn = {1872-6240}, mesh = {*Habenula/physiology/physiopathology ; Animals ; *Stress, Psychological/physiopathology ; *Depression/physiopathology ; *Restraint, Physical/methods ; Male ; Disease Models, Animal ; Deep Brain Stimulation/methods ; Rats ; Rats, Sprague-Dawley ; }, abstract = {Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.}, }
@article {pmid38579958, year = {2024}, author = {Lo, YT and Lim, MJR and Kok, CY and Wang, S and Blok, SZ and Ang, TY and Ng, VYP and Rao, JP and Chua, KSG}, title = {Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis.}, journal = {Archives of physical medicine and rehabilitation}, volume = {105}, number = {12}, pages = {2336-2349}, doi = {10.1016/j.apmr.2024.04.001}, pmid = {38579958}, issn = {1532-821X}, mesh = {Humans ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/methods ; Exoskeleton Device ; Neural Prostheses ; Neurological Rehabilitation/methods ; Recovery of Function ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; }, abstract = {OBJECTIVE: To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.<br><br>DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.<br><br>STUDY SELECTION: Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.<br><br>DATA EXTRACTION: Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.<br><br>DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs &#x2264;4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively).<br><br>CONCLUSION: Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.}, }
@article {pmid38579696, year = {2024}, author = {Ali, YH and Bodkin, K and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, LR and Au Yong, N and Stavisky, SD and Miller, LE and Brandman, DM and Pandarinath, C}, title = {BRAND: a platform for closed-loop experiments with deep network models.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, pmid = {38579696}, issn = {1741-2552}, support = {K12 HD073945/HD/NICHD NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; Neural Networks, Computer ; *Brain-Computer Interfaces ; *Neurosciences ; }, abstract = {Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.}, }
@article {pmid38578854, year = {2024}, author = {Lin, PJ and Li, W and Zhai, X and Li, Z and Sun, J and Xu, Q and Pan, Y and Ji, L and Li, C}, title = {Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1546-1555}, doi = {10.1109/TNSRE.2024.3384498}, pmid = {38578854}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Stroke ; Electroencephalography/methods ; *Stroke Rehabilitation/methods ; }, abstract = {Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.}, }
@article {pmid38577666, year = {2024}, author = {Liu, F and Zheng, H and Ma, S and Zhang, W and Liu, X and Chua, Y and Shi, L and Zhao, R}, title = {Advancing brain-inspired computing with hybrid neural networks.}, journal = {National science review}, volume = {11}, number = {5}, pages = {nwae066}, pmid = {38577666}, issn = {2053-714X}, abstract = {Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.}, }
@article {pmid38576451, year = {2024}, author = {Ning, M and Duwadi, S and Yücel, MA and von Lühmann, A and Boas, DA and Sen, K}, title = {fNIRS dataset during complex scene analysis.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1329086}, pmid = {38576451}, issn = {1662-5161}, support = {T32 DC013017/DC/NIDCD NIH HHS/United States ; }, }
@article {pmid38572293, year = {2024}, author = {}, title = {Correction to: Transfer learning promotes acquisition of individual BCI skills.}, journal = {PNAS nexus}, volume = {3}, number = {4}, pages = {pgae137}, doi = {10.1093/pnasnexus/pgae137}, pmid = {38572293}, issn = {2752-6542}, abstract = {[This corrects the article DOI: 10.1093/pnasnexus/pgae076.].}, }
@article {pmid38572110, year = {2024}, author = {Wang, L and Hong, W and Zhu, H and He, Q and Yang, B and Wang, J and Weng, Q}, title = {Macrophage senescence in health and diseases.}, journal = {Acta pharmaceutica Sinica. B}, volume = {14}, number = {4}, pages = {1508-1524}, pmid = {38572110}, issn = {2211-3835}, abstract = {Macrophage senescence, manifested by the special form of durable cell cycle arrest and chronic low-grade inflammation like senescence-associated secretory phenotype, has long been considered harmful. Persistent senescence of macrophages may lead to maladaptation, immune dysfunction, and finally the development of age-related diseases, infections, autoimmune diseases, and malignancies. However, it is a ubiquitous, multi-factorial, and dynamic complex phenomenon that also plays roles in remodeled processes, including wound repair and embryogenesis. In this review, we summarize some general molecular changes and several specific biomarkers during macrophage senescence, which may bring new sight to recognize senescent macrophages in different conditions. Also, we take an in-depth look at the functional changes in senescent macrophages, including metabolism, autophagy, polarization, phagocytosis, antigen presentation, and infiltration or recruitment. Furthermore, some degenerations and diseases associated with senescent macrophages as well as the mechanisms or relevant genetic regulations of senescent macrophages are integrated, not only emphasizing the possibility of regulating macrophage senescence to benefit age-associated diseases but also has an implication on the finding of potential targets or drugs clinically.}, }
@article {pmid38570593, year = {2024}, author = {van Stuijvenberg, OC and Broekman, MLD and Wolff, SEC and Bredenoord, AL and Jongsma, KR}, title = {Developer perspectives on the ethics of AI-driven neural implants: a qualitative study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {7880}, pmid = {38570593}, issn = {2045-2322}, support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; }, mesh = {*Artificial Intelligence ; Reproducibility of Results ; Qualitative Research ; Focus Groups ; *Cochlear Implants ; }, abstract = {Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users' safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.}, }
@article {pmid38570113, year = {2024}, author = {Sun, WB and Fu, JX and Chen, YL and Li, HF and Wu, ZY and Chen, DF}, title = {Both gain- and loss-of-function variants of KCNA1 are associated with paroxysmal kinesigenic dyskinesia.}, journal = {Journal of genetics and genomics = Yi chuan xue bao}, volume = {51}, number = {8}, pages = {801-810}, doi = {10.1016/j.jgg.2024.03.013}, pmid = {38570113}, issn = {1673-8527}, mesh = {Humans ; *Kv1.1 Potassium Channel/genetics ; Male ; Female ; *Pedigree ; *Dystonia/genetics/pathology ; *Mutation, Missense/genetics ; Exome Sequencing ; Loss of Function Mutation/genetics ; Adult ; Gain of Function Mutation/genetics ; Child ; Adolescent ; Genetic Predisposition to Disease ; HEK293 Cells ; Ataxia ; Myokymia ; }, abstract = {KCNA1 is the coding gene for Kv1.1 voltage-gated potassium-channel α subunit. Three variants of KCNA1 have been reported to manifest as paroxysmal kinesigenic dyskinesia (PKD), but the correlation between them remains unclear due to the phenotypic complexity of KCNA1 variants as well as the rarity of PKD cases. Using the whole exome sequencing followed by Sanger sequencing, we screen for potential pathogenic KCNA1 variants in patients clinically diagnosed with paroxysmal movement disorders and identify three previously unreported missense variants of KCNA1 in three unrelated Chinese families. The proband of one family (c.496G>A, p.A166T) manifests as episodic ataxia type 1, and the other two (c.877G>A, p.V293I and c.1112C>A, p.T371A) manifest as PKD. The pathogenicity of these variants is confirmed by functional studies, suggesting that p.A166T and p.T371A cause a loss-of-function of the channel, while p.V293I leads to a gain-of-function with the property of voltage-dependent gating and activation kinetic affected. By reviewing the locations of PKD-manifested KCNA1 variants in Kv1.1 protein, we find that these variants tend to cluster around the pore domain, which is similar to epilepsy. Thus, our study strengthens the correlation between KCNA1 variants and PKD and provides more information on genotype-phenotype correlations of KCNA1 channelopathy.}, }
@article {pmid38565846, year = {2024}, author = {Li, H and Li, Z and Yuan, X and Tian, Y and Ye, W and Zeng, P and Li, XM and Guo, F}, title = {Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {2834}, pmid = {38565846}, issn = {2041-1723}, mesh = {Animals ; Circadian Rhythm/physiology ; Temperature ; Sleep/physiology ; Drosophila ; *Circadian Clocks/physiology ; *Drosophila Proteins/genetics ; Drosophila melanogaster/physiology ; }, abstract = {The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.}, }
@article {pmid38565100, year = {2024}, author = {Li, W and Li, H and Sun, X and Kang, H and An, S and Wang, G and Gao, Z}, title = {Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad3986}, pmid = {38565100}, issn = {1741-2552}, mesh = {*Learning ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; Algorithms ; }, abstract = {Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.}, }
@article {pmid38563704, year = {2024}, author = {Yang, Q and Wu, B and Castagnola, E and Pwint, MY and Williams, NP and Vazquez, AL and Cui, XT}, title = {Integrated Microprism and Microelectrode Array for Simultaneous Electrophysiology and Two-Photon Imaging across All Cortical Layers.}, journal = {Advanced healthcare materials}, volume = {13}, number = {24}, pages = {e2302362}, pmid = {38563704}, issn = {2192-2659}, support = {R01 NS110564/NS/NINDS NIH HHS/United States ; R01NS089688/GF/NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; BRAINR01NS110564/GF/NIH HHS/United States ; F32 MH132145/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; *Microelectrodes ; Mice ; Cerebral Cortex/physiology/diagnostic imaging ; Electrophysiology/methods/instrumentation ; Microscopy, Fluorescence, Multiphoton/methods/instrumentation ; Electrophysiological Phenomena ; Neurons/physiology ; }, abstract = {Cerebral neural electronics play a crucial role in neuroscience research with increasing translational applications such as brain-computer interfaces for sensory input and motor output restoration. While widely utilized for decades, the understanding of the cellular mechanisms underlying this technology remains limited. Although two-photon microscopy (TPM) has shown great promise in imaging superficial neural electrodes, its application to deep-penetrating electrodes is technically difficult. Here, a novel device integrating transparent microelectrode arrays with glass microprisms, enabling electrophysiology recording and stimulation alongside TPM imaging across all cortical layers in a vertical plane, is introduced. Tested in Thy1-GCaMP6 mice for over 4 months, the integrated device demonstrates the capability for multisite electrophysiological recording/stimulation and simultaneous TPM calcium imaging. As a proof of concept, the impact of microstimulation amplitude, frequency, and depth on neural activation patterns is investigated using the setup. With future improvements in material stability and single unit yield, this multimodal tool greatly expands integrated electrophysiology and optical imaging from the superficial brain to the entire cortical column, opening new avenues for neuroscience research and neurotechnology development.}, }
@article {pmid38562772, year = {2025}, author = {Rajeswaran, P and Payeur, A and Lajoie, G and Orsborn, AL}, title = {Assistive sensory-motor perturbations influence learned neural representations.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.03.20.585972}, pmid = {38562772}, issn = {2692-8205}, support = {R01 NS134634/NS/NINDS NIH HHS/United States ; }, abstract = {Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for an unexpectedly small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.}, }
@article {pmid38562360, year = {2024}, author = {Ilchev, B and Chervenkov, V and Valchev, N and Nakov, V and Minchev, T and Vassilev, G and Tsvetanov, T and Laleva, L and Milev, M and Spiriev, T}, title = {Interdisciplinary Successful Revascularization of Traumatic Occlusion of the Right Common Carotid Artery.}, journal = {Cureus}, volume = {16}, number = {3}, pages = {e55395}, pmid = {38562360}, issn = {2168-8184}, abstract = {Blunt carotid artery injury (BCI) poses a rare yet severe threat following vascular trauma, often leading to significant morbidity and mortality. We present a case of a 33-year-old male who suffered complete thrombotic occlusion of the right common carotid artery (CCA) following a workplace accident. Clinical evaluation revealed profound neurological deficits, prompting multidisciplinary surgical intervention guided by the Denver criteria (Grade I - disruption inside the vessel that results in a narrowing of the lumen by less than 25%; Grade II - dissection or intramural hematoma causing greater than 25% stenosis; Grade III - comprises pseudoaneurysm formation; Grade IV - causes total vessel occlusion; Grade V - describes vessel transection with extravasation). Surgical exploration unveiled extensive arterial damage, necessitating thrombectomy, primary repair, and double-layered patch angioplasty using an autologous saphenous vein. Postoperative recovery was uneventful, with the restoration of pulsatile blood flow confirmed by Doppler ultrasound. Three-month follow-up demonstrated patent arterial reconstruction and improved cerebral perfusion, despite the persistent neurological deficits. Our case underscores the challenges in diagnosing and managing BCI, advocating for a tailored approach based on injury severity and neurological status. While conservative management remains standard, surgical intervention offers a viable option in select cases, particularly those with complete vessel occlusion and neurological compromise. Long-term surveillance is imperative to assess the durability of arterial reconstruction and monitor for recurrent thromboembolic events. Further research is warranted to refine management algorithms and elucidate optimal treatment strategies in this rare but critical vascular pathology.}, }
@article {pmid38560190, year = {2024}, author = {Akuthota, S and K, R and Ravichander, J}, title = {Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.}, journal = {Heliyon}, volume = {10}, number = {7}, pages = {e27198}, pmid = {38560190}, issn = {2405-8440}, abstract = {This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a &amp; 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.}, }
@article {pmid38560116, year = {2024}, author = {Chen, D}, title = {Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification.}, journal = {Heliyon}, volume = {10}, number = {7}, pages = {e28235}, pmid = {38560116}, issn = {2405-8440}, abstract = {BACKGROUND: Traditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets.<br><br>NEW METHOD: To solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification.<br><br>RESULTS: The improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets.<br><br>CONCLUSIONS: The proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.}, }
@article {pmid38558011, year = {2024}, author = {Chen, D and Zhao, Z and Zhang, S and Chen, S and Wu, X and Shi, J and Liu, N and Pan, C and Tang, Y and Meng, C and Zhao, X and Tao, B and Liu, W and Chen, D and Ding, H and Zhang, P and Tang, Z}, title = {Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.}, journal = {Translational stroke research}, volume = {}, number = {}, pages = {}, pmid = {38558011}, issn = {1868-601X}, support = {2022B37//Research Fund of Tongji Hospital/ ; 92148206//National Natural Science Foundation of China/ ; }, abstract = {Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.}, }
@article {pmid38557253, year = {2024}, author = {Van Horn, AL and Burgess, JR}, title = {From Blunt Cardiac Injury to Heart Transplant Following Motorcycle Collision.}, journal = {The American surgeon}, volume = {90}, number = {8}, pages = {2080-2082}, doi = {10.1177/00031348241241699}, pmid = {38557253}, issn = {1555-9823}, mesh = {Humans ; Male ; *Heart Transplantation/adverse effects ; *Accidents, Traffic ; *Motorcycles ; Young Adult ; *Wounds, Nonpenetrating/complications/surgery ; *Heart Injuries/etiology/surgery ; }, abstract = {Traumatic coronary artery occlusion and dissection is an exceedingly rare complication of blunt cardiac injury (BCI), though it has been previously noted in a number of case reports. However, it can also lead to heart transplant, which to our knowledge has not been previously described in the literature. We present a case of a healthy 24-year-old man without significant past medical history who was in a motorcycle accident, resulting in sternal fracture and BCI. He was ultimately found to have thrombotic occlusion and dissection of his left anterior descending artery (LAD), requiring mechanical thrombectomy and drug-eluting stent, as well as subsequent hospitalizations and operations due to various complications. It was suspected that he went into ventricular fibrillation and had a second motorcycle collision, resulting in cardiogenic shock. Ultimately, his progression of ischemic cardiomyopathy and mitral regurgitation led to the need for heart transplant. Blunt cardiac injury with myocardial contusion has such a broad range of pathologies. It is essential that patients with these injury patterns raise a high level of suspicion for BCI and are followed closely with appropriate diagnostic testing and rapid intervention for best possible outcomes.}, }
@article {pmid38557034, year = {2024}, author = {Ling, W and Shang, X and Yu, C and Li, C and Xu, K and Feng, L and Wei, Y and Tang, T and Huang, X}, title = {Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.}, journal = {ACS nano}, volume = {18}, number = {15}, pages = {10596-10608}, doi = {10.1021/acsnano.4c00632}, pmid = {38557034}, issn = {1936-086X}, mesh = {Rats ; Animals ; *Dopamine/analysis ; *Metal-Organic Frameworks/metabolism ; Fluorescent Dyes/metabolism ; Fluorescence ; Brain/diagnostic imaging/metabolism ; Neurotransmitter Agents/metabolism ; }, abstract = {Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 μm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.}, }
@article {pmid38555287, year = {2024}, author = {Wei, M and Xu, K and Tang, B and Li, J and Yun, Y and Zhang, P and Wu, Y and Bao, K and Lei, K and Chen, Z and Ma, H and Sun, C and Liu, R and Li, M and Li, L and Lin, H}, title = {Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {2786}, pmid = {38555287}, issn = {2041-1723}, support = {91950204, 62105287, 61975179, and 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Monolithic integration of novel materials without modifying the existing photonic component library is crucial to advancing heterogeneous silicon photonic integrated circuits. Here we show the introduction of a silicon nitride etch stop layer at select areas, coupled with low-loss oxide trench, enabling incorporation of functional materials without compromising foundry-verified device reliability. As an illustration, two distinct chalcogenide phase change materials (PCMs) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated, offering compact phase and intensity tuning units with zero-static power consumption. By employing these building blocks, the phase error of a push-pull Mach-Zehnder interferometer optical switch could be reduced with a 48% peak power consumption reduction. Mirco-ring filters with >5-bit wavelength selective intensity modulation and waveguide-based >7-bit intensity-modulation broadband attenuators could also be achieved. This foundry-compatible platform could open up the possibility of integrating other excellent optoelectronic materials into future silicon photonic process design kits.}, }
@article {pmid38554856, year = {2024}, author = {Bader, ER and Boro, AD and Killian, NJ and Eskandar, EN}, title = {A method for precisely timed, on-demand intracranial stimulation using the RNS device.}, journal = {Brain stimulation}, volume = {17}, number = {2}, pages = {444-447}, pmid = {38554856}, issn = {1876-4754}, mesh = {Humans ; *Deep Brain Stimulation/instrumentation/methods ; }, }
@article {pmid38554787, year = {2024}, author = {Chunduri, V and Aoudni, Y and Khan, S and Aziz, A and Rizwan, A and Deb, N and Keshta, I and Soni, M}, title = {Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification.}, journal = {Journal of neuroscience methods}, volume = {406}, number = {}, pages = {110128}, doi = {10.1016/j.jneumeth.2024.110128}, pmid = {38554787}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; Attention/physiology ; Neural Networks, Computer ; Motor Activity/physiology ; Brain/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.<br><br>NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.<br><br>RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.<br><br>In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.<br><br>CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.}, }
@article {pmid38552161, year = {2024}, author = {Wang, W and Zhou, H and Xu, Z and Li, Z and Zhang, L and Wan, P}, title = {Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {36}, number = {31}, pages = {e2401035}, doi = {10.1002/adma.202401035}, pmid = {38552161}, issn = {1521-4095}, support = {52222303//National Natural Science Foundation of China/ ; XK2022-03//BRC-BC/ ; //Fundamental Research Funds for the Central Universities/ ; }, mesh = {*Hydrogels/chemistry ; Humans ; *Wearable Electronic Devices ; *Machine Learning ; Anti-Bacterial Agents/pharmacology/chemistry ; Electric Conductivity ; Animals ; Skin ; Mice ; Adhesiveness ; Ultraviolet Rays ; Electronics ; }, abstract = {Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV) protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected electromyogram (EMG) signals is designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility, and effective hemostasis properties for promising bacterial-infected wound bleeding.}, }
@article {pmid38550646, year = {2024}, author = {Bossi, F and Ciardo, F and Mostafaoui, G}, title = {Editorial: Neurocognitive features of human-robot and human-machine interaction.}, journal = {Frontiers in psychology}, volume = {15}, number = {}, pages = {1394970}, pmid = {38550646}, issn = {1664-1078}, }
@article {pmid38550567, year = {2024}, author = {Racz, FS and Kumar, S and Kaposzta, Z and Alawieh, H and Liu, DH and Liu, R and Czoch, A and Mukli, P and Millán, JDR}, title = {Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1271831}, pmid = {38550567}, issn = {1662-4548}, abstract = {Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.}, }
@article {pmid38547834, year = {2024}, author = {Abbott, JR and Jeakle, EN and Haghighi, P and Usoro, JO and Sturgill, BS and Wu, Y and Geramifard, N and Radhakrishna, R and Patnaik, S and Nakajima, S and Hess, J and Mehmood, Y and Devata, V and Vijayakumar, G and Sood, A and Doan Thai, TT and Dogra, K and Hernandez-Reynoso, AG and Pancrazio, JJ and Cogan, SF}, title = {Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex.}, journal = {Biomaterials}, volume = {308}, number = {}, pages = {122543}, pmid = {38547834}, issn = {1878-5905}, support = {R01 NS104344/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Silicon Compounds/chemistry ; *Rats, Sprague-Dawley ; *Microelectrodes ; Female ; *Motor Cortex/physiology/cytology ; *Carbon Compounds, Inorganic/chemistry ; Rats ; *Electrodes, Implanted ; Neurons/physiology ; }, abstract = {Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm[2] on week 1 and 55.9 mC/cm[2] on week 16, and at 50,000 mV/s, 4.27 mC/cm[2] on week 1 and 5.93 mC/cm[2] on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.}, }
@article {pmid38545737, year = {2024}, author = {Ergün, E and Aydemir, &#xd6; and Korkmaz, OE}, title = {Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-16}, doi = {10.1080/10255842.2024.2333924}, pmid = {38545737}, issn = {1476-8259}, abstract = {In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.}, }
@article {pmid39323876, year = {2023}, author = {Varshney, S and Farias, D and Brandman, DM and Stavisky, SD and Miller, LM}, title = {Using Automatic Speech Recognition to Measure the Intelligibility of Speech Synthesized from Brain Signals.}, journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering}, volume = {2023}, number = {}, pages = {}, pmid = {39323876}, issn = {1948-3546}, support = {UL1 TR001860/TR/NCATS NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCIs) can potentially restore lost function in patients with neurological injury. A promising new application of BCI technology has focused on speech restoration. One approach is to synthesize speech from the neural correlates of a person who cannot speak, as they attempt to do so. However, there is no established gold-standard for quantifying the quality of BCI-synthesized speech. Quantitative metrics, such as applying correlation coefficients between true and decoded speech, are not applicable to anarthric users and fail to capture intelligibility by actual human listeners; by contrast, methods involving people completing forced-choice multiple-choice questionnaires are imprecise, not practical at scale, and cannot be used as cost functions for improving speech decoding algorithms. Here, we present a deep learning-based "AI Listener" that can be used to evaluate BCI speech intelligibility objectively, rapidly, and automatically. We begin by adapting several leading Automatic Speech Recognition (ASR) deep learning models - Deepspeech, Wav2vec 2.0, and Kaldi - to suit our application. We then evaluate the performance of these ASRs on multiple speech datasets with varying levels of intelligibility, including: healthy speech, speech from people with dysarthria, and synthesized BCI speech. Our results demonstrate that the multiple-language ASR model XLSR-Wav2vec 2.0, trained to output phonemes, yields superior performance in terms of speech transcription accuracy. Notably, the AI Listener reports that several previously published BCI output datasets are not intelligible, which is consistent with human listeners.}, }
@article {pmid38751471, year = {2023}, author = {Li, S and Yu, X and Xu, Y}, title = {Breast cancer gene expression signatures: development and clinical significance-a narrative review.}, journal = {Translational breast cancer research : a journal focusing on translational research in breast cancer}, volume = {4}, number = {}, pages = {7}, pmid = {38751471}, issn = {2218-6778}, abstract = {BACKGROUND AND OBJECTIVE: Breast cancer gene expression signatures are developing rapidly and are expected to better understand the intrinsic features of the tumor, and also to optimize the treatment strategy in clinical practice. This review is to summarize the controversy and consensus in clinical practice of gene expression signatures, and to provide our perspective on these issues as well as recommendation for future direction.<br><br>METHODS: We reviewed English publications in PubMed related to breast cancer gene expression signatures from 2002 to 2022.<br><br>KEY CONTENT AND FINDINGS: Five mature commercial gene expression signatures: Oncotype, MammaPrint, Prosigna/PAM50, EndoPredict and Breast Cancer Index (BCI) are available to provide the prognostic and predictive assessment. Although they could help to evaluate the risk of recurrence and to predict the benefits of certain treatments, their applications remain challenging. Treatment decisions should be determined by a combination of related clinical pathological factors in clinical practice.<br><br>CONCLUSIONS: Gene expression signatures could assist in the determination of the adjuvant therapy of early-stage breast cancer. The prospective randomized clinical trials showed that chemotherapy may be exempted in low-risk patients. More sufficient data are expected for the application in radiotherapy, extended endocrine therapy, and neoadjuvant treatment. The treatment cannot be determined by a single factor but by comprehensive assessments of clinicopathological factors, test purpose, and cost-effectiveness. Patients will benefit from personalized treatments with the publication of further evidence.}, }
@article {pmid38699292, year = {2024}, author = {Zhang, Y and Lv, Q and Yin, Y and Wang, H and Bueber, MA and Phillips, MR and Li, T}, title = {Research in China about the biological mechanisms that potentially link socioenvironmental changes and mental health: a scoping review.}, journal = {The Lancet regional health. Western Pacific}, volume = {45}, number = {}, pages = {100610}, pmid = {38699292}, issn = {2666-6065}, abstract = {China's rapid socioeconomic development since 1990 makes it a fitting location to summarise research about how biological changes associated with socioenvironmental changes affect population mental health and, thus, lay the groundwork for subsequent, more focused studies. An initial search identified 308 review articles in the international literature about biomarkers associated with 12 common mental health disorders. We then searched for studies conducted in China that assessed the association of the identified mental health related-biomarkers with socioenvironmental factors in English-language and Chinese-language databases. We located 1330 articles published between 1 January 1990 and 1 August 2021 that reported a total of 3567 associations between 56 specific biomarkers and 11 socioenvironmental factors: 3156 (88·5%) about six types of environmental pollution, 381 (10·7%) about four health-related behaviours (diet, physical inactivity, internet misuse, and other lifestyle factors), and 30 (0·8%) about socioeconomic inequity. Only 245 (18·4%) of the papers simultaneously considered the possible effect of the biomarkers on mental health conditions; moreover, most of these studies assessed biomarkers in animal models of mental disorders, not human subjects. Among the 245 papers, mental health conditions were linked with biomarkers of environmental pollution in 188 (76·7%), with biomarkers of health-related behaviours in 48 (19·6%), and with biomarkers of socioeconomic inequality in 9 (3·7%). The 604 biomarker-mental health condition associations reported (107 in human subjects and 497 in animal models) included 379 (62·7%) about cognitive functioning, 117 (19·4%) about anxiety, 56 (9·3%) about depression, 21 (3·5%) about neurodevelopmental conditions, and 31 (5·1%) about neurobehavioural symptoms. Improved understanding of the biological mechanisms linking socioenvironmental changes to community mental health will require expanding the range of socioenvironmental factors considered, including mental health outcomes in more of the studies about the association of biomarkers with socioenvironmental factors, and increasing the proportion of studies that assess mental health outcomes in humans.}, }
@article {pmid39169955, year = {2022}, author = {Ukhovskyi, V and Pyskun, A and Korniienko, L and Aliekseieva, H and Moroz, O and Pyskun, O and Kyivska, G and Mezhenskyi, A}, title = {Serological prevalence of Leptospira serovars among pigs in Ukraine during the period of 2001-2019.}, journal = {Veterinarni medicina}, volume = {67}, number = {1}, pages = {13-27}, pmid = {39169955}, issn = {0375-8427}, abstract = {Leptospirosis is a widespread infection among pigs throughout the world. In most cases in Ukraine, only the microscopic agglutination test (MAT) is used for the diagnosis of leptospirosis in animals. In general, during the period of 2001-2019, 2 381 163 samples of blood sera from swine were tested in our country and 85 338 positive reactions were received, which is 3.58% [binomial confidence intervals (BCI), 3.56-3.61%]. It was established that the serovars copenhageni - 33.91% (BCI, 33.59-34.23%), bratislava - 14.14% (BCI, 13.90-14.37%), pomona - 8.58% (BCI, 8.39-8.77%), and tarassovi - 7.12% (BCI, 6.95-7.30%) play a leading role in the aetiological structure of swine leptospirosis. A large number of positive reactions to several serovars was observed - 29.78% (BCI, 29.47-30.09%) of the total number of positive cases. In addition, the article presents data according to a retrospective analysis of the eight serovars circulating among pigs in Ukraine. Thus, during the nineteen year period, there was a decrease in the number of positive reactions to bratislava, pomona, and tarassovi and an increase in the number of positive reactions to copenhageni, polonica, and kabura. Mapping Ukraine's territory for leptospirosis among pigs was carried out. This allows one to identify zones with a risk of leptospirosis infections among swine. The maps show that the highest incidence rates were identified in the eastern and central parts of Ukraine.}, }
@article {pmid38993225, year = {2021}, author = {Osawa, T and Wei, JT and Abe, T and Honda, M and Rew, KT and Dunn, R and Yamada, S and Furumido, J and Kikuchi, H and Matsumoto, R and Sato, Y and Harabayashi, T and Takada, N and Minami, K and Morita, K and Kashiwagi, A and Fukuhara, S and Murai, S and Ito, YM and Ogasawara, K and Shinohara, N}, title = {Comparison of Health-Related Quality of Life Between Japanese and American Patients with Bladder Cancer as Measured by a Newly Developed Japanese Version of the Bladder Cancer Index.}, journal = {Bladder cancer (Amsterdam, Netherlands)}, volume = {7}, number = {1}, pages = {61-69}, pmid = {38993225}, issn = {2352-3735}, abstract = {INTRODUCTION: The aim of this study is to characterize health related quality of life (HRQOL) in Japanese patients after bladder cancer surgery and to perform cross-cultural comparison between Japanese and American patients.<br><br>METHODS: Firstly, we cross-sectionally assessed HRQOL of 371 patients in Japan using the Bladder Cancer Index (BCI-Japanese). HRQOL of the four groups of patients (native bladder without intravesical therapy, native bladder with intravesicaltherapy, cystectomy with ileal conduit, and cystectomy with neobladder) were assessed. Secondly, we compared the Japanese with the American cohort (n&#x200a;=&#x200a;315) from the original BCI paper. After adjusting for age and gender, the differences in each BCI subdomain score was analyzed.<br><br>RESULTS: Among Japanese patients, the urinary domain function score was significantly lower among the cystectomy with neobladder group, compared to the cystectomy with ileal conduit group (p&#x200a;<&#x200a;0. 01). Despite this, the urinary bother was comparable between the two groups. Although there were apparent differences between Japanese and American patients, there were few differences in Urinary and Bowel HRQOL. In three of the four treatment groups (other than native bladder with intravesical therapy), Japanese patients were more likely than Americans to report poor sexual function (p&#x200a;<&#x200a;0.05). However, Japanese patients were less likely than Americans to be bothered by their lower sexual function, regardless of treatment (p&#x200a;<&#x200a;0.05).<br><br>CONCLUSIONS: HRQOL outcomes following treatment of bladder cancer in Japan are comparable to those in the USA, except for sexual functioning and sexual bother. The BCI can be used for cross-cultural assessments of HRQOL in bladder cancer patients.}, }
@article {pmid38624425, year = {2021}, author = {Bergsman, KC and Chudler, EH}, title = {Adapting a Neural Engineering Summer Camp for High School Students to a Fully Online Experience.}, journal = {Biomedical engineering education}, volume = {1}, number = {1}, pages = {37-42}, pmid = {38624425}, issn = {2730-5945}, abstract = {The COVID-19 pandemic and its resulting health and safety concerns caused the cancellation of many engineering education opportunities for high school students. To expose high school students to the field of neural engineering and encourage them to pursue academic pathways in biomedical engineering, the Center for Neurotechnology (CNT) at the University of Washington converted an in-person summer camp to a fully online program (Virtual REACH Program, VRP) offering both synchronous and asynchronous resources. The VRP is a five-day online program that focuses on a different daily theme (neuroscience, brain-computer interfaces, electrical stimulation, neuroethics, career/academic pathways). Each day, the VRP starts with a live videoconference meeting (lecture and interactive discussion) with a CNT faculty member. The online lectures are supported by at-home learning resources (e.g., text, videos, activities, quizzes) embedded within a digital book created using the Pressbook platform. An online bulletin board (Padlet) is also used by students to share artifacts and build community. Program evaluation will be conducted by an external evaluator. A summative survey will collect information on participants' experiences in the VRP and will help inform future iterations of the program. Although significant time was required to create a digital book, the VRP will reach a larger audience than the prior in-person program and resulted in the creation of learning tools that can be used in the future.}, }
@article {pmid38566861, year = {2019}, author = {Fontaine, AK and Segil, JL and Caldwell, JH and Weir, RFF}, title = {Real-Time Prosthetic Digit Actuation by Optical Read-out of Activity-Dependent Calcium Signals in an Ex Vivo Peripheral Nerve.}, journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering}, volume = {2019}, number = {}, pages = {143-146}, pmid = {38566861}, issn = {1948-3546}, support = {OT2 OD023852/OD/NIH HHS/United States ; }, abstract = {Improved neural interfacing strategies are needed for the full articulation of advanced prostheses. To address limitations of existing control interface designs, the work of our laboratory has presented an optical approach to reading activity from individual nerve fibers using activity-dependent calcium transients. Here, we demonstrate the feasibility of such signals to control prosthesis actuation by using the axonal fluorescence signal in an ex vivo mouse nerve to drive a prosthetic digit in real-time. Additionally, signals of varying action potential frequency are streamed post hoc to the prosthesis, showing graded motor output and the potential for proportional neural control. This proof-of-concept work is a novel demonstration of the functional use of activity-dependent optical read-out in the nerve.}, }
@article {pmid39449990, year = {2018}, author = {Mashamba-Thompson, TP and Moodley, P and Sartorius, B and Drain, PK}, title = {Evaluation of antenatal rapid human immunodeficiency virus testing in rural South Africa.}, journal = {Southern African journal of HIV medicine}, volume = {19}, number = {1}, pages = {771}, pmid = {39449990}, issn = {2078-6751}, abstract = {INTRODUCTION: South African guidelines recommend two rapid tests for diagnosing human immunodeficiency virus (HIV) using the serial HIV testing algorithm, but the accuracy and compliance to this algorithm is unknown in rural clinics. We evaluated the accuracy of HIV rapid testing and the time to receiving test results among pregnant women in rural KwaZulu-Natal (KZN).<br><br>METHOD: We observed the accuracy of rapid HIV testing algorithms for 208 consenting antenatal patients accessing voluntary HIV testing services in nine rural primary healthcare (PHC) clinics in KZN. A PHC-based HIV counsellor obtained finger-prick whole blood from each participant to perform rapid testing using the Advanced Quality&#x2122; One Step anti-HIV (1&amp;2) and/or ABON&#x2122; HIV 1/2/O Tri-Line HIV test. A research nurse obtained venous blood for an enzyme-linked immunosorbent assay (ELISA) HIV test, which is the gold standard diagnostic test. We recorded the time of receipt of HIV test results for each test.<br><br>RESULTS: Among 208 pregnant women with a mean age of 26 years, 72 women from nine rural PHC clinics were identified as HIV-positive by two rapid tests with an HIV-prevalence of 35% (95% Bayesian credibility intervals [BCI]: 28% - 41%). Of the 208 patients, 135 patients from six clinics were tested with the serial HIV testing algorithm. The estimated sensitivity and specificity for the 135 participants were 100% (95% confidence interval [CI]: 93% - 100%) and 99% (CI: 95% - 100%), respectively. The positive predictive value and negative predictive value were estimated at 98% (CI: 94% - 100%) and 95% (CI: 88% - 99%), respectively. All women received their HIV rapid test results within 20 min of testing. Test stock-out resulted in poor test availability at point-of-care, preventing performance of a second HIV test in three out of nine PHC clinics in rural KZN.<br><br>CONCLUSION: Despite the poor compliance with national guidelines for HIV rapid testing services, HIV rapid test results provided to pregnant women in rural PHC clinics in KZN were generally accurate and timely. Test stock-out was shown to be one of the barriers to test availability in rural PHC clinics, resulting in poor compliance with guidelines. We recommend a compulsory confirmation HIV rapid test for all HIV-negative test results obtained from pregnant patients in rural and resource-limited settings.}, }
@article {pmid38544185, year = {2024}, author = {Albán-Escobar, M and Navarrete-Arroyo, P and De la Cruz-Guevara, DR and Tobar-Quevedo, J}, title = {Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {6}, pages = {}, pmid = {38544185}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Electroencephalography/methods ; *Self-Help Devices ; Evoked Potentials, Visual ; Photic Stimulation ; }, abstract = {This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.}, }
@article {pmid38541720, year = {2024}, author = {Pais-Vieira, C and Figueiredo, JG and Perrotta, A and Matos, D and Aguiar, M and Ramos, J and Gato, M and Poleri, T and Pais-Vieira, M}, title = {Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.}, journal = {Life (Basel, Switzerland)}, volume = {14}, number = {3}, pages = {}, pmid = {38541720}, issn = {2075-1729}, support = {UIDP/04501/2020, UIDB/04279/2020, FCT/IF/00098/2015, project CISUC -UID/CEC/00326/2020, and via the doctoral scholarship 2023.02051.BD//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 95/2016//Bial (Portugal)/ ; (grant agreement No. 779963)//European Union's Horizon 2020 Research and Innovation Programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement No. 779963) Thertact-Fb and Thertact- NEXT/ ; MC-12-2018//SANTA CASA Pr&#xe9;mios Neuroci&#xea;ncias Melo e Castro/ ; }, abstract = {Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.}, }
@article {pmid38539656, year = {2024}, author = {Yao, X and Li, T and Ding, P and Wang, F and Zhao, L and Gong, A and Nan, W and Fu, Y}, title = {Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.}, journal = {Brain sciences}, volume = {14}, number = {3}, pages = {}, pmid = {38539656}, issn = {2076-3425}, support = {62376112, 82172058, 81771926, 61763022, and 62006246.//National Natural Science Foundation of China/ ; }, abstract = {OBJECTIVES: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification.<br><br>METHODS: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax.<br><br>RESULTS: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset.<br><br>CONCLUSIONS: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies.<br><br>SIGNIFICANCE: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.}, }
@article {pmid38539622, year = {2024}, author = {Kuang, M and Zhan, Z and Gao, S}, title = {Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.}, journal = {Brain sciences}, volume = {14}, number = {3}, pages = {}, pmid = {38539622}, issn = {2076-3425}, support = {2019SCUH0007//Sichuan University Innovation Spark Project/ ; }, abstract = {Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain-computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node-edge interaction and a multi-scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node-edge interaction (NEI-GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi-stage reconstruction network that restricts the reconstructed images in a coarse-to-fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state-of-the-art methods in terms of accuracy in the commonly used n-way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n-way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n-way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual-decoding brain-computer interfaces.}, }
@article {pmid38539605, year = {2024}, author = {Gu, X and Jiang, L and Chen, H and Li, M and Liu, C}, title = {Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.}, journal = {Brain sciences}, volume = {14}, number = {3}, pages = {}, pmid = {38539605}, issn = {2076-3425}, support = {2023YFC3604100//the National Key Research and Development Program of China/ ; YG2021169//the Art Planning Program of Jiangxi Province/ ; YS22247//Jiangxi University Humanities and Social Science Program/ ; }, abstract = {In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.}, }
@article {pmid38539602, year = {2024}, author = {Niu, C and Yan, Z and Yin, K and Zhou, S}, title = {Identification and Verification of Error-Related Potentials Based on Cerebellar Targets.}, journal = {Brain sciences}, volume = {14}, number = {3}, pages = {}, pmid = {38539602}, issn = {2076-3425}, support = {62293552//National Natural Science Foundation of China/ ; }, abstract = {The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.}, }
@article {pmid38539585, year = {2024}, author = {Wu, S and Bhadra, K and Giraud, AL and Marchesotti, S}, title = {Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.}, journal = {Brain sciences}, volume = {14}, number = {3}, pages = {}, pmid = {38539585}, issn = {2076-3425}, support = {#51NF40_180888/SNSF_/Swiss National Science Foundation/Switzerland ; }, abstract = {Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and remain unchanged throughout the BCI use. However, this approach might be inadequate to effectively handle the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use, as parameters are expected to change, and all the more in a real-time setting. To address this limitation, we developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI control setting. Twenty healthy participants performed two BCI control sessions based on the imagery of two syllables, using a static LDA and an adaptive LDA classifier, in randomized order. As hypothesized, the adaptive classifier led to better performances than the static one in this real-time BCI control task. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets, acquired using the same syllable imagery task. These findings highlight the effectiveness and reliability of adaptive LDA classifiers for real-time imagined speech decoding. Such an improvement can shorten the training time and favor the development of multi-class BCIs, representing a clear interest for non-invasive systems notably characterized by low decoding accuracies.}, }
@article {pmid38538229, year = {2024}, author = {Andrade, K and Houmani, N and Guieysse, T and Razafimahatratra, S and Klarsfeld, A and Dreyfus, G and Dubois, B and Vialatte, F and Medani, T}, title = {Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {3}, pages = {67}, doi = {10.31083/j.jin2303067}, pmid = {38538229}, issn = {0219-6352}, support = {//URGOTECH/ ; }, mesh = {Humans ; Aged ; *Neurofeedback/methods ; Electroencephalography ; Brain/physiology ; Cognition/physiology ; *Alzheimer Disease/therapy ; Biomarkers ; }, abstract = {BACKGROUND: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly.<br><br>METHODS AND OBJECTIVES: Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation.<br><br>RESULTS: A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre- versus post-training neuropsychological tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result.<br><br>CONCLUSION: The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.}, }
@article {pmid38538143, year = {2024}, author = {Wei 魏赣超, G and Tajik Mansouri زینب تاجیک منصوری, Z and Wang 王晓婧, X and Stevenson, IH}, title = {Calibrating Bayesian Decoders of Neural Spiking Activity.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {18}, pages = {}, pmid = {38538143}, issn = {1529-2401}, mesh = {Animals ; *Bayes Theorem ; Male ; Rats ; *Neurons/physiology ; *Action Potentials/physiology ; Calibration ; Mice ; Motor Cortex/physiology ; Macaca mulatta ; Hippocampus/physiology ; Photic Stimulation/methods ; Models, Neurological ; }, abstract = {Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.}, }
@article {pmid38538142, year = {2024}, author = {Lee, WH and Karpowicz, BM and Pandarinath, C and Rouse, AG}, title = {Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {20}, pages = {}, pmid = {38538142}, issn = {1529-2401}, support = {DP2 NS127291/NS/NINDS NIH HHS/United States ; R00 NS101127/NS/NINDS NIH HHS/United States ; K99 NS101127/NS/NINDS NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; }, mesh = {Animals ; Male ; *Macaca mulatta ; *Psychomotor Performance/physiology ; *Motor Cortex/physiology ; *Neurons/physiology ; Movement/physiology ; Deep Learning ; Action Potentials/physiology ; }, abstract = {Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an autoencoder-based deep-learning model, was applied to provide a clearer picture of neurons' activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.}, }
@article {pmid38538056, year = {2024}, author = {Brannigan, J and McClanahan, A and Hui, F and Fargen, KM and Pinter, N and Oxley, TJ}, title = {Superior cortical venous anatomy for endovascular device implantation: a systematic review.}, journal = {Journal of neurointerventional surgery}, volume = {16}, number = {12}, pages = {1353-1359}, doi = {10.1136/jnis-2023-021434}, pmid = {38538056}, issn = {1759-8486}, mesh = {Humans ; *Cerebral Veins/anatomy &amp; histology/surgery ; *Endovascular Procedures/methods ; Cerebral Cortex/anatomy &amp; histology/blood supply/surgery ; Brain-Computer Interfaces ; Electrodes, Implanted ; Superior Sagittal Sinus/anatomy &amp; histology/surgery ; }, abstract = {Endovascular electrode arrays provide a minimally invasive approach to access intracranial structures for neural recording and stimulation. These arrays are currently used as brain-computer interfaces (BCIs) and are deployed within the superior sagittal sinus (SSS), although cortical vein implantation could improve the quality and quantity of recorded signals. However, the anatomy of the superior cortical veins is heterogenous and poorly characterised. MEDLINE and Embase databases were systematically searched from inception to December 15, 2023 for studies describing the anatomy of the superior cortical veins. A total of 28 studies were included: 19 cross-sectional imaging studies, six cadaveric studies, one intraoperative anatomical study and one review. There was substantial variability in cortical vein diameter, length, confluence angle, and location relative to the underlying cortex. The mean number of SSS branches ranged from 11 to 45. The vein of Trolard was most often reported as the largest superior cortical vein, with a mean diameter ranging from 2.1 mm to 3.3 mm. The mean vein of Trolard was identified posterior to the central sulcus. One study found a significant age-related variability in cortical vein diameter and another identified myoendothelial sphincters at the base of the cortical veins. Cortical vein anatomical data are limited and inconsistent. The vein of Trolard is the largest tributary vein of the SSS; however, its relation to the underlying cortex is variable. Variability in cortical vein anatomy may necessitate individualized pre-procedural planning of training and neural decoding in endovascular BCI. Future focus on the relation to the underlying cortex, sulcal vessels, and vessel wall anatomy is required.}, }
@article {pmid38537269, year = {2024}, author = {Soldado-Magraner, J and Antonietti, A and French, J and Higgins, N and Young, MJ and Larrivee, D and Monteleone, R}, title = {Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad3852}, pmid = {38537269}, issn = {1741-2552}, support = {F32 MH123001/MH/NIMH NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Paralysis ; *Neurosciences ; }, abstract = {Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.}, }
@article {pmid38537268, year = {2024}, author = {Suematsu, N and Vazquez, AL and Kozai, TDY}, title = {Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, pmid = {38537268}, issn = {1741-2552}, support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R01 NS117515/NS/NINDS NIH HHS/United States ; R01 NS119410/NS/NINDS NIH HHS/United States ; }, mesh = {Mice ; Animals ; *Calcium ; Photic Stimulation ; Oxyhemoglobins ; Neurons/physiology ; *Visual Cortex ; Electric Stimulation/methods ; }, abstract = {Objective. Intracortical microstimulation (ICMS) can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site.Approach. Different microstimulation frequencies were investigatedin vivoon Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging.Main results. Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies.Significance. These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by ICMS and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.}, }
@article {pmid38536681, year = {2024}, author = {Liang, G and Cao, D and Wang, J and Zhang, Z and Wu, Y}, title = {EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1535-1545}, doi = {10.1109/TNSRE.2024.3382226}, pmid = {38536681}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Imagination ; }, abstract = {The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.}, }
@article {pmid38533741, year = {2024}, author = {Siu, C and Aoude, M and Andersen, J and Adams, KD}, title = {The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {19}, number = {7}, pages = {2641-2650}, doi = {10.1080/17483107.2024.2333884}, pmid = {38533741}, issn = {1748-3115}, mesh = {Humans ; *Children with Disabilities/rehabilitation ; *Brain-Computer Interfaces ; *Play and Playthings ; Male ; Child ; Female ; *Parents/psychology ; Adolescent ; Qualitative Research ; Interviews as Topic ; }, abstract = {Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children's play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children's play can be interpreted as passive or active depending on one's definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents' views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.Implications for RehabilitationAssistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factorsFuture research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselvesBrain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.}, }
@article {pmid38533483, year = {2024}, author = {Wang, G and Tang, J and Yin, Z and Yu, S and Shi, X and Hao, X and Zhao, Z and Pan, Y and Li, S}, title = {The neurocomputational signature of decision-making for unfair offers in females under acute psychological stress.}, journal = {Neurobiology of stress}, volume = {30}, number = {}, pages = {100622}, pmid = {38533483}, issn = {2352-2895}, abstract = {Stress is a crucial factor affecting social decision-making. However, its impacts on the behavioral and neural processes of females' unfairness decision-making remain unclear. Combining computational modeling and functional near-infrared spectroscopy (fNIRS), this study attempted to illuminate the neurocomputational signature of unfairness decision-making in females. We also considered the effect of trait stress coping styles. Forty-four healthy young females (20.98 ± 2.89 years) were randomly assigned to the stress group (n = 21) and the control group (n = 23). Acute psychosocial stress was induced by the Trier Social Stress Test (TSST), and participants then completed the one-shot ultimatum game (UG) as responders. The results showed that acute psychosocial stress reduced the adaptability to fairness and lead to more random decision-making responses. Moreover, in the stress group, a high level of negative coping style predicted more deterministic decision. fNIRS results showed that stress led to an increase of oxy-hemoglobin (HbO) peak in the right temporoparietal junction (rTPJ), while decreased the activation of left middle temporal gyrus (lMTG) when presented the moderately unfair (MU) offers. This signified more involvement of the mentalization and the inhibition of moral processing. Moreover, individuals with higher negative coping scores showed more deterministic decision behaviors under stress. Taken together, our study emphasizes the role of acute psychosocial stress in affecting females' unfairness decision-making mechanisms in social interactions, and provides evidences for the "tend and befriend" pattern based on a cognitive neuroscience perspec.}, }
@article {pmid38532987, year = {2024}, author = {Hu, S and Ng, CH and Mann, JJ}, title = {Editorial: Linking treatment target identification to biological mechanisms underlying mood disorders - Volume II.}, journal = {Frontiers in psychiatry}, volume = {15}, number = {}, pages = {1385955}, doi = {10.3389/fpsyt.2024.1385955}, pmid = {38532987}, issn = {1664-0640}, }
@article {pmid38532608, year = {2024}, author = {Assi, DS and Huang, H and Karthikeyan, V and Theja, VCS and de Souza, MM and Roy, VAL}, title = {Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {36}, number = {27}, pages = {e2306254}, doi = {10.1002/adma.202306254}, pmid = {38532608}, issn = {1521-4095}, support = {EP/X016846/1//EPSRC/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Quantum Theory ; Synapses/physiology ; Electroencephalography ; Brain/physiology ; Evoked Potentials/physiology ; Electronics ; }, abstract = {Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.}, }
@article {pmid38531360, year = {2024}, author = {Losey, DM and Hennig, JA and Oby, ER and Golub, MD and Sadtler, PT and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Yu, BM and Chase, SM}, title = {Learning leaves a memory trace in motor cortex.}, journal = {Current biology : CB}, volume = {34}, number = {7}, pages = {1519-1531.e4}, pmid = {38531360}, issn = {1879-0445}, support = {R01 EB026953/EB/NIBIB NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 HD090125/HD/NICHD NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; R01 NS120579/NS/NINDS NIH HHS/United States ; R00 MH121533/MH/NIMH NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; }, mesh = {*Motor Cortex ; *Brain-Computer Interfaces ; Learning ; Brain ; Brain Mapping ; Electroencephalography ; }, abstract = {How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.}, }
@article {pmid38531054, year = {2024}, author = {Kesarwani, M and Kincaid, Z and Azhar, M and Azam, M}, title = {Enhanced MAPK signaling induced by CSF3R mutants confers dependence to DUSP1 for leukemic transformation.}, journal = {Blood advances}, volume = {8}, number = {11}, pages = {2765-2776}, pmid = {38531054}, issn = {2473-9537}, support = {R01 CA211594/CA/NCI NIH HHS/United States ; R01 CA250516/CA/NCI NIH HHS/United States ; }, mesh = {Animals ; Humans ; Mice ; Apoptosis ; *Cell Transformation, Neoplastic/genetics/metabolism ; *Dual Specificity Phosphatase 1/metabolism/genetics ; Gene Expression Regulation, Leukemic ; Leukemia/metabolism/genetics ; *MAP Kinase Signaling System/drug effects ; Mutation ; *Receptors, Colony-Stimulating Factor/genetics/metabolism ; }, abstract = {Elevated MAPK and the JAK-STAT signaling play pivotal roles in the pathogenesis of chronic neutrophilic leukemia and atypical chronic myeloid leukemia. Although inhibitors targeting these pathways effectively suppress the diseases, they fall short in providing enduring remission, largely attributed to the cytostatic nature of these drugs. Even combinations of these drugs are ineffective in achieving sustained remission. Enhanced MAPK signaling besides promoting proliferation and survival triggers a proapoptotic response. Consequently, malignancies reliant on elevated MAPK signaling use MAPK feedback regulators to intricately modulate the signaling output, prioritizing proliferation and survival while dampening the apoptotic stimuli. Herein, we demonstrate that enhanced MAPK signaling in granulocyte colony-stimulating factor 3 receptor (CSF3R)-driven leukemia upregulates the expression of dual specificity phosphatase 1 (DUSP1) to suppress the apoptotic stimuli crucial for leukemogenesis. Consequently, genetic deletion of Dusp1 in mice conferred synthetic lethality to CSF3R-induced leukemia. Mechanistically, DUSP1 depletion in leukemic context causes activation of JNK1/2 that results in induced expression of BIM and P53 while suppressing the expression of BCL2 that selectively triggers apoptotic response in leukemic cells. Pharmacological inhibition of DUSP1 by BCI (a DUSP1 inhibitor) alone lacked antileukemic activity due to ERK1/2 rebound caused by off-target inhibition of DUSP6. Consequently, a combination of BCI with a MEK inhibitor successfully cured CSF3R-induced leukemia in a preclinical mouse model. Our findings underscore the pivotal role of DUSP1 in leukemic transformation driven by enhanced MAPK signaling and advocate for the development of a selective DUSP1 inhibitor for curative treatment outcomes.}, }
@article {pmid38530872, year = {2024}, author = {Meng, L and He, L and Chen, M and Huang, Y}, title = {The compensation effect of competence frustration and its behavioral manifestations.}, journal = {PsyCh journal}, volume = {13}, number = {4}, pages = {654-662}, pmid = {38530872}, issn = {2046-0260}, support = {2021ZGL004//Shanghai Philosophy and Social Science Planning Project/ ; 72271165//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Frustration ; Male ; Female ; Adult ; Motivation ; Resilience, Psychological ; Personal Autonomy ; }, abstract = {The frustration of competence, one of the three basic psychological needs proposed by self-determination theory, has been widely demonstrated to negatively influence one's motivation and well-being in both work and life. However, research on the recovery mechanism of competence is still in the nascent stage. In this study, a two-stage behavioral experiment was conducted to examine the restoration of competence and the potential moderating role of resilience. Results showed that individuals who were asked to recall experience of competence frustration performed better on subsequent tasks, manifesting their behavioral efforts of competence restoration. However, resilience does not play a significant moderating role in competence restoration. Through convergent behavioral evidence, findings of this study demonstrate the compensation effect of competence frustration.}, }
@article {pmid38529662, year = {2024}, author = {Lein, A and Baumgartner, WD and Riss, D and Gstöttner, W and Landegger, LD and Liu, DT and Thurner, T and Vyskocil, E and Brkic, FF}, title = {Early Results With the New Active Bone-Conduction Hearing Implant: A Systematic Review and Meta-Analysis.}, journal = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery}, volume = {170}, number = {6}, pages = {1630-1647}, doi = {10.1002/ohn.728}, pmid = {38529662}, issn = {1097-6817}, mesh = {Humans ; *Bone Conduction ; Hearing Loss, Conductive/surgery ; Hearing Aids ; Treatment Outcome ; Child ; Prosthesis Design ; }, abstract = {OBJECTIVE: The bone conduction implant (BCI) 602 is a new transcutaneous BCI with smaller dimensions. However, limited patient numbers restrict the statistical power and generalizability of the current studies. The present systematic review and meta-analysis summarize early audiological and medical outcomes of adult and pediatric patients implanted with the BCI 602 due to mixed or conductive hearing loss.<br><br>DATA SOURCE: Following the Preferred Reporting items for Systematic Reviews and Meta-analyses guidelines, 108 studies were reviewed, and 6 (5.6%) were included in the meta-analysis.<br><br>REVIEW METHOD: The data on study and patient characteristics, surgical outcomes, and audiological test results were extracted from each article. Meta-analysis employed the fixed-effect and random-effects models to analyze the mean differences (MDs) between pre- and postoperative performances.<br><br>RESULTS: In total, 116 patients were evaluated, including 64 (55%) adult and 52 (45%) pediatric patients. No intraoperative adverse events were reported, while postoperative complications were reported in 2&#xa0;(3.1%) adult and 2 (3.8%) pediatric patients. Studies consistently showed significant improvements in audiological outcomes, quality of life, and sound localization in the aided condition. In the meta-analysis, we observed a significant difference in the unaided compared to the aided condition in sound field thresholds (n&#x2009;=&#x2009;112; MD, -27.05&#x2009;dB; P&#x2009;<&#x2009;0.01), signal-to-noise ratio (n&#x2009;=&#x2009;96; MD, -6.35&#x2009;dB; P&#x2009;<&#x2009;0.01), and word recognition scores (n&#x2009;=&#x2009;96; MD, 68.89%; P&#x2009;<&#x2009;0.01).<br><br>CONCLUSION: The implantation of the BCI 602 was associated with minimal surgical complications and excellent audiological outcomes for both the pediatric and the adult cohort. Therefore, our analysis indicates a high level of safety and reliability. Further research should focus on direct comparisons with other BCIs and long-term functional outcomes.}, }
@article {pmid38529269, year = {2024}, author = {Kothe, C and Hanada, G and Mullen, S and Mullen, T}, title = {On decoding of rapid motor imagery in a diverse population using a high-density NIRS device.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1355534}, pmid = {38529269}, issn = {2673-6195}, abstract = {INTRODUCTION: Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis.<br><br>METHODS: BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study.<br><br>RESULTS: We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone.<br><br>DISCUSSION: Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.}, }
@article {pmid38527459, year = {2024}, author = {Li, G and Lan, L and He, T and Tang, Z and Liu, S and Li, Y and Huang, Z and Guan, Y and Li, X and Zhang, Y and Lai, HY}, title = {Comprehensive Assessment of Ischemic Stroke in Nonhuman Primates: Neuroimaging, Behavioral, and Serum Proteomic Analysis.}, journal = {ACS chemical neuroscience}, volume = {15}, number = {7}, pages = {1548-1559}, pmid = {38527459}, issn = {1948-7193}, mesh = {Animals ; Humans ; *Ischemic Stroke/diagnostic imaging ; Diffusion Tensor Imaging/methods ; Proteomics ; Tandem Mass Spectrometry ; *Stroke/diagnostic imaging ; Neuroimaging ; Primates ; Profilins ; }, abstract = {Ischemic strokes, prevalence and impactful, underscore the necessity of advanced research models closely resembling human physiology. Our study utilizes nonhuman primates (NHPs) to provide a detailed exploration of ischemic stroke, integrating neuroimaging data, behavioral outcomes, and serum proteomics to elucidate the complex interplay of factors involved in stroke pathophysiology. We observed a consistent pattern in infarct volume, peaking at 1-month postmiddle cerebral artery occlusion (MCAO) and then stabilized. This pattern was strongly correlated to notable changes in motor function and working memory performance. Using diffusion tensor imaging (DTI), we detected significant alterations in fractional anisotropy (FA) and mean diffusivity (MD) values, signaling microstructural changes in the brain. These alterations closely correlated with the neurological and cognitive deficits that we observed, highlighting the sensitivity of DTI metrics in stroke assessment. Behaviorally, the monkeys exhibited a reliance on their unaffected limb for compensatory movements, a common response to stroke impairment. This adaptation, along with consistent DTI findings, suggests a significant impact of stroke on motor function and spatial perception. Proteomic analysis through MS/MS functional enrichment identified two distinct groups of proteins with significant changes post-MCAO. Notably, MMP9, THBS1, MB, PFN1, and YWHAZ were identified as potential biomarkers and therapeutic targets for ischemic stroke. Our results underscore the complex nature of stroke and advocate for an integrated approach, combining neuroimaging, behavioral studies, and proteomics, for advancing our understanding and treatment of this condition.}, }
@article {pmid38526885, year = {2024}, author = {Qin, Y and Zhang, W and Tao, X}, title = {TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1466-1476}, doi = {10.1109/TNSRE.2024.3380595}, pmid = {38526885}, issn = {1558-0210}, mesh = {Humans ; *Brain ; Algorithms ; Wavelet Analysis ; Electroencephalography ; *Brain-Computer Interfaces ; Imagination ; }, abstract = {The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.}, }
@article {pmid38523306, year = {2024}, author = {Zolfaghari, S and Yousefi Rezaii, T and Meshgini, S}, title = {Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.}, journal = {Clinical EEG and neuroscience}, volume = {55}, number = {4}, pages = {486-495}, doi = {10.1177/15500594241234836}, pmid = {38523306}, issn = {2169-5202}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Movement/physiology ; Adult ; Male ; Brain/physiology ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; Algorithms ; Wrist/physiology ; }, abstract = {Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.}, }
@article {pmid38523252, year = {2024}, author = {Deng, L and Wei, W and Qiao, C and Yin, Y and Li, X and Yu, H and Jian, L and Ma, X and Zhao, L and Wang, Q and Deng, W and Guo, W and Li, T}, title = {Dynamic aberrances of substantia nigra-relevant coactivation patterns in first-episode treatment-naïve patients with schizophrenia.}, journal = {Psychological medicine}, volume = {54}, number = {10}, pages = {2527-2537}, doi = {10.1017/S0033291724000655}, pmid = {38523252}, issn = {1469-8978}, mesh = {Humans ; *Schizophrenia/physiopathology/diagnostic imaging ; *Substantia Nigra/physiopathology/diagnostic imaging ; Female ; Male ; *Magnetic Resonance Imaging ; *Connectome ; Adult ; Young Adult ; Case-Control Studies ; }, abstract = {BACKGROUND: Although dopaminergic disturbances are well-known in schizophrenia, the understanding of dopamine-related brain dynamics remains limited. This study investigates the dynamic coactivation patterns (CAPs) associated with the substantia nigra (SN), a key dopaminergic nucleus, in first-episode treatment-naïve patients with schizophrenia (FES).<br><br>METHODS: Resting-state fMRI data were collected from 84 FES and 94 healthy controls (HCs). Frame-wise clustering was implemented to generate CAPs related to SN activation or deactivation. Connectome features of each CAP were derived using an edge-centric method. The occurrence for each CAP and the balance ratio for antagonistic CAPs were calculated and compared between two groups, and correlations between temporal dynamic metrics and symptom burdens were explored.<br><br>RESULTS: Functional reconfigurations in CAPs exhibited significant differences between the activation and deactivation states of SN. During SN activation, FES more frequently recruited a CAP characterized by activated default network, language network, control network, and the caudate, compared to HCs (F = 8.54, FDR-p = 0.030). Moreover, FES displayed a tilted balance towards a CAP featuring SN-coactivation with the control network, caudate, and thalamus, as opposed to its antagonistic CAP (F = 7.48, FDR-p = 0.030). During SN deactivation, FES exhibited increased recruitment of a CAP with activated visual and dorsal attention networks but decreased recruitment of its opposing CAP (F = 6.58, FDR-p = 0.034).<br><br>CONCLUSION: Our results suggest that neuroregulatory dysfunction in dopaminergic pathways involving SN potentially mediates aberrant time-varying functional reorganizations in schizophrenia. This finding enriches the dopamine hypothesis of schizophrenia from the perspective of brain dynamics.}, }
@article {pmid38520132, year = {2024}, author = {Jeong, HJ and Lee, H and Choo, MS and Cho, SY and Jeong, SJ and Oh, SJ}, title = {Effect of detrusor underactivity on surgical outcomes of holmium laser enucleation of the prostate.}, journal = {BJU international}, volume = {133}, number = {6}, pages = {770-777}, doi = {10.1111/bju.16346}, pmid = {38520132}, issn = {1464-410X}, mesh = {Humans ; Male ; Aged ; *Prostatic Hyperplasia/surgery/complications ; *Lasers, Solid-State/therapeutic use ; Treatment Outcome ; *Urinary Bladder, Underactive/surgery/physiopathology ; Middle Aged ; Prospective Studies ; *Prostatectomy/methods/adverse effects ; Laser Therapy/methods ; Patient Satisfaction ; }, abstract = {OBJECTIVE: To evaluate the effect of detrusor underactivity (DUA) on the postoperative outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic hyperplasia (BPH).<br><br>PATIENTS AND METHODS: Patients with BPH who underwent HoLEP between January 2018 and December 2022 were enrolled in this prospective database study. Patients were divided into DUA (bladder contractility index [BCI] <100) and non-DUA (BCI &#x2265;100) groups. Objective (maximum urinary flow rate [Qmax], post-void residual urine volume [PVR]) and subjective outcomes (International Prostate Symptom Score [IPSS], Overactive Bladder Symptom Score [OABSS], satisfaction with treatment question [STQ], overall response assessment [ORA], and willingness to undergo surgery question [WUSQ]) were compared between the two groups before surgery, and at 3 and 6&#x2009;months after HoLEP.<br><br>RESULTS: A total of 689 patients, with a mean (standard deviation [SD]) age of 69.8&#x2009;(7.1)&#x2009;years, were enrolled. The mean (SD) BCI in the non-DUA (325 [47.2%]) and DUA (364 [52.8%]) groups was 123.4&#x2009;(21.4) and 78.6&#x2009;(14.2), respectively. Both objective (Qmax and PVR) and subjective (IPSS, IPSS-quality of life, and OABSS) outcomes after surgery significantly improved in both groups. The Qmax was lower in the DUA than in the non-DUA group postoperatively. At 6&#x2009;months postoperatively, the total IPSS was higher in the DUA than in the non-DUA group. There were no significant differences in surgical complications between the two groups. Responses to the STQ, ORA, and WUSQ at 6&#x2009;months postoperatively demonstrated that the patients were satisfied with the surgery (90.5% in the DUA group; 95.2% in the non-DUA group), their symptoms improved with surgery (95.9% in the DUA group; 100.0% in the non-DUA group), and they were willing to undergo surgery again (95.9% in the DUA group; 97.9% in the non-DUA group). There were no significant differences in the responses to the STQ and WUSQ between the two groups.<br><br>CONCLUSION: Our midterm results demonstrated that patients with BPH and DUA showed minimal differences in clinical outcomes after HoLEP compared to those without DUA. The overall satisfaction was high in the DUA group.}, }
@article {pmid38520009, year = {2024}, author = {Zou, P and Liu, C and Zhang, Y and Wei, C and Liu, X and Xu, S and Ling, Q and Chen, Z and Du, G and Yuan, X}, title = {Transurethral surgical treatment for benign prostatic hyperplasia with detrusor underactivity: a systematic review and meta-analysis.}, journal = {Systematic reviews}, volume = {13}, number = {1}, pages = {93}, pmid = {38520009}, issn = {2046-4053}, mesh = {Humans ; *Prostatic Hyperplasia/surgery/complications ; Male ; *Transurethral Resection of Prostate/methods ; *Urinary Bladder, Underactive/surgery ; *Quality of Life ; Treatment Outcome ; Urodynamics ; }, abstract = {BACKGROUND: The efficacy of surgical treatment for benign prostatic hyperplasia (BPH) patients with detrusor underactivity (DU) remains controversial.<br><br>METHODS: To summarize relevant evidence, three databases (PubMed, Embase, and Web of Science) were searched from database inception to May 1, 2023. Transurethral surgical treatment modalities include transurethral prostatectomy (TURP), photoselective vaporization of the prostate (PVP), and transurethral incision of the prostate (TUIP). The efficacy of the transurethral surgical treatment was assessed according to maximal flow rate on uroflowmetry (Qmax), International Prostate Symptom Score (IPSS), postvoid residual (PVR), quality of life (QoL), voided volume, bladder contractility index (BCI) and maximal detrusor pressure at maximal flow rate (PdetQmax). Pooled mean differences (MDs) were used as summary statistics for comparison. The quality of enrolled studies was evaluated by using the Newcastle-Ottawa Scale. Sensitivity analysis and funnel plots were applied to assess possible biases.<br><br>RESULTS: In this study, 10 studies with a total of 1142 patients enrolled. In BPH patients with DU, within half a year, significant improvements in Qmax (pooled MD, 4.79; 95% CI, 2.43-7.16; P&#x2009;<&#x2009;0.05), IPSS(pooled MD,&#x2009;-&#x2009;14.29; 95%CI,&#x2009;-&#x2009;16.67-11.90; P&#x2009;<&#x2009;0.05), QoL (pooled MD,&#x2009;-&#x2009;1.57; 95% CI,&#x2009;-&#x2009;2.37-0.78; P&#x2009;<&#x2009;0.05), voided volume (pooled MD, 62.19; 95% CI, 17.91-106.48; P&#x2009;<&#x2009;0.05), BCI (pooled MD, 23.59; 95% CI, 8.15-39.04; P&#x2009;<&#x2009;0.05), and PdetQmax (pooled MD, 28.62; 95% CI, 6.72-50.52; P&#x2009;<&#x2009;0.05) were observed after surgery. In addition, after more than 1&#xa0;year, significant improvements were observed in Qmax (pooled MD, 6.75; 95%CI, 4.35-9.15; P&#x2009;<&#x2009;0.05), IPSS(pooled MD,&#x2009;-&#x2009;13.76; 95%CI,&#x2009;-&#x2009;15.17-12.35; P&#x2009;<&#x2009;0.05), PVR (pooled MD,&#x2009;-&#x2009;179.78; 95%CI,&#x2009;-&#x2009;185.12-174.44; P&#x2009;<&#x2009;0.05), QoL (pooled MD,&#x2009;-&#x2009;2.61; 95%CI,&#x2009;-&#x2009;3.12-2.09; P&#x2009;<&#x2009;0.05), and PdetQmax (pooled MD, 27.94; 95%CI, 11.70-44.19; P&#x2009;<&#x2009;0.05). Compared with DU patients who did not receive surgery, DU patients who received surgery showed better improvement in PVR (pooled MD, 137.00; 95%CI, 6.90-267.10; P&#x2009;<&#x2009;0.05) and PdetQmax (pooled MD,&#x2009;-&#x2009;8.00; 95%CI,&#x2009;-&#x2009;14.68-1.32; P&#x2009;<&#x2009;0.05).<br><br>CONCLUSIONS: Our meta-analysis results showed that transurethral surgery can improve the symptoms of BPH patients with DU. Surgery also showed advantages over pharmacological treatment for BPH patients with DU.<br><br>PROSPERO CRD42023415188.}, }
@article {pmid38518778, year = {2024}, author = {Li, XY and Zhang, SY and Hong, YZ and Chen, ZG and Long, Y and Yuan, DH and Zhao, JJ and Tang, SS and Wang, H and Hong, H}, title = {TGR5-mediated lateral hypothalamus-dCA3-dorsolateral septum circuit regulates depressive-like behavior in male mice.}, journal = {Neuron}, volume = {112}, number = {11}, pages = {1795-1814.e10}, doi = {10.1016/j.neuron.2024.02.019}, pmid = {38518778}, issn = {1097-4199}, mesh = {Animals ; Male ; Mice ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; *Depression/metabolism ; Disease Models, Animal ; *GABAergic Neurons/metabolism/physiology ; *Hypothalamic Area, Lateral/metabolism ; Mice, Inbred C57BL ; Neural Pathways/metabolism ; *Receptors, G-Protein-Coupled/metabolism/genetics ; Septal Nuclei/metabolism ; Social Defeat ; Stress, Psychological/metabolism ; }, abstract = {Although bile acids play a notable role in depression, the pathological significance of the bile acid TGR5 membrane-type receptor in this disorder remains elusive. Using depression models of chronic social defeat stress and chronic restraint stress in male mice, we found that TGR5 in the lateral hypothalamic area (LHA) predominantly decreased in GABAergic neurons, the excitability of which increased in depressive-like mice. Upregulation of TGR5 or inhibition of GABAergic excitability in LHA markedly alleviated depressive-like behavior, whereas down-regulation of TGR5 or enhancement of GABAergic excitability facilitated stress-induced depressive-like behavior. TGR5 also bidirectionally regulated excitability of LHA GABAergic neurons via extracellular regulated protein kinases-dependent Kv4.2 channels. Notably, LHA GABAergic neurons specifically innervated dorsal CA3 (dCA3) CaMKIIα neurons for mediation of depressive-like behavior. LHA GABAergic TGR5 exerted antidepressant-like effects by disinhibiting dCA3 CaMKIIα neurons projecting to the dorsolateral septum (DLS). These findings advance our understanding of TGR5 and the LHA[GABA]→dCA3[CaMKIIα]→DLS[GABA] circuit for the development of potential therapeutic strategies in depression.}, }
@article {pmid38518426, year = {2024}, author = {Caillet, AH and Phillips, ATM and Modenese, L and Farina, D}, title = {NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo.}, journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology}, volume = {76}, number = {}, pages = {102873}, doi = {10.1016/j.jelekin.2024.102873}, pmid = {38518426}, issn = {1873-5711}, mesh = {Humans ; *Muscle, Skeletal/physiology/innervation ; *Electromyography/methods ; *Motor Neurons/physiology ; Muscle Contraction/physiology ; Action Potentials/physiology ; Computer Simulation ; Models, Neurological ; }, abstract = {The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.}, }
@article {pmid38518365, year = {2024}, author = {McNamara, IN and Wellman, SM and Li, L and Eles, JR and Savya, S and Sohal, HS and Angle, MR and Kozai, TDY}, title = {Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad36e1}, pmid = {38518365}, issn = {1741-2552}, mesh = {Rats ; Animals ; Electrodes, Implanted ; *Blood-Brain Barrier ; Microelectrodes ; *Inflammation ; }, abstract = {Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the 'bed-of-nails' effect and tissue dimpling.Approach. Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.Main results.Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.Significance.These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.}, }
@article {pmid38517720, year = {2024}, author = {Han, Y and Ke, Y and Wang, R and Wang, T and Ming, D}, title = {Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1407-1415}, doi = {10.1109/TNSRE.2024.3380635}, pmid = {38517720}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Fatigue ; Algorithms ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.}, }
@article {pmid38514730, year = {2024}, author = {Myszor, IT and Lapka, K and Hermannsson, K and Rekha, RS and Bergman, P and Gudmundsson, GH}, title = {Bile acid metabolites enhance expression of cathelicidin antimicrobial peptide in airway epithelium through activation of the TGR5-ERK1/2 pathway.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {6750}, pmid = {38514730}, issn = {2045-2322}, mesh = {Humans ; *Bile Acids and Salts/metabolism ; *Cathelicidins/metabolism ; MAP Kinase Signaling System ; Receptors, G-Protein-Coupled/metabolism ; Epithelium/metabolism ; Lithocholic Acid/pharmacology/metabolism ; }, abstract = {Signals for the maintenance of epithelial homeostasis are provided in part by commensal bacteria metabolites, that promote tissue homeostasis in the gut and remote organs as microbiota metabolites enter the bloodstream. In our study, we investigated the effects of bile acid metabolites, 3-oxolithocholic acid (3-oxoLCA), alloisolithocholic acid (AILCA) and isolithocholic acid (ILCA) produced from lithocholic acid (LCA) by microbiota, on the regulation of innate immune responses connected to the expression of host defense peptide cathelicidin in lung epithelial cells. The bile acid metabolites enhanced expression of cathelicidin at low concentrations in human bronchial epithelial cell line BCi-NS1.1 and primary bronchial/tracheal cells (HBEpC), indicating physiological relevance for modulation of innate immunity in airway epithelium by bile acid metabolites. Our study concentrated on deciphering signaling pathways regulating expression of human cathelicidin, revealing that LCA and 3-oxoLCA activate the surface G protein-coupled bile acid receptor 1 (TGR5, Takeda-G-protein-receptor-5)-extracellular signal-regulated kinase (ERK1/2) cascade, rather than the nuclear receptors, aryl hydrocarbon receptor, farnesoid X receptor and vitamin D3 receptor in bronchial epithelium. Overall, our study provides new insights into the modulation of innate immune responses by microbiota bile acid metabolites in the gut-lung axis, highlighting the differences in epithelial responses between different tissues.}, }
@article {pmid38514500, year = {2024}, author = {Chen, S and Xi, X and Wang, T and Li, H and Wang, M and Li, L and Lü, Z}, title = {Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {8}, pages = {2305-2318}, pmid = {38514500}, issn = {1741-0444}, support = {No. 61971169//National Natural Science Foundation of China/ ; No.2021C03031//Zhejiang Provincial Key Research and Development Program of China/ ; NO. LQ21H180005//Zhejiang Provincial Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; Imagination/physiology ; Motion ; }, abstract = {The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm's ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm's effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%.}, }
@article {pmid38513290, year = {2024}, author = {Zheng, L and Dong, Y and Tian, S and Pei, W and Gao, X and Wang, Y}, title = {A calibration-free c-VEP based BCI employing narrow-band random sequences.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad3679}, pmid = {38513290}, issn = {1741-2552}, mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Calibration ; Algorithms ; Photic Stimulation ; }, abstract = {Objective.Code-modulated visual evoked potential (c-VEP) based brain-computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences.Approach.For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15-25 Hz (NBRS-15) and 8-16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8-15.8 Hz.Main results.The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS-15 frequency band achieved an average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 102.1 ± 57.59 bits/min in a simulation of a 1000-target BCI system.Significance.This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR.}, }
@article {pmid38513289, year = {2024}, author = {Sadras, N and Pesaran, B and Shanechi, MM}, title = {Event detection and classification from multimodal time series with application to neural data.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, pmid = {38513289}, issn = {1741-2552}, support = {R01 MH123770/MH/NIMH NIH HHS/United States ; }, mesh = {*Algorithms ; Neurons/physiology ; Normal Distribution ; Animals ; Models, Neurological ; Action Potentials/physiology ; Computer Simulation ; Humans ; }, abstract = {The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.}, }
@article {pmid38513274, year = {2024}, author = {Srimadumathi, V and Ramasubba Reddy, M}, title = {Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {3}, pages = {}, doi = {10.1088/2057-1976/ad3647}, pmid = {38513274}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Neural Networks, Computer ; Algorithms ; }, abstract = {A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.}, }
@article {pmid38512735, year = {2024}, author = {Zhang, X and He, L and Gao, Q and Jiang, N}, title = {Performance of the Action Observation-Based Brain-Computer Interface in Stroke Patients and Gaze Metrics Analysis.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1370-1379}, doi = {10.1109/TNSRE.2024.3379995}, pmid = {38512735}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Stroke ; Eye Movements ; Electroencephalography/methods ; }, abstract = {Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were recruited. Four AO stimuli were designed, each presenting a decomposed action to complete the reach-and-grasp task. EEG data and eye movement data were collected. Eye movement data was utilized to analyze the reasons for individual differences in BCI performance. Task discriminative component analysis was utilized to perform online target detection. The results showed that the designed AO-based BCI could simultaneously induce steady state motion visual evoked potential (SSMVEP) from the occipital region and sensory motor rhythm from the sensorimotor region in stroke patients. The average online detection accuracy among the four AO stimuli reached 67% within 3 s in the non-hemineglect group, while the accuracy only reached 35% in the hemineglect group. Gaze metrics showed that the average total duration of fixations during the stimulus phase in the hemineglect group was only 1.31 s ± 0.532 s which was significantly lower than that in the non-hemineglect group. The results indicated that hemineglect patients have difficulty gazing at the AO stimulus, resulting in poor detection performance and weak desynchronization in the sensorimotor region. Furthermore, the degree of neglect is inversely proportional to the target detection accuracy in hemineglect stroke patients. In addition, the gaze metrics associated with cognitive load were significantly correlated with the accuracy in non-hemineglect patients. It indicated the cognitive load may affect the AO-based BCI. The current study will expedite the clinical application of AO-based BCI.}, }
@article {pmid38510513, year = {2024}, author = {Liu, Y and Luo, Y and Zhang, N and Zhang, X and Liu, S}, title = {A scientometric review of the growing trends in transcranial alternating current stimulation (tACS).}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1362593}, pmid = {38510513}, issn = {1662-5161}, abstract = {OBJECTIVE: The aim of the current study was to provide a comprehensive picture of tACS-related research in the last decade through a bibliometric approach in order to systematically analyze the current status and cutting-edge trends in this field.<br><br>METHODS: Articles and review articles related to tACS from 2013 to 2022 were searched on the Web of Science platform. A bibliometric analysis of authors, journals, countries, institutions, references, and keywords was performed using CiteSpace (6.2.R2), VOSviewer (1.6.19), Scimago Graphica (1.0.30), and Bibliometrix (4.2.2).<br><br>RESULTS: A total of 602 papers were included. There was an overall increase in annual relevant publications in the last decade. The most contributing author was Christoph S. Herrmann. Brain Stimulation was the most prolific journal. The most prolific countries and institutions were Germany and Harvard University, respectively.<br><br>CONCLUSION: The findings reveal the development prospects and future directions of tACS and provide valuable references for researchers in the field. In recent years, the keywords "gamma," "transcranial direct current simulation," and "Alzheimer's disease" that have erupted, as well as many references cited in the outbreak, have provided certain clues for the mining of research prefaces. This will act as a guide for future researchers in determining the path of tACS research.}, }
@article {pmid38510464, year = {2024}, author = {Tian, Z and Wu, Z and Ying, S}, title = {Editorial: Brain functional analysis and brain-like intelligence.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1383481}, pmid = {38510464}, issn = {1662-4548}, }
@article {pmid38510138, year = {2024}, author = {Huang, C and Shi, N and Miao, Y and Chen, X and Wang, Y and Gao, X}, title = {Visual tracking brain-computer interface.}, journal = {iScience}, volume = {27}, number = {4}, pages = {109376}, pmid = {38510138}, issn = {2589-0042}, abstract = {Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.}, }
@article {pmid38510045, year = {2024}, author = {Mainul, EA and Hossain, MF}, title = {A metamaterial unit-cell based patch radiator for brain-machine interface technology.}, journal = {Heliyon}, volume = {10}, number = {6}, pages = {e27775}, pmid = {38510045}, issn = {2405-8440}, abstract = {This paper presents a novel approach to the design of a brain implantable antenna tailored for brain-machine interface (BMI) technology. The design is based on a U-shaped unit-cell metamaterial (MTM), introducing innovative features to enhance performance and address specific challenges associated with BMI applications. The motivation behind the use of the unit-cell structure is to elongate the electric path within the antenna patch, diverging from a reliance on the electrical properties of the MTM. Consequently, the unit cell is connected to an inset-fed transmission line and shorted to the ground. This configuration serves the dual purpose of reducing the size of the antenna and enabling resonance at the 2.442 GHz band within a seven-layer brain phantom. The antenna is designed using a FR-4 substrate (εr = 4.3 and tan δ = 0.025) of 1.5 mm thickness, and it is coated with a biocompatible polyamide material (εr = 4.3 and tan δ = 0.004) of 0.05 mm thickness. The proposed antenna achieves a compact dimension of 20 × 20 × 1.6 mm3 (0.338 × 0.338 × 0.027 λg3) and demonstrates a high bandwidth of 974 MHz with its gain of -14.6 dBi in the 2.442 GHz band. It also exhibits a matched impedance of 49.41-j1.32 Ω in the implantable condition, corresponding to a 50 Ω source impedance. In comparison to a selection of relevant research works, the proposed antenna has a low specific absorption rate (SAR) of 218 W/kg and 68 W/kg at 1g and 10g brain tissue standards, respectively. An antenna prototype has been fabricated and measured for return loss in both free space and in-vivo conditions using sheep's brain. The measurement results are found to be in close agreement with the simulation results for both conditions, showing the practical applicability of the proposed antenna for BMI applications.}, }
@article {pmid38509590, year = {2024}, author = {Liu, XY and Mu, JJ and Han, JG and Pang, MJ and Zhang, K and Zhai, WQ and Su, N and Ni, GJ and Guo, ZG and Ming, D}, title = {Heart-brain axis: low blood pressure during off-pump CABG surgery is associated with postoperative heart failure.}, journal = {Military Medical Research}, volume = {11}, number = {1}, pages = {18}, pmid = {38509590}, issn = {2054-9369}, support = {2021YFF1200602//Key Technologies Research and Development Program/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; c02022088//National Defense Science and Technology Innovation Fund of the Chinese Academy of Sciences/ ; 20JCZDJC00810//Tianjin science and technology program/ ; }, mesh = {Humans ; Coronary Artery Bypass/adverse effects ; Treatment Outcome ; Brain ; *Hypotension ; *Heart Failure/complications ; }, }
@article {pmid38509350, year = {2024}, author = {Liu, X and Hu, B and Si, Y and Wang, Q}, title = {The role of eye movement signals in non-invasive brain-computer interface typing system.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {7}, pages = {1981-1990}, pmid = {38509350}, issn = {1741-0444}, mesh = {*Brain-Computer Interfaces ; Humans ; *Eye Movements/physiology ; Electroencephalography/methods ; Electrooculography ; Signal Processing, Computer-Assisted ; }, abstract = {Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.}, }
@article {pmid38508294, year = {2024}, author = {Yang, H and Wu, H and Kong, L and Luo, W and Xie, Q and Pan, J and Quan, W and Hu, L and Li, D and Wu, X and Liang, H and Qin, P}, title = {Precise detection of awareness in disorders of consciousness using deep learning framework.}, journal = {NeuroImage}, volume = {290}, number = {}, pages = {120580}, doi = {10.1016/j.neuroimage.2024.120580}, pmid = {38508294}, issn = {1095-9572}, mesh = {Humans ; *Consciousness Disorders ; *Deep Learning ; Brain/diagnostic imaging ; Persistent Vegetative State ; Unconsciousness ; Consciousness ; }, abstract = {Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.}, }
@article {pmid38505100, year = {2024}, author = {Reichert, C and Sweeney-Reed, CM and Hinrichs, H and Dürschmid, S}, title = {A toolbox for decoding BCI commands based on event-related potentials.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1358809}, pmid = {38505100}, issn = {1662-5161}, abstract = {Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.}, }
@article {pmid38505099, year = {2024}, author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y}, title = {EEG-based finger movement classification with intrinsic time-scale decomposition.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1362135}, pmid = {38505099}, issn = {1662-5161}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.<br><br>METHODS: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.<br><br>RESULTS: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.<br><br>DISCUSSION: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).}, }
@article {pmid38502615, year = {2024}, author = {Peng, B and Bi, L and Wang, Z and Feleke, AG and Fei, W}, title = {Robust Decoding of Upper-Limb Movement Direction Under Cognitive Distraction With Invariant Patterns in Embedding Manifold.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1344-1354}, doi = {10.1109/TNSRE.2024.3379451}, pmid = {38502615}, issn = {1558-0210}, mesh = {Humans ; *Upper Extremity ; Movement ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Cognition ; }, abstract = {Motor brain-computer interfaces (BCIs) have gained growing research interest in motor rehabilitation, restoration, and prostheses control. Decoding upper-limb movement direction with noninvasive BCIs has been extensively investigated. However, few of them address the intervention of cognitive distraction that impairs decoding performance in practice. In this study, we propose a novel decoding model with invariant patterns in embedding manifold on a mixed dataset pooled from electroencephalograph (EEG) signals under different attentional states. We reconstruct an embedding low-dimensional manifold that intrinsically characterizes movements of the upper limb and transfer patterns of neural activities decomposed from brain functional connectivity (FC) to the manifold subspace to further preserve movement-related information. Experimental results showed that the proposed decoding model had higher robustness on the mixed dataset of attentive and distracted states compared to the baseline method. Our research provides insights into modeling a uniform underlying mechanism of movement-related EEG signals and can help enhance the practicability of BCI systems under real-world situations.}, }
@article {pmid38502151, year = {2024}, author = {Zou, Q and Duan, H and Fang, S and Sheng, W and Li, X and Stoika, R and Finiuk, N and Panchuk, R and Liu, K and Wang, L}, title = {Fabrication of yeast β-glucan/sodium alginate/γ-polyglutamic acid composite particles for hemostasis and wound healing.}, journal = {Biomaterials science}, volume = {12}, number = {9}, pages = {2394-2407}, doi = {10.1039/d3bm02068a}, pmid = {38502151}, issn = {2047-4849}, mesh = {Animals ; *Wound Healing/drug effects ; *Alginates/chemistry/pharmacology ; *Polyglutamic Acid/chemistry/pharmacology/*analogs &amp; derivatives ; *beta-Glucans/chemistry/pharmacology ; Mice ; *Hemostasis/drug effects ; *Saccharomyces cerevisiae ; Cell Line ; Hemostatics/pharmacology/chemistry/administration &amp; dosage ; Biocompatible Materials/chemistry/pharmacology ; Male ; }, abstract = {Particles with a porous structure can lead to quick hemostasis and provide a good matrix for cell proliferation during wound healing. Recently, many particle-based wound healing materials have been clinically applied. However, these products show good hemostatic ability but with poor wound healing ability. To solve this problem, this study fabricated APGG composite particles using yeast β-glucan (obtained from Saccharomyces cerevisiae), sodium alginate, and γ-polyglutamic acid as the starting materials. The structure of yeast β-glucan was modified with many carboxymethyl groups to obtain carboxymethylated β-glucan, which could coordinate with Ca[2+] ions to form a crosslinked structure. A morphology study indicated that the APGG particles showed an irregular spheroidal structure with a low density (<0.1 g cm[-3]) and high porosity (>40%). An in vitro study revealed that the particles exhibited a low BCI value, low hemolysis ratio, and good cytocompatibility against L929 cells. The APGG particles could quickly stop bleeding in a mouse liver injury model and exhibited better hemostatic ability than the commercially available product Celox. Furthermore, the APGG particles could accelerate the healing of non-infected wounds, and the expression levels of CD31, α-SMA, and VEGF related to angiogenesis were significantly enhanced.}, }
@article {pmid38500488, year = {2024}, author = {Sieghartsleitner, S and Sebastián-Romagosa, M and Cho, W and Grünwald, J and Ortner, R and Scharinger, J and Kamada, K and Guger, C}, title = {Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1346607}, pmid = {38500488}, issn = {1662-4548}, abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.<br><br>METHODS: Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.<br><br>RESULTS: Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.<br><br>DISCUSSION: The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.}, }
@article {pmid38499913, year = {2024}, author = {Shiina, T and Yunoki, T and Tachino, H and Hayashi, A}, title = {Comparative study of surgical outcomes regarding tear meniscus area and high-order aberrations between two different interventional methods for primary acquired nasolacrimal duct obstruction.}, journal = {Japanese journal of ophthalmology}, volume = {68}, number = {2}, pages = {139-145}, pmid = {38499913}, issn = {1613-2246}, mesh = {Male ; Humans ; Female ; Middle Aged ; Aged ; Aged, 80 and over ; *Lacrimal Duct Obstruction/diagnosis/therapy ; *Nasolacrimal Duct/surgery ; Retrospective Studies ; *Dacryocystorhinostomy/methods ; Treatment Outcome ; *Meniscus ; }, abstract = {PURPOSE: To compare endonasal dacryocystorhinostomy (EN-DCR) with sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) by evaluating tear meniscus area (TMA) and total high-order aberrations (HOAs) for primary acquired nasolacrimal duct obstruction (PANDO).<br><br>METHOD: We retrospectively reviewed 56 eyes of 42 patients (7 men, 35 women; age, 72.7&#xb1;13.1 years) who underwent EN-DCR or SG-BCI for PANDO in Toyama University Hospital from February 2020 to June 2022. In the EN-DCR and SG-BCI groups, we measured the patency of the lacrimal passage, preoperative and postoperative TMA, and HOAs of the central 4 mm of the cornea using optical coherence tomography (AS-OCT), six months postoperatively.<br><br>RESULTS: There was a positive correlation between preoperative TMA and preoperative HOAs in all cases. Postoperative patency of lacrimal passage was 100% in the EN-DCR and 80.8% in the SG-BCI group. There was a significant difference in the number of passages between the two groups (p = 0.01). Preoperative TMA and HOAs showed a significant postoperative decrease in both groups (EN-DCR group: p<0.01, p<0.01, SG-BCI group: p<0.01, p=0.03, respectively). We then calculated the rate of change of preoperative and postoperative TMA and HOAs and compared them between the two groups. The rate of change was significantly higher in the EN-DCR group than that in the SG-BCI group (TMA, p=0.03; HOAs, p=0.02).<br><br>CONCLUSION: Although both EN-DCR and SG-BCI are effective for PANDO, our results suggest that EN-DCR is more effective in improving TMA and HOAs.}, }
@article {pmid38499709, year = {2024}, author = {Schreiner, L and Jordan, M and Sieghartsleitner, S and Kapeller, C and Pretl, H and Kamada, K and Asman, P and Ince, NF and Miller, KJ and Guger, C}, title = {Mapping of the central sulcus using non-invasive ultra-high-density brain recordings.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {6527}, pmid = {38499709}, issn = {2045-2322}, support = {RHUMBO-H2020-MSCAITN-2018-813234//European Commission project/ ; }, mesh = {*Brain/diagnostic imaging/physiology ; *Brain Mapping/methods ; Head ; Electroencephalography/methods ; Electrodes, Implanted ; Electrodes ; }, abstract = {Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.}, }
@article {pmid38498746, year = {2024}, author = {He, Y and Ven, SV and Liaw, HP and Shi, C and Russo, P and Gourdouparis, M and Konijnenburg, M and Traferro, S and Timmermans, M and Lopez, CM and Harpe, P and Cantatore, E and Chicca, E and Liu, YH}, title = {An Event-Based Neural Compressive Telemetry With &gt;11× Loss-Less Data Reduction for High-Bandwidth Intracortical Brain Computer Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {18}, number = {5}, pages = {1100-1111}, pmid = {38498746}, issn = {1940-9990}, support = {101001448/ERC_/European Research Council/International ; }, mesh = {*Brain-Computer Interfaces ; *Telemetry/instrumentation/methods ; Signal Processing, Computer-Assisted/instrumentation ; Humans ; Animals ; Data Compression/methods ; Electroencephalography/methods/instrumentation ; Algorithms ; }, abstract = {Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.}, }
@article {pmid38496885, year = {2024}, author = {Klomchitcharoen, S and Wechakarn, P and Tangwattanasirikun, T and Smerwong, N and Netrapathompornkij, P and Chatmeeboon, T and Nangsue, N and Thitasirivit, V and Kaweewongsunthorn, K and Piyanopharoj, S and Phumiprathet, P and Wongsawat, Y}, title = {High-altitude balloon platform for studying the biological response of living organisms exposed to near-space environments.}, journal = {Heliyon}, volume = {10}, number = {6}, pages = {e27406}, pmid = {38496885}, issn = {2405-8440}, abstract = {The intangible desire to explore the mysteries of the universe has driven numerous advancements for humanity for centuries. Extraterrestrial journeys are becoming more realistic as a result of human curiosity and endeavors. Over the years, space biology research has played a significant role in understanding the hazardous effects of the space environment on human health during long-term space travel. The inevitable consequence of a space voyage is space ionizing radiation, which has deadly aftereffects on the human body. The paramount objective of this study is to provide a robust platform for performing biological experiments within the Earth's stratosphere by utilizing high-altitude balloons. This platform allows the use of a biological payload to simulate spaceflight missions within the unique properties of space that cannot be replicated in terrestrial facilities. This paper describes the feasibility and demonstration of a biological balloon mission suitable for students and scientists to perform space biology experiments within the boundary of the stratosphere. In this study, a high-altitude balloon was launched into the upper atmosphere (∼29 km altitude), where living microorganisms were exposed to a hazardous combination of UV irradiation, ultralow pressure and cold shock. The balloon carried the budding yeast Saccharomyces cerevisiae to investigate microbial survival potential under extreme conditions. The results indicated a notable reduction in biosample mortality two orders of magnitude (2-log) after exposure to 164.9 kJ m[-2] UV. Postflight experiments have shown strong evidence that the effect of UV irradiation on living organisms is stronger than that of other extreme conditions.}, }
@article {pmid38496552, year = {2024}, author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR}, title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.02.29.582733}, pmid = {38496552}, issn = {2692-8205}, support = {I01 RX003803/RX/RRD VA/United States ; I01 RX002827/RX/RRD VA/United States ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.}, }
@article {pmid38496527, year = {2024}, author = {Capadona, J and Hoeferlin, G and Grabinski, S and Druschel, L and Duncan, J and Burkhart, G and Weagraff, G and Lee, A and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Memberg, W and Sweet, J and Hamedani, HA and Acharya, A and Hernandez-Reynoso, A and Donskey, C and Jaskiw, G and Chan, R and Ajiboye, A and von Recum, H and Zhang, L}, title = {Bacteria Invade the Brain Following Sterile Intracortical Microelectrode Implantation.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {38496527}, issn = {2693-5015}, support = {IK6 RX003077/RX/RRD VA/United States ; R01 NS131502/NS/NINDS NIH HHS/United States ; R25 CA221718/CA/NCI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; }, abstract = {Brain-machine interface performance is largely affected by the neuroinflammatory responses resulting in large part from blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings strongly suggest that certain gut bacterial constituents penetrate the BBB and are resident in various brain regions of rodents and humans, both in health and disease. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could amplify dysregulation of the microbiome-gut-brain axis. Here, we report that bacteria, including those commonly found in the gut, enter the brain following intracortical microelectrode implantation in mice implanted with single-shank silicon microelectrodes. Systemic antibiotic treatment of mice implanted with microelectrodes to suppress bacteria resulted in differential expression of bacteria in the brain tissue and a reduced acute inflammatory response compared to untreated controls, correlating with temporary improvements in microelectrode recording performance. Long-term antibiotic treatment resulted in worsening microelectrode recording performance and dysregulation of neurodegenerative pathways. Fecal microbiome composition was similar between implanted mice and an implanted human, suggesting translational findings. However, a significant portion of invading bacteria was not resident in the brain or gut. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.}, }
@article {pmid38496403, year = {2024}, author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and Lam, JL and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA}, title = {Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.03.01.583000}, pmid = {38496403}, issn = {2692-8205}, abstract = {UNLABELLED: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.<br><br>TEASER: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.}, }
@article {pmid38492454, year = {2024}, author = {Borra, D and Filippini, M and Ursino, M and Fattori, P and Magosso, E}, title = {Convolutional neural networks reveal properties of reach-to-grasp encoding in posterior parietal cortex.}, journal = {Computers in biology and medicine}, volume = {172}, number = {}, pages = {108188}, doi = {10.1016/j.compbiomed.2024.108188}, pmid = {38492454}, issn = {1879-0534}, mesh = {*Artificial Intelligence ; Reproducibility of Results ; *Psychomotor Performance/physiology ; Parietal Lobe/physiology ; Neural Networks, Computer ; Hand Strength/physiology ; Movement/physiology ; }, abstract = {Deep neural networks (DNNs) are widely adopted to decode motor states from both non-invasively and invasively recorded neural signals, e.g., for realizing brain-computer interfaces. However, the neurophysiological interpretation of how DNNs make the decision based on the input neural activity is limitedly addressed, especially when applied to invasively recorded data. This reduces decoder reliability and transparency, and prevents the exploitation of decoders to better comprehend motor neural encoding. Here, we adopted an explainable artificial intelligence approach - based on a convolutional neural network and an explanation technique - to reveal spatial and temporal neural properties of reach-to-grasping from single-neuron recordings of the posterior parietal area V6A. The network was able to accurately decode 5 different grip types, and the explanation technique automatically identified the cells and temporal samples that most influenced the network prediction. Grip encoding in V6A neurons already started at movement preparation, peaking during movement execution. A difference was found within V6A: dorsal V6A neurons progressively encoded more for increasingly advanced grips, while ventral V6A neurons for increasingly rudimentary grips, with both subareas following a linear trend between the amount of grip encoding and the level of grip skills. By revealing the elements of the neural activity most relevant for each grip with no a priori assumptions, our approach supports and advances current knowledge about reach-to-grasp encoding in V6A, and it may represent a general tool able to investigate neural correlates of motor or cognitive tasks (e.g., attention and memory tasks) from single-neuron recordings.}, }
@article {pmid38491170, year = {2024}, author = {Fan, L and Liu, J and Hu, W and Chen, Z and Lan, J and Zhang, T and Zhang, Y and Wu, X and Zhong, Z and Zhang, D and Zhang, J and Qin, R and Chen, H and Zong, Y and Zhang, J and Chen, B and Jiang, J and Cheng, J and Zhou, J and Gao, Z and Liu, Z and Chai, Y and Fan, J and Wu, P and Chen, Y and Zhu, Y and Wang, K and Yuan, Y and Huang, P and Zhang, Y and Feng, H and Song, K and Zeng, X and Zhu, W and Hu, X and Yin, W and Chen, W and Wang, J}, title = {Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.}, journal = {Cell research}, volume = {34}, number = {6}, pages = {407-427}, pmid = {38491170}, issn = {1748-7838}, support = {2017ZX10203205//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, mesh = {Humans ; *Atherosclerosis/immunology/drug therapy/pathology/therapy ; *T-Lymphocytes/immunology/metabolism ; *Programmed Cell Death 1 Receptor/metabolism/antagonists &amp; inhibitors ; Inflammation/pathology ; Antibodies, Monoclonal/therapeutic use/pharmacology ; Female ; Male ; Retrospective Studies ; Receptors, IgG/metabolism ; Plaque, Atherosclerotic/pathology/immunology/therapy/drug therapy ; Middle Aged ; }, abstract = {Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1[+] T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.}, }
@article {pmid38487836, year = {2024}, author = {Li, S and Xu, C and Hu, S and Lai, J}, title = {Efficacy and tolerability of FDA-approved atypical antipsychotics for the treatment of bipolar depression: a systematic review and network meta-analysis.}, journal = {European psychiatry : the journal of the Association of European Psychiatrists}, volume = {67}, number = {1}, pages = {e29}, pmid = {38487836}, issn = {1778-3585}, mesh = {Humans ; *Bipolar Disorder/drug therapy ; *Antipsychotic Agents/therapeutic use/adverse effects ; *Network Meta-Analysis ; United States ; United States Food and Drug Administration ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Quetiapine Fumarate/therapeutic use/adverse effects ; Olanzapine/therapeutic use/adverse effects ; }, abstract = {We employed a Bayesian network meta-analysis for comparison of the efficacy and tolerability of US Food and Drug Administration (FDA)-approved atypical antipsychotics (AAPs) for the treatment of bipolar patients with depressive episodes. Sixteen randomized controlled trials with 7234 patients treated by one of the five AAPs (cariprazine, lumateperone, lurasidone, olanzapine, and quetiapine) were included. For the response rate (defined as an improvement of ≥50% from baseline on the Montgomery-Åsberg Depression Rating Scale [MADRS]), all AAPs were more efficacious than placebo. For the remission rate (defined as the endpoint of MADRS ≤12 or ≤ 10), cariprazine, lurasidone, olanzapine, and quetiapine had higher remission rates than placebo. In terms of tolerability, olanzapine was unexpectedly associated with lower odds of all-cause discontinuation in comparison with placebo, whereas quetiapine was associated with higher odds of discontinuation due to adverse events than placebo. Compared with placebo, lumateperone, olanzapine, and quetiapine showed higher odds of somnolence. Lumateperone had a lower rate of ≥ weight gain of 7% than placebo and other treatments. Olanzapine was associated with a significant increase from baseline in total cholesterol and triglycerides than placebo. These findings inform individualized prescriptions of AAPs for treating bipolar depression in clinical practice.}, }
@article {pmid38487198, year = {2023}, author = {Zhang, J and Li, J and Huang, Z and Huang, D and Yu, H and Li, Z}, title = {Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.}, journal = {Health data science}, volume = {3}, number = {}, pages = {0096}, pmid = {38487198}, issn = {2765-8783}, abstract = {Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.}, }
@article {pmid38486966, year = {2024}, author = {Wu, H and Cai, C and Ming, W and Chen, W and Zhu, Z and Feng, C and Jiang, H and Zheng, Z and Sawan, M and Wang, T and Zhu, J}, title = {Speech decoding using cortical and subcortical electrophysiological signals.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1345308}, pmid = {38486966}, issn = {1662-4548}, abstract = {INTRODUCTION: Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces.<br><br>METHODS: In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150&#x2009;Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable.<br><br>RESULTS: Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone.<br><br>DISCUSSION: This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.}, }
@article {pmid38486923, year = {2024}, author = {Juan, JV and Martínez, R and Iáñez, E and Ortiz, M and Tornero, J and Azorín, JM}, title = {Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.}, journal = {Frontiers in neuroinformatics}, volume = {18}, number = {}, pages = {1345425}, pmid = {38486923}, issn = {1662-5196}, abstract = {INTRODUCTION: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.<br><br>METHODS: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.<br><br>RESULTS AND DISCUSSION: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.}, }
@article {pmid38485742, year = {2024}, author = {Gao, Y and Liu, T and Hong, T and Fang, Y and Jiang, W and Zhang, X}, title = {Subwavelength dielectric waveguide for efficient travelling-wave magnetic resonance imaging.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {2298}, pmid = {38485742}, issn = {2041-1723}, abstract = {Magnetic resonance imaging (MRI) has diverse applications in physics, biology, and medicine. Uniform excitation of nuclei spins through circular-polarized transverse magnetic component of electromagnetic field is vital for obtaining unbiased tissue contrasts. However, achieving this in the electrically large human body poses a significant challenge, especially at ultra-high fields (UHF) with increased working frequencies (≥297 MHz). Canonical volume resonators struggle to meet this challenge, while radiative excitation methods like travelling-wave (TW) show promise but often suffer from inadequate excitation efficiency. Here, we introduce a new technique using a subwavelength dielectric waveguide insert that enhances both efficiency and homogeneity at 7 T. Through TE11-to-TM11 mode conversion, power focusing, wave impedance matching, and phase velocity matching, we achieved a 114% improvement in TW efficiency and mitigated the center-brightening effect. This fundamental advancement in TW MRI through effective wave manipulation could promote the electromagnetic design of UHF MRI systems.}, }
@article {pmid38485630, year = {2024}, author = {Marchi, A and Guex, R and Denis, M and El Youssef, N and Pizzo, F and Bénar, CG and Bartolomei, F}, title = {Neurofeedback and epilepsy: Renaissance of an old self-regulation method?.}, journal = {Revue neurologique}, volume = {180}, number = {4}, pages = {314-325}, doi = {10.1016/j.neurol.2024.02.386}, pmid = {38485630}, issn = {0035-3787}, mesh = {Humans ; *Neurofeedback/methods ; *Epilepsy/therapy/psychology ; *Brain-Computer Interfaces/trends ; Neuronal Plasticity/physiology ; Self-Control ; Brain/physiology/physiopathology ; }, abstract = {Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.}, }
@article {pmid38481577, year = {2024}, author = {Cai, XL and Ye, Q and Ni, K and Zhu, L and Zhang, Q and Yin, M and Zhang, Z and Wei, W and Preece, DA and Li, BM}, title = {Chinese version of the Perth Alexithymia Questionnaire: psychometric properties and clinical applications.}, journal = {General psychiatry}, volume = {37}, number = {2}, pages = {e101281}, pmid = {38481577}, issn = {2517-729X}, abstract = {BACKGROUND: The alexithymia trait is of high clinical interest. The Perth Alexithymia Questionnaire (PAQ) was recently developed to enable detailed facet-level and valence-specific assessments of alexithymia.<br><br>AIMS: In this paper, we introduce the first Chinese version of the PAQ and examine its psychometric properties and clinical applications.<br><br>METHODS: In Study 1, the PAQ was administered to 990 Chinese participants. We examined its factor structure, internal consistency, test-retest reliability, as well as convergent, concurrent and discriminant validity. In Study 2, four groups, including a major depressive disorder (MDD) group (n=50), a matched healthy control group for MDD (n=50), a subclinical depression group (n=50) and a matched healthy control group for subclinical depression (n=50), were recruited. Group comparisons were conducted to assess the clinical relevance of the PAQ.<br><br>RESULTS: In Study 1, the intended five-factor structure of the PAQ was found to fit the data well. The PAQ showed good internal consistency and test-retest reliability, as well as good convergent, concurrent and discriminant validity. In Study 2, the PAQ was able to successfully distinguish the MDD group and the subclinical depression group from their matched healthy controls.<br><br>CONCLUSIONS: The Chinese version of the PAQ is a valid and reliable instrument for comprehensively assessing alexithymia in the general population and adults with clinical/subclinical depression.}, }
@article {pmid38480743, year = {2024}, author = {Chen, PC and Tsai, TP and Liao, YC and Liao, YC and Cheng, HW and Weng, YH and Lin, CM and Kao, CY and Tai, CC and Ruan, JW}, title = {Intestinal dual-specificity phosphatase 6 regulates the cold-induced gut microbiota remodeling to promote white adipose browning.}, journal = {NPJ biofilms and microbiomes}, volume = {10}, number = {1}, pages = {22}, pmid = {38480743}, issn = {2055-5008}, support = {107-2320-B-006 -020 -MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2320-B-006 -051 -MY3//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2320-B-006 -051 -MY3//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; }, mesh = {Animals ; Humans ; Mice ; Adiposity ; Cold Temperature ; Dual-Specificity Phosphatases/metabolism ; *Gastrointestinal Microbiome/physiology ; Obesity ; }, abstract = {Gut microbiota rearrangement induced by cold temperature is crucial for browning in murine white adipose tissue. This study provides evidence that DUSP6, a host factor, plays a critical role in regulating cold-induced gut microbiota rearrangement. When exposed to cold, the downregulation of intestinal DUSP6 increased the capacity of gut microbiota to produce ursodeoxycholic acid (UDCA). The DUSP6-UDCA axis is essential for driving Lachnospiraceae expansion in the cold microbiota. In mice experiencing cold-room temperature (CR) transitions, prolonged DUSP6 inhibition via the DUSP6 inhibitor (E/Z)-BCI maintained increased cecal UDCA levels and cold-like microbiota networks. By analyzing DUSP6-regulated microbiota dynamics in cold-exposed mice, we identified Marvinbryantia as a genus whose abundance increased in response to cold exposure. When inoculated with human-origin Marvinbryantia formatexigens, germ-free recipient mice exhibited significantly enhanced browning phenotypes in white adipose tissue. Moreover, M. formatexigens secreted the methylated amino acid Nε-methyl-L-lysine, an enriched cecal metabolite in Dusp6 knockout mice that reduces adiposity and ameliorates nonalcoholic steatohepatitis in mice. Our work revealed that host-microbiota coadaptation to cold environments is essential for regulating the browning-promoting gut microbiome.}, }
@article {pmid38479013, year = {2024}, author = {Alsuradi, H and Khattak, A and Fakhry, A and Eid, M}, title = {Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad33b3}, pmid = {38479013}, issn = {1741-2552}, mesh = {Humans ; *Imagination ; Imagery, Psychotherapy ; Fingers ; *Brain-Computer Interfaces ; Brain ; Electroencephalography/methods ; Algorithms ; }, abstract = {Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.}, }
@article {pmid38478611, year = {2024}, author = {Liu, W and Mei, T and Cao, Z and Li, C and Wu, Y and Wang, L and Xu, G and Chen, Y and Zhou, Y and Wang, S and Xue, Y and Yu, Y and Kong, XY and Chen, R and Tu, B and Xiao, K}, title = {Bioinspired carbon nanotube-based nanofluidic ionic transistor with ultrahigh switching capabilities for logic circuits.}, journal = {Science advances}, volume = {10}, number = {11}, pages = {eadj7867}, pmid = {38478611}, issn = {2375-2548}, abstract = {The voltage-gated ion channels, also known as ionic transistors, play substantial roles in biological systems and ion-ion selective separation. However, implementing the ultrafast switchable capabilities and polarity switching of ionic transistors remains a challenge. Here, we report a nanofluidic ionic transistor based on carbon nanotubes, which exhibits an on/off ratio of 10[4] at operational gate voltage as low as 1 V. By controlling the morphology of carbon nanotubes, both unipolar and ambipolar ionic transistors are realized, and their on/off ratio can be further improved by introducing an Al2O3 dielectric layer. Meanwhile, this ionic transistor enables the polarity switching between p-type and n-type by controlled surface properties of carbon nanotubes. The implementation of constructing ionic circuits based on ionic transistors is demonstrated, which enables the creation of NOT, NAND, and NOR logic gates. The ionic transistors are expected to have profound implications for low-energy consumption computing devices and brain-machine interfacing.}, }
@article {pmid38476872, year = {2024}, author = {Lv, Z and Liu, X and Dai, M and Jin, X and Huang, X and Chen, Z}, title = {Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1361486}, pmid = {38476872}, issn = {1662-4548}, abstract = {INTRODUCTION: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.<br><br>METHODS: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.<br><br>RESULTS: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.<br><br>DISCUSSION: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).}, }
@article {pmid38475214, year = {2024}, author = {Huang, J and Li, G and Zhang, Q and Yu, Q and Li, T}, title = {Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {5}, pages = {}, pmid = {38475214}, issn = {1424-8220}, support = {2021-I2M-1-042, 2022-I2M-C&amp;T-A-005, and 2022-I2M-C&amp;T-B-012//Chinese Academy of Medical Science health innovation project/ ; 20JCJQIC00230//Tianjin Outstanding Youth Fund Project/ ; }, mesh = {Humans ; Imagination ; *Brain-Computer Interfaces ; Imagery, Psychotherapy/methods ; *Stroke ; Electroencephalography/methods ; Algorithms ; }, abstract = {Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.}, }
@article {pmid38472417, year = {2024}, author = {Jiao, Y and Zheng, Q and Qiao, D and Lang, X and Xie, L and Pan, Y}, title = {EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.}, journal = {Biological cybernetics}, volume = {118}, number = {1-2}, pages = {21-37}, pmid = {38472417}, issn = {1432-0770}, support = {Grant No. KQTD20200820113106007//The Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Multivariate Analysis ; Brain/physiology ; Computer Simulation ; }, abstract = {Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.}, }
@article {pmid38471478, year = {2024}, author = {Sintas, JI and Bean, RH and Zhang, R and Long, TE}, title = {Nonisocyanate Polyurethane Segmented Copolymers from Bis-Carbonylimidazolides.}, journal = {Macromolecular rapid communications}, volume = {45}, number = {11}, pages = {e2400057}, doi = {10.1002/marc.202400057}, pmid = {38471478}, issn = {1521-3927}, support = {E3P-2132183//National Science Foundation/ ; //Eyring Materials Center and ASU Biodesign Institute/ ; }, mesh = {*Polyurethanes/chemistry/chemical synthesis ; Polymers/chemistry/chemical synthesis ; Imidazoles/chemistry ; Molecular Structure ; Polymerization ; Calorimetry, Differential Scanning ; }, abstract = {Bis-carbonylimidazolide (BCI) functionalization enables an efficient synthetic strategy to generate high molecular weight segmented nonisocyanate polyurethanes (NIPUs). Melt phase polymerization of ED-2003 Jeffamine, 4,4'-methylenebis(cyclohexylamine), and a BCI monomer that mimics a 1,4-butanediol chain extender enables polyether NIPUs that contain varying concentrations of hard segments ranging from 40 to 80 wt. %. Dynamic mechanical analysis and differential scanning calorimetry reveal thermal transitions for soft, hard, and mixed phases. Hard segment incorporations between 40 and 60 wt. % display up to three distinct phases pertaining to the poly(ethylene glycol) (PEG) soft segment Tg, melting transition, and hard segment Tg, while higher hard segment concentrations prohibit soft segment crystallization, presumably due to restricted molecular mobility from the hard segment. Atomic force microscopy allows for visualization and size determination of nanophase-separated regimes, revealing a nanoscale rod-like assembly of HS. Small-angle X-ray scattering confirms nanophase separation within the NIPU, characterizing both nanoscale amorphous domains and varying degrees of crystallinity. These NIPUs, which are synthesized with BCI monomers, display expected phase separation that is comparable to isocyanate-derived analogues. This work demonstrates nanophase separation in BCI-derived NIPUs and the feasibility of this nonisocyanate synthetic pathway for the preparation of segmented PU copolymers.}, }
@article {pmid38470574, year = {2024}, author = {Pan, H and Wang, Y and Li, Z and Chu, X and Teng, B and Gao, H}, title = {A Complete Scheme for Multi-Character Classification Using EEG Signals From Speech Imagery.}, journal = {IEEE transactions on bio-medical engineering}, volume = {71}, number = {8}, pages = {2454-2462}, doi = {10.1109/TBME.2024.3376603}, pmid = {38470574}, issn = {1558-2531}, mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Speech/physiology ; *Brain-Computer Interfaces ; Algorithms ; Wavelet Analysis ; Imagination/physiology ; Adult ; Male ; Female ; Neural Networks, Computer ; }, abstract = {Some classification studies of brain-computer interface (BCI) based on speech imagery show potential for improving communication skills in patients with amyotrophic lateral sclerosis (ALS). However, current research on speech imagery is limited in scope and primarily focuses on vowels or a few selected words. In this paper, we propose a complete research scheme for multi-character classification based on EEG signals derived from speech imagery. Firstly, we record 31 speech imagery contents, including 26 alphabets and five commonly used punctuation marks, from seven subjects using a 32-channel electroencephalogram (EEG) device. Secondly, we introduce the wavelet scattering transform (WST), which shares a structural resemblance to Convolutional Neural Networks (CNNs), for feature extraction. The WST is a knowledge-driven technique that preserves high-frequency information and maintains the deformation stability of EEG signals. To reduce the dimensionality of wavelet scattering coefficient features, we employ Kernel Principal Component Analysis (KPCA). Finally, the reduced features are fed into an Extreme Gradient Boosting (XGBoost) classifier within a multi-classification framework. The XGBoost classifier is optimized through hyperparameter tuning using grid search and 10-fold cross-validation, resulting in an average accuracy of 78.73% for the multi-character classification task. We utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) technology to visualize the low-dimensional representation of multi-character speech imagery. This visualization effectively enables us to observe the clustering of similar characters. The experimental results demonstrate the effectiveness of our proposed multi-character classification scheme. Furthermore, our classification categories and accuracy exceed those reported in existing research.}, }
@article {pmid38468815, year = {2024}, author = {Larsen, OFP and Tresselt, WG and Lorenz, EA and Holt, T and Sandstrak, G and Hansen, TI and Su, X and Holt, A}, title = {A method for synchronized use of EEG and eye tracking in fully immersive VR.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1347974}, pmid = {38468815}, issn = {1662-5161}, abstract = {This study explores the synchronization of multimodal physiological data streams, in particular, the integration of electroencephalography (EEG) with a virtual reality (VR) headset featuring eye-tracking capabilities. A potential use case for the synchronized data streams is demonstrated by implementing a hybrid steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) speller within a fully immersive VR environment. The hardware latency analysis reveals an average offset of 36 ms between EEG and eye-tracking data streams and a mean jitter of 5.76 ms. The study further presents a proof of concept brain-computer interface (BCI) speller in VR, showcasing its potential for real-world applications. The findings highlight the feasibility of combining commercial EEG and VR technologies for neuroscientific research and open new avenues for studying brain activity in ecologically valid VR environments. Future research could focus on refining the synchronization methods and exploring applications in various contexts, such as learning and social interactions.}, }
@article {pmid38467434, year = {2024}, author = {Huang, Q and Ellis, CL and Leo, SM and Velthuis, H and Pereira, AC and Dimitrov, M and Ponteduro, FM and Wong, NML and Daly, E and Murphy, DGM and Mahroo, OA and McAlonan, GM}, title = {Retinal GABAergic Alterations in Adults with Autism Spectrum Disorder.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {14}, pages = {}, pmid = {38467434}, issn = {1529-2401}, support = {/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Male ; Adult ; Female ; Humans ; *Autism Spectrum Disorder/drug therapy ; Retina ; Electroencephalography ; gamma-Aminobutyric Acid ; Electroretinography ; }, abstract = {Alterations in γ-aminobutyric acid (GABA) have been implicated in sensory differences in individuals with autism spectrum disorder (ASD). Visual signals are initially processed in the retina, and in this study, we explored the hypotheses that the GABA-dependent retinal response to light is altered in individuals with ASD. Light-adapted electroretinograms were recorded from 61 adults (38 males and 23 females; n = 22 ASD) in response to three stimulus protocols: (1) the standard white flash, (2) the standard 30 Hz flickering protocol, and (3) the photopic negative response protocol. Participants were administered an oral dose of placebo, 15 or 30 mg of arbaclofen (STX209, GABAB agonist) in a randomized, double-blind, crossover order before the test. At baseline (placebo), the a-wave amplitudes in response to single white flashes were more prominent in ASD, relative to typically developed (TD) participants. Arbaclofen was associated with a decrease in the a-wave amplitude in ASD, but an increase in TD, eliminating the group difference observed at baseline. The extent of this arbaclofen-elicited shift significantly correlated with the arbaclofen-elicited shift in cortical responses to auditory stimuli as measured by using an electroencephalogram in our prior study and with broader autistic traits measured with the autism quotient across the whole cohort. Hence, GABA-dependent differences in retinal light processing in ASD appear to be an accessible component of a wider autistic difference in the central processing of sensory information, which may be upstream of more complex autistic phenotypes.}, }
@article {pmid38464165, year = {2024}, author = {Ping, A and Wang, J and García-Cabezas, M&#xc1; and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW}, title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {38464165}, issn = {2692-8205}, support = {R01 MH121706/MH/NIMH NIH HHS/United States ; }, abstract = {The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal (BA), lateral (LA), and central (CeA) nuclei, respectively. Based on recent electrophysiological evidence suggesting mesoscale (millimeters-scale) nature of intra-amygdala functional organization, we have investigated the connectivity of these nuclei using Infrared Neural Stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7T functional Magnetic Resonance Imaging (INS-fMRI). Stimulation of multiple sites within amygdala of single individuals evoked 'mesoscale functional connectivity maps', allowing comparison of BA, LA and CeA connected brainwide networks. This revealed a mesoscale nature of connected sites, complementary spatial patterns of functional connectivity, and topographic relationships of nucleus-specific connections. Our data reveal a functional architecture of systematically organized brainwide networks mediating sensory, cognitive, and autonomic influences from the amygdala.}, }
@article {pmid38463871, year = {2024}, author = {Zhang, X and Zhang, T and Jiang, Y and Zhang, W and Lu, Z and Wang, Y and Tao, Q}, title = {A novel brain-controlled prosthetic hand method integrating AR-SSVEP augmentation, asynchronous control, and machine vision assistance.}, journal = {Heliyon}, volume = {10}, number = {5}, pages = {e26521}, pmid = {38463871}, issn = {2405-8440}, abstract = {BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system.<br><br>METHODS: An asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios.<br><br>RESULTS: Under the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71&#xa0;&#xb1;&#xa0;3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66&#xa0;&#xb1;&#xa0;3.87% and 97.40&#xa0;&#xb1;&#xa0;2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system.<br><br>CONCLUSION: This research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.}, }
@article {pmid38459277, year = {2024}, author = {da Cruz, SP and da Cruz, SP and Pereira, S and Saboya, C and Ramalho, A}, title = {Vitamin D and the Metabolic Phenotype in Weight Loss After Bariatric Surgery: A Longitudinal Study.}, journal = {Obesity surgery}, volume = {34}, number = {5}, pages = {1561-1568}, pmid = {38459277}, issn = {1708-0428}, mesh = {Adult ; Humans ; Vitamin D ; Longitudinal Studies ; *Obesity, Morbid/surgery ; Retrospective Studies ; Obesity/surgery ; Vitamins ; Body Mass Index ; Weight Loss ; *Gastric Bypass ; Phenotype ; Obesity, Abdominal ; }, abstract = {PURPOSE: To evaluate the influence of vitamin D (VD) concentrations coupled with metabolic phenotypes preoperatively and 6 months after Roux-en-Y gastric bypass (RYGB) on body variables and weight loss.<br><br>MATERIALS AND METHODS: A longitudinal, retrospective, analytical study comprising 30 adult individuals assessed preoperatively (T0) and 6&#xa0;months (T1) after undergoing Roux-en-Y gastric bypass. The participants were distributed preoperatively into metabolically healthy obese (MHO) and metabolically unhealthy obese individuals (MUHO) according to the HOMA-IR classification, as well as the adequacy and inadequacy of vitamin D concentrations in the form of 25(OH)D. All participants were assessed for weight, height, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), visceral adiposity index (VAI), body circularity index (BCI), body adiposity index (BAI), weight loss, and assessment of 25(OH)D and 1,25(OH)2D concentrations using high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). The statistical program used was SPSS version 21.<br><br>RESULTS: VD adequacy and a healthy phenotype in the preoperative period may play an important role concerning body fat distribution, as the body averages for WHtR (0.020*) and BCI (0.020*) were lower in MHO participants. In comparison, those with VD inadequacy and MUHOs had higher BAI averages (0.000*) in the postoperative period. Furthermore, it is possible that VD inadequacy before and after RYGB, even in the presence of an unhealthy phenotype, may contribute to the increase in VAI values (0.029*) after this surgery. Only those with inadequate VD and MUHOs had higher 25(OH)D concentrations. Besides, this unhealthy phenotype had a greater reduction in BMI in the early postoperative period (p&#x2009;<&#x2009;0.001).<br><br>CONCLUSION: This study suggests that VD adequacy and the presence of a healthy phenotype appear to have a positive impact on the reduction of visceral fat in the context of pre- and postoperative obesity. In addition, there was a greater weight reduction in those with VD inadequacy and in MUHO, which suggests that the volumetric dilution effect of VD and catabolism after bariatric surgery is more pronounced in this specific metabolic phenotype.}, }
@article {pmid38459194, year = {2024}, author = {Lin, S and Fan, CY and Wang, HR and Li, XF and Zeng, JL and Lan, PX and Li, HX and Zhang, B and Hu, C and Xu, J and Luo, JH}, title = {Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice.}, journal = {Molecular psychiatry}, volume = {29}, number = {8}, pages = {2308-2320}, pmid = {38459194}, issn = {1476-5578}, support = {3192010300//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22A20306//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970902//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; Mice ; *Cell Adhesion Molecules, Neuronal/genetics/metabolism ; *Nucleus Accumbens/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; *Disease Models, Animal ; Male ; *Dopamine/metabolism ; *Nerve Tissue Proteins/genetics/metabolism ; *Membrane Proteins/genetics/metabolism ; *Cognition/physiology ; Autism Spectrum Disorder/genetics/metabolism/physiopathology ; Neurons/metabolism ; Reward ; Corpus Striatum/metabolism ; Gene Knock-In Techniques/methods ; Neural Pathways/metabolism/physiopathology ; Autistic Disorder/genetics/physiopathology/metabolism ; Mice, Inbred C57BL ; Choice Behavior/physiology ; }, abstract = {Cognitive and behavioral rigidity are observed in various psychiatric diseases, including in autism spectrum disorder (ASD). However, the underlying mechanism remains to be elucidated. In this study, we found that neuroligin-3 (NL3) R451C knockin mouse model of autism (KI mice) exhibited deficits in behavioral flexibility in choice selection tasks. Single-unit recording of medium spiny neuron (MSN) activity in the nucleus accumbens (NAc) revealed altered encoding of decision-related cue and impaired updating of choice anticipation in KI mice. Additionally, fiber photometry demonstrated significant disruption in dynamic mesolimbic dopamine (DA) signaling for reward prediction errors (RPEs), along with reduced activity in medial prefrontal cortex (mPFC) neurons projecting to the NAc in KI mice. Interestingly, NL3 re-expression in the mPFC, but not in the NAc, rescued the deficit of flexible behaviors and simultaneously restored NAc-MSN encoding, DA dynamics, and mPFC-NAc output in KI mice. Taken together, this study reveals the frontostriatal circuit dysfunction underlying cognitive inflexibility and establishes a critical role of the mPFC NL3 deficiency in this deficit in KI mice. Therefore, these findings provide new insights into the mechanisms of cognitive and behavioral inflexibility and potential intervention strategies.}, }
@article {pmid38458498, year = {2024}, author = {Song, SS and Druschel, LN and Conard, JH and Wang, JJ and Kasthuri, NM and Ricky Chan, E and Capadona, JR}, title = {Depletion of complement factor 3 delays the neuroinflammatory response to intracortical microelectrodes.}, journal = {Brain, behavior, and immunity}, volume = {118}, number = {}, pages = {221-235}, doi = {10.1016/j.bbi.2024.03.004}, pmid = {38458498}, issn = {1090-2139}, mesh = {Animals ; Mice ; *Complement C3/genetics ; Electrodes, Implanted ; *Inflammation ; Microelectrodes ; }, abstract = {The neuroinflammatory response to intracortical microelectrodes (IMEs) used with brain-machine interfacing (BMI) applications is regarded as the primary contributor to poor chronic performance. Recent developments in high-plex gene expression technologies have allowed for an evolution in the investigation of individual proteins or genes to be able to identify specific pathways of upregulated genes that may contribute to the neuroinflammatory response. Several key pathways that are upregulated following IME implantation are involved with the complement system. The complement system is part of the innate immune system involved in recognizing and eliminating pathogens - a significant contributor to the foreign body response against biomaterials. Specifically, we have identified Complement 3 (C3) as a gene of interest because it is the intersection of several key complement pathways. In this study, we investigated the role of C3 in the IME inflammatory response by comparing the neuroinflammatory gene expression at the microelectrode implant site between C3 knockout (C3[-/-]) and wild-type (WT) mice. We have found that, like in WT mice, implantation of intracortical microelectrodes in C3[-/-] mice yields a dramatic increase in the neuroinflammatory gene expression at all post-surgery time points investigated. However, compared to WT mice, C3 depletion showed reduced expression of many neuroinflammatory genes pre-surgery and 4 weeks post-surgery. Conversely, depletion of C3 increased the expression of many neuroinflammatory genes at 8 weeks and 16 weeks post-surgery, compared to WT mice. Our results suggest that C3 depletion may be a promising therapeutic target for acute, but not chronic, relief of the neuroinflammatory response to IME implantation. Additional compensatory targets may also be required for comprehensive long-term reduction of the neuroinflammatory response for improved intracortical microelectrode performance.}, }
@article {pmid38458260, year = {2024}, author = {Deng, H and Li, M and Li, J and Guo, M and Xu, G}, title = {A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding.}, journal = {Journal of neuroscience methods}, volume = {405}, number = {}, pages = {110108}, doi = {10.1016/j.jneumeth.2024.110108}, pmid = {38458260}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; Electrodiagnosis ; Intention ; Motion ; Movement ; Electroencephalography ; Algorithms ; }, abstract = {BACKGROUND: Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters.<br><br>NEW METHODS: This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects.<br><br>RESULTS: The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust.<br><br>CONCLUSIONS: The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.}, }
@article {pmid38458112, year = {2024}, author = {Cao, HL and Meng, YJ and Zhang, YM and Deng, W and Guo, WJ and Li, ML and Li, T}, title = {The volume of gray matter mediates the relationship between glucolipid metabolism and neurocognition in first-episode, drug-naïve patients with schizophrenia.}, journal = {Journal of psychiatric research}, volume = {172}, number = {}, pages = {402-410}, doi = {10.1016/j.jpsychires.2024.02.055}, pmid = {38458112}, issn = {1879-1379}, mesh = {Humans ; Gray Matter/diagnostic imaging/pathology ; *Schizophrenia/diagnostic imaging/pathology ; *Insulin Resistance ; Magnetic Resonance Imaging/methods ; Cholesterol ; Triglycerides ; }, abstract = {We aimed to examine the hypotheses that glucolipid metabolism is linked to neurocognition and gray matter volume (GMV) and that GMV mediates the association of glucolipid metabolism with neurocognition in first-episode, drug-naïve (FEDN) patients with schizophrenia. Parameters of glucolipid metabolism, neurocognition, and magnetic resonance imaging were assessed in 63 patients and 31 controls. Compared to controls, patients exhibited higher levels of fasting glucose, triglyceride, and insulin resistance index, lower levels of cholesterol and high-density lipoprotein cholesterol, poorer neurocognitive functions, and decreased GMV in the bilateral insula, left middle occipital gyrus, and left postcentral gyrus. In the patient group, triglyceride levels and the insulin resistance index exhibited a negative correlation with Rapid Visual Information Processing (RVP) mean latency, a measure of attention within the Cambridge Neurocognitive Test Automated Battery (CANTAB), while showing a positive association with GMV in the right insula. The mediation model revealed that triglyceride and insulin resistance index had a significant positive indirect (mediated) influence on RVP mean latency through GMV in the right insula. Glucolipid metabolism was linked to both neurocognitive functions and GMV in FEDN patients with schizophrenia, with the effect pattern differing from that observed in chronic schizophrenia or schizophrenia comorbid with metabolic syndrome. Moreover, glucolipid metabolism might indirectly contribute to neurocognitive deficits through the mediating role of GMV in these patients.}, }
@article {pmid38457067, year = {2024}, author = {Ma, C and Li, W and Ke, S and Lv, J and Zhou, T and Zou, L}, title = {Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {7}, pages = {2133-2144}, pmid = {38457067}, issn = {1741-0444}, support = {BE2021012-2//Jiangsu Provincial Key Research and Development Program/ ; BE2021012-5//Jiangsu Provincial Key Research and Development Program/ ; CE20225034//Changzhou Science and Technology Bureau Plan/ ; 2020E10010-04//Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province/ ; }, mesh = {Humans ; *Autism Spectrum Disorder/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiopathology ; Male ; *Neural Networks, Computer ; Female ; Adolescent ; Child ; Young Adult ; Adult ; Brain Mapping/methods ; Image Processing, Computer-Assisted/methods ; }, abstract = {Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.}, }
@article {pmid38457065, year = {2024}, author = {Majdi, H and Azarnoosh, M and Ghoshuni, M and Sabzevari, VR}, title = {Direct lingam and visibility graphs for analyzing brain connectivity in BCI.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {62}, number = {7}, pages = {2117-2132}, pmid = {38457065}, issn = {1741-0444}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Algorithms ; Bayes Theorem ; Support Vector Machine ; Signal Processing, Computer-Assisted ; Imagination/physiology ; }, abstract = {The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.}, }
@article {pmid38456888, year = {2024}, author = {Meier, K and de Vos, CC and Bordeleau, M and van der Tuin, S and Billet, B and Ruland, T and Blichfeldt-Eckhardt, MR and Winkelmüller, M and Gulisano, HA and Gatzinsky, K and Knudsen, AL and Hedemann Sørensen, JC and Milidou, I and Cottin, SC}, title = {Examining the Duration of Carryover Effect in Patients With Chronic Pain Treated With Spinal Cord Stimulation (EChO Study): An Open, Interventional, Investigator-Initiated, International Multicenter Study.}, journal = {Neuromodulation : journal of the International Neuromodulation Society}, volume = {27}, number = {5}, pages = {887-898}, doi = {10.1016/j.neurom.2024.01.002}, pmid = {38456888}, issn = {1525-1403}, mesh = {Humans ; *Spinal Cord Stimulation/methods ; Male ; Female ; Middle Aged ; *Chronic Pain/therapy ; Aged ; Adult ; Time Factors ; Prospective Studies ; Pain Measurement/methods ; Treatment Outcome ; Internationality ; Neuralgia/therapy ; }, abstract = {OBJECTIVES: Spinal cord stimulation (SCS) is a surgical treatment for severe, chronic, neuropathic pain. It is based on one to two lead(s) implanted in the epidural space, stimulating the dorsal column. It has long been assumed that when deactivating SCS, there is a variable interval before the patient perceives the return of the pain, a phenomenon often termed echo or carryover effect. Although the carryover effect has been problematized as a source of error in crossover studies, no experimental investigation of the effect has been published. This open, prospective, international multicenter study aimed to systematically document, quantify, and investigate the carryover effect in SCS.<br><br>MATERIALS AND METHODS: Eligible patients with a beneficial effect from their SCS treatment were instructed to deactivate their SCS device in a home setting and to reactivate it when their pain returned. The primary outcome was duration of carryover time defined as the time interval from deactivation to reactivation. Central clinical parameters (age, sex, indication for SCS, SCS treatment details, pain score) were registered and correlated with carryover time using nonparametric tests (Mann-Whitney/Kruskal-Wallis) for categorical data and linear regression for continuous data.<br><br>RESULTS: In total, 158 patients were included in the analyses. A median carryover time of five hours was found (interquartile range 2.5;21 hours). Back pain as primary indication for SCS, high-frequency stimulation, and higher pain score at the time of deactivation were correlated with longer carryover time.<br><br>CONCLUSIONS: This study confirms the existence of the carryover effect and indicates a remarkably high degree of interindividual variation. The results suggest that the magnitude of carryover may be correlated to the nature of the pain condition and possibly stimulation paradigms.<br><br>CLINICAL TRIAL REGISTRATION: The Clinicaltrials.gov registration number for the study is NCT03386058.}, }
@article {pmid38454700, year = {2024}, author = {Zhang, Y and Wu, X and Sun, J and Yue, K and Lu, S and Wang, B and Liu, W and Shi, H and Zou, L}, title = {Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {21}, number = {2}, pages = {2646-2670}, doi = {10.3934/mbe.2024117}, pmid = {38454700}, issn = {1551-0018}, mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Canonical Correlation Analysis ; Brain/diagnostic imaging ; Electroencephalography ; Brain Mapping/methods ; }, abstract = {Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.}, }
@article {pmid38454099, year = {2024}, author = {Chen, Y and Stephani, T and Bagdasarian, MT and Hilsmann, A and Eisert, P and Villringer, A and Bosse, S and Gaebler, M and Nikulin, VV}, title = {Realness of face images can be decoded from non-linear modulation of EEG responses.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {5683}, pmid = {38454099}, issn = {2045-2322}, mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Eye ; Neurologic Examination ; Photic Stimulation ; *Brain-Computer Interfaces ; }, abstract = {Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face's eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.}, }
@article {pmid38452824, year = {2024}, author = {Yu, Q and Liu, L and Du, M and Müller, D and Gu, Y and Gao, Z and Xin, X and Gu, Y and He, M and Marquardt, T and Wang, L}, title = {Sacral Neural Crest-Independent Origin of the Enteric Nervous System in Mouse.}, journal = {Gastroenterology}, volume = {166}, number = {6}, pages = {1085-1099}, doi = {10.1053/j.gastro.2024.02.034}, pmid = {38452824}, issn = {1528-0012}, mesh = {Animals ; *Neural Crest/cytology/embryology ; *Enteric Nervous System/embryology ; *Cell Lineage ; Mice ; *Cell Movement ; Coculture Techniques ; Mice, Transgenic ; Vagus Nerve/embryology ; Sacrum/innervation ; }, abstract = {BACKGROUND &amp; AIMS: The enteric nervous system (ENS), the gut's intrinsic nervous system critical for gastrointestinal function and gut-brain communication, is believed to mainly originate from vagal neural crest cells (vNCCs) and partially from sacral NCCs (sNCCs). Resolving the exact origins of the ENS is critical for understanding congenital ENS diseases but has been confounded by the inability to distinguish between both NCC populations in situ. Here, we aimed to resolve the exact origins of the mammalian ENS.<br><br>METHODS: We genetically engineered mouse embryos facilitating comparative lineage-tracing of either all (pan-) NCCs including vNCCs or caudal trunk and sNCCs (s/tNCCs) excluding vNCCs. This was combined with dual-lineage tracing and 3-dimensional reconstruction of pelvic plexus and hindgut to precisely pinpoint sNCC and vNCC contributions. We further used coculture assays to determine the specificity of cell migration from different neural tissues into the hindgut.<br><br>RESULTS: Both pan-NCCs and s/tNCCs contributed to established NCC derivatives but only&#xa0;pan-NCCs contributed to the ENS. Dual-lineage tracing combined with 3-dimensional reconstruction revealed that s/tNCCs settle in complex patterns in pelvic plexus and hindgut-surrounding tissues, explaining previous confusion regarding their contributions. Coculture experiments revealed unspecific cell migration from autonomic, sensory, and neural tube explants into the hindgut. Lineage tracing of ENS precursors lastly provided complimentary evidence for an exclusive vNCC origin of the murine ENS.<br><br>CONCLUSIONS: sNCCs do not contribute to the murine ENS, suggesting that the mammalian ENS exclusively originates from vNCCs. These results have immediate implications for comprehending (and devising treatments for) congenital ENS disorders, including Hirschsprung's disease.}, }
@article {pmid38450225, year = {2024}, author = {Attallah, O and Al-Kabbany, A and Zaghlool, SB and Kholief, M}, title = {Editorial: Immersive technology and ambient intelligence for assistive living, medical, and healthcare solutions.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1376959}, pmid = {38450225}, issn = {1662-5161}, }
@article {pmid38450005, year = {2024}, author = {Welter, M and Lotte, F}, title = {Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review.}, journal = {Frontiers in neuroergonomics}, volume = {5}, number = {}, pages = {1341790}, pmid = {38450005}, issn = {2673-6195}, abstract = {In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.}, }
@article {pmid38449664, year = {2024}, author = {Hu, J}, title = {Augmented-reality based brain-computer interface of robot control.}, journal = {Heliyon}, volume = {10}, number = {5}, pages = {e26255}, pmid = {38449664}, issn = {2405-8440}, abstract = {Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.}, }
@article {pmid38448793, year = {2024}, author = {Vogel, AP and Spencer, C and Burke, K and de Bruyn, D and Gibilisco, P and Blackman, S and Vojtech, JM and Kathiresan, T}, title = {Optimizing Communication in Ataxia: A Multifaceted Approach to Alternative and Augmentative Communication (AAC).}, journal = {Cerebellum (London, England)}, volume = {23}, number = {5}, pages = {2142-2151}, pmid = {38448793}, issn = {1473-4230}, support = {220100253//Australian Research Council/ ; }, mesh = {Humans ; *Ataxia/therapy/rehabilitation/physiopathology ; *Communication Devices for People with Disabilities ; Communication ; Communication Disorders/rehabilitation/therapy ; }, abstract = {The progression of multisystem neurodegenerative diseases such as ataxia significantly impacts speech and communication, necessitating adaptive clinical care strategies. With the deterioration of speech, Alternative and Augmentative Communication (AAC) can play an ever increasing role in daily life for individuals with ataxia. This review describes the spectrum of AAC resources available, ranging from unaided gestures and sign language to high-tech solutions like speech-generating devices (SGDs) and eye-tracking technology. Despite the availability of various AAC tools, their efficacy is often compromised by the physical limitations inherent in ataxia, including upper limb ataxia and visual disturbances. Traditional speech-to-text algorithms and eye gaze technology face challenges in accuracy and efficiency due to the atypical speech and movement patterns associated with the disease.In addressing these challenges, maintaining existing speech abilities through rehabilitation is prioritized, complemented by advances in digital therapeutics to provide home-based treatments. Simultaneously, projects incorporating AI driven solutions aim to enhance the intelligibility of dysarthric speech through improved speech-to-text accuracy.This review discusses the complex needs assessment for AAC in ataxia, emphasizing the dynamic nature of the disease and the importance of regular reassessment to tailor communication strategies to the changing abilities of the individual. It also highlights the necessity of multidisciplinary involvement for effective AAC assessment and intervention. The future of AAC looks promising with developments in brain-computer interfaces and the potential of voice banking, although their application in ataxia requires further exploration.}, }
@article {pmid38447577, year = {2024}, author = {Cheng, H and Chen, D and Li, X and Al-Sheikh, U and Duan, D and Fan, Y and Zhu, L and Zeng, W and Hu, Z and Tong, X and Zhao, G and Zhang, Y and Zou, W and Duan, S and Kang, L}, title = {Phasic/tonic glial GABA differentially transduce for olfactory adaptation and neuronal aging.}, journal = {Neuron}, volume = {112}, number = {9}, pages = {1473-1486.e6}, doi = {10.1016/j.neuron.2024.02.006}, pmid = {38447577}, issn = {1097-4199}, mesh = {Animals ; *gamma-Aminobutyric Acid/metabolism ; *Caenorhabditis elegans ; *Neuroglia/metabolism/physiology ; *Adaptation, Physiological/physiology ; Smell/physiology ; Caenorhabditis elegans Proteins/metabolism/genetics ; Signal Transduction/physiology ; Cellular Senescence/physiology ; Olfactory Receptor Neurons/physiology/metabolism ; Aging/physiology/metabolism ; Receptors, GABA-A/metabolism ; }, abstract = {Phasic (fast) and tonic (sustained) inhibition of γ-aminobutyric acid (GABA) are fundamental for regulating day-to-day activities, neuronal excitability, and plasticity. However, the mechanisms and physiological functions of glial GABA transductions remain poorly understood. Here, we report that the AMsh glia in Caenorhabditis elegans exhibit both phasic and tonic GABAergic signaling, which distinctively regulate olfactory adaptation and neuronal aging. Through genetic screening, we find that GABA permeates through bestrophin-9/-13/-14 anion channels from AMsh glia, which primarily activate the metabolic GABAB receptor GBB-1 in the neighboring ASH sensory neurons. This tonic action of glial GABA regulates the age-associated changes of ASH neurons and olfactory responses via a conserved signaling pathway, inducing neuroprotection. In addition, the calcium-evoked, vesicular glial GABA release acts upon the ionotropic GABAA receptor LGC-38 in ASH neurons to regulate olfactory adaptation. These findings underscore the fundamental significance of glial GABA in maintaining healthy aging and neuronal stability.}, }
@article {pmid38446762, year = {2024}, author = {Sabio, J and Williams, NS and McArthur, GM and Badcock, NA}, title = {A scoping review on the use of consumer-grade EEG devices for research.}, journal = {PloS one}, volume = {19}, number = {3}, pages = {e0291186}, pmid = {38446762}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Engineering ; }, abstract = {BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.<br><br>PURPOSE: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.<br><br>METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.<br><br>RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.<br><br>CONCLUSIONS: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.}, }
@article {pmid38445386, year = {2024}, author = {Liu, Y and Ren, H and Zhang, Y and Deng, W and Ma, X and Zhao, L and Li, X and Sham, P and Wang, Q and Li, T}, title = {Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study.}, journal = {Psychological medicine}, volume = {54}, number = {9}, pages = {2054-2062}, pmid = {38445386}, issn = {1469-8978}, mesh = {Humans ; *Schizophrenia/genetics/pathology ; *Mendelian Randomization Analysis ; *Inflammation/genetics ; *Genome-Wide Association Study ; *Brain/pathology/diagnostic imaging ; *Polymorphism, Single Nucleotide ; C-Reactive Protein/genetics/metabolism ; Cytokines/genetics/metabolism ; Magnetic Resonance Imaging ; White Matter/pathology/diagnostic imaging ; }, abstract = {BACKGROUND: Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia.<br><br>METHOD: We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain.<br><br>RESULTS: We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-&#x3b2;, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum.<br><br>CONCLUSION: With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.}, }
@article {pmid38444036, year = {2024}, author = {Guo, Z and Tian, C and Shi, Y and Song, XR and Yin, W and Tao, QQ and Liu, J and Peng, GP and Wu, ZY and Wang, YJ and Zhang, ZX and Zhang, J}, title = {Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer's disease diagnosis and differential diagnosis.}, journal = {Acta neuropathologica communications}, volume = {12}, number = {1}, pages = {38}, pmid = {38444036}, issn = {2051-5960}, support = {82020108012//Natural Science Foundation of China/ ; 82201560//Natural Science Foundation of China/ ; 2020R01001//Leading Innovation and Entrepreneurship Team in Zhejiang Province/ ; }, mesh = {Humans ; *Alzheimer Disease/diagnosis ; Diagnosis, Differential ; NAD ; Proteomics ; *Extracellular Vesicles ; }, abstract = {Accurate differential diagnosis among various dementias is crucial for effective treatment of Alzheimer's disease (AD). The study began with searching for novel blood-based neuronal extracellular vesicles (EVs) that are more enriched in the brain regions vulnerable to AD development and progression. With extensive proteomic profiling, GABRD and GPR162 were identified as novel brain regionally enriched plasma EVs markers. The performance of GABRD and GPR162, along with the AD molecule pTau217, was tested using the self-developed and optimized nanoflow cytometry-based technology, which not only detected the positive ratio of EVs but also concurrently presented the corresponding particle size of the EVs, in discovery (n = 310) and validation (n = 213) cohorts. Plasma GABRD[+]- or GPR162[+]-carrying pTau217-EVs were significantly reduced in AD compared with healthy control (HC). Additionally, the size distribution of GABRD[+]- and GPR162[+]-carrying pTau217-EVs were significantly different between AD and non-AD dementia (NAD). An integrative model, combining age, the number and corresponding size of the distribution of GABRD[+]- or GPR162[+]-carrying pTau217-EVs, accurately and sensitively discriminated AD from HC [discovery cohort, area under the curve (AUC) = 0.96; validation cohort, AUC = 0.93] and effectively differentiated AD from NAD (discovery cohort, AUC = 0.91; validation cohort, AUC = 0.90). This study showed that brain regionally enriched neuronal EVs carrying pTau217 in plasma may serve as a robust diagnostic and differential diagnostic tool in both clinical practice and trials for AD.}, }
@article {pmid38442053, year = {2024}, author = {Lai, E and Mai, X and Ji, M and Li, S and Meng, J}, title = {High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction.}, journal = {IEEE journal of biomedical and health informatics}, volume = {28}, number = {5}, pages = {2769-2780}, doi = {10.1109/JBHI.2024.3373332}, pmid = {38442053}, issn = {2168-2208}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Male ; Adult ; *Signal Processing, Computer-Assisted ; Female ; Young Adult ; *Electroencephalography/methods ; *Algorithms ; Photic Stimulation/methods ; Asthenopia/physiopathology ; }, abstract = {In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.}, }
@article {pmid38441825, year = {2024}, author = {Patel, P and Balasubramanian, S and Annavarapu, RN}, title = {Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.}, journal = {Brain informatics}, volume = {11}, number = {1}, pages = {7}, pmid = {38441825}, issn = {2198-4018}, abstract = {Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.}, }
@article {pmid38440775, year = {2024}, author = {Fischer-Janzen, A and Wendt, TM and Van Laerhoven, K}, title = {A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.}, journal = {Frontiers in robotics and AI}, volume = {11}, number = {}, pages = {1326670}, pmid = {38440775}, issn = {2296-9144}, abstract = {Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.}, }
@article {pmid38439983, year = {2024}, author = {Wang, Z and Ding, J and Tan, J and Liu, J and Zhang, T and Cai, W and Meng, S}, title = {UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.}, journal = {Frontiers in plant science}, volume = {15}, number = {}, pages = {1358965}, pmid = {38439983}, issn = {1664-462X}, abstract = {Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R[2] of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.}, }
@article {pmid38439522, year = {2024}, author = {Zheng, M and Lou, F and Huang, Y and Pan, S and Zhang, X}, title = {MR-based electrical property tomography using a physics-informed network at 3 and 7 T.}, journal = {NMR in biomedicine}, volume = {37}, number = {8}, pages = {e5137}, doi = {10.1002/nbm.5137}, pmid = {38439522}, issn = {1099-1492}, support = {2021ZD0200401//STI 2030 - Major Projects/ ; 52277232//National Natural Science Foundation of China/ ; 52293424//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 226-2022-00136//Fundamental Research Funds for the Central Universities/ ; 226-2023-00125//Fundamental Research Funds for the Central Universities/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation/ ; BE2022049//Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//Key-Area R&amp;D Program of Guangdong Province/ ; }, mesh = {Humans ; *Magnetic Resonance Imaging ; *Tomography ; Brain/diagnostic imaging ; Physics ; Phantoms, Imaging ; Water/chemistry ; Computer Simulation ; Male ; Female ; }, abstract = {Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of B → 1 + and B → 1 - by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.}, }
@article {pmid38438935, year = {2024}, author = {Papaioannou, D and Sprange, K and Hamer-Kiwacz, S and Mooney, C and Moody, G and Cooper, C}, title = {Recording harms in randomised controlled trials of behaviour change interventions: a qualitative study of UK clinical trials units and NIHR trial investigators.}, journal = {Trials}, volume = {25}, number = {1}, pages = {163}, pmid = {38438935}, issn = {1745-6215}, support = {CTU Support Funding//National Institute for Health and Care Research/ ; }, mesh = {Humans ; Focus Groups ; Knowledge ; Language ; Qualitative Research ; Randomized Controlled Trials as Topic ; United Kingdom ; *Behavior Therapy ; }, abstract = {BACKGROUND: Harms, also known as adverse events (AEs), are recorded and monitored in randomised controlled trials (RCTs) to ensure participants' safety. Harms are recorded poorly or inconsistently in RCTs of Behaviour Change Interventions (BCI); however, limited guidance exists on how to record harms in BCI trials. This qualitative study explored experiences and perspectives from multi-disciplinary trial experts on recording harms in BCI trials.<br><br>METHODS: Data were collected through fifteen in-depth semi-structured qualitative interviews and three focus groups with thirty-two participants who work in the delivery and oversight of clinical trials. Participants included multi-disciplinary staff from eight CTUs, Chief investigators, and patient and public representatives. Interviews and focus group recordings were transcribed verbatim and thematic analysis was used to analyse the transcripts.<br><br>RESULTS: Five themes were identified, namely perception and understanding of harm, proportionate reporting and plausibility, the need for a multi-disciplinary approach, language of BCI harms and complex harms for complex interventions. Participants strongly believed harms should be recorded in BCI trials; however, making decisions on "how and what to record as harms" was difficult. Recording irrelevant harms placed a high burden on trial staff and participants, drained trial resources and was perceived as for little purpose. Participants believed proportionate recording was required that focused on events with a strong plausible link to the intervention. Multi-disciplinary trial team input was essential for identifying and collecting harms; however, this was difficult in practice due to lack of knowledge on harms from BCIs, lack of input or difference in opinion. The medical language of harms was recognised as a poor fit for BCI trial harms but was familiar and established within internal processes. Future guidance on this topic would be welcomed and could include summarised literature.<br><br>CONCLUSIONS: Recording harms or adverse events in behaviour change intervention trials is complex and challenging; multi-disciplinary experts in trial design and implementation welcome forthcoming guidance on this topic. Issues include the high burden of recording irrelevant harms and use of definitions originally designed for drug trials. Proportionate recording of harms focused on events with a strong plausible link to the intervention and multi-disciplinary team input into decision making are essential.}, }
@article {pmid38437792, year = {2024}, author = {Sanft, TB and Wong, J and O'Neal, B and Siuliukina, N and Jankowitz, RC and Pegram, MD and Fox, JR and Zhang, Y and Treuner, K and O'Shaughnessy, JA}, title = {Impact of the Breast Cancer Index for Extended Endocrine Decision-Making: First Results of the Prospective BCI Registry Study.}, journal = {Journal of the National Comprehensive Cancer Network : JNCCN}, volume = {22}, number = {2}, pages = {99-107}, doi = {10.6004/jnccn.2023.7087}, pmid = {38437792}, issn = {1540-1413}, mesh = {Humans ; Female ; *Breast Neoplasms/pathology ; Prospective Studies ; *Brain-Computer Interfaces ; Chemotherapy, Adjuvant/methods ; Neoplasm Recurrence, Local/drug therapy ; }, abstract = {BACKGROUND: The Breast Cancer Index (BCI) test assay provides an individualized risk of late distant recurrence (5-10 years) and predicts the likelihood of benefitting from extended endocrine therapy (EET) in hormone receptor-positive early-stage breast cancer. This analysis aimed to assess the impact of BCI on EET decision-making in current clinical practice.<br><br>METHODS: The BCI Registry study evaluates long-term outcomes, decision impact, and medication adherence in patients receiving BCI testing as part of routine clinical care. Physicians and patients completed pre-BCI and post-BCI test questionnaires to assess a range of questions, including physician decision-making and confidence regarding EET; patient preferences and concerns about the cost, side effects, drug safety, and benefit of EET; and patient satisfaction regarding treatment recommendations. Pre-BCI and post-BCI test responses were compared using McNemar's test and Wilcoxon signed rank test.<br><br>RESULTS: Pre-BCI and post-BCI questionnaires were completed for 843 physicians and 823 patients. The mean age at enrollment was 65 years, and 88.4% of patients were postmenopausal. Of the tumors, 74.7% were T1, 53.4% were grade 2, 76.0% were N0, and 13.8% were HER2-positive. Following BCI testing, physicians changed EET recommendations in 40.1% of patients (P<.0001), and 45.1% of patients changed their preferences for EET (P<.0001). In addition, 38.8% of physicians felt more confident in their recommendation (P<.0001), and 41.4% of patients felt more comfortable with their EET decision (P<.0001). Compared with baseline, significantly more patients were less concerned about the cost (20.9%; P<.0001), drug safety (25.4%; P=.0014), and benefit of EET (29.3%; P=.0002).<br><br>CONCLUSIONS: This analysis in a large patient cohort of the BCI Registry confirms and extends previous findings on the significant decision-making impact of BCI on EET. Incorporating BCI into clinical practice resulted in changes in physician recommendations, increased physician confidence, improved patient satisfaction, and reduced patient concerns regarding the cost, drug safety, and benefit of EET.}, }
@article {pmid38437148, year = {2024}, author = {Gu, M and Pei, W and Gao, X and Wang, Y}, title = {Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {1090-1099}, doi = {10.1109/TNSRE.2024.3372594}, pmid = {38437148}, issn = {1558-0210}, mesh = {Humans ; Photic Stimulation/methods ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Computer Systems ; }, abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.}, }
@article {pmid38437070, year = {2024}, author = {Shi, C and He, Y and Gourdouparis, M and Dolmans, G and Liu, YH}, title = {A Spatially Diverse 2TX-3RX Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {18}, number = {5}, pages = {1014-1023}, pmid = {38437070}, issn = {1940-9990}, support = {101001448/ERC_/European Research Council/International ; }, mesh = {*Brain-Computer Interfaces ; *Telemetry/instrumentation/methods ; Signal Processing, Computer-Assisted/instrumentation ; Humans ; Equipment Design ; Wireless Technology/instrumentation ; Animals ; Brain/physiology ; }, abstract = {A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is presented. The proposed design features a two channels transmitter and three channels receiver (2TX-3RX) topology, which introduces spatial diversity to effectively mitigate misalignments (both lateral and rotational) between the brain and the skull and recovers the path loss by 13 dB when the RX is in the worst-case blind spot. This spatial diversity also allows the presented telemetry to support the spatial division multiplexing required for a high-capacity multi-implant distributed network. It achieves a signal-to-interference ratio of 12 dB, even with the adjacent interference node placed only 8 mm away from the desired link. While consuming only 0.33 mW for each channel, the presented RX achieves a wide bandwidth of 360 MHz and a low input referred noise of 13.21 nV/√Hz. The presented telemetry achieves a 270 Mbps data rate with a BER < 10[-6] and an energy efficiency of 3.4 pJ/b and 3.7 pJ/b, respectively. The core footprint of the TX and RX modules is only 100 and 52 mm[2], respectively, minimizing the invasiveness of the surgery. The proposed transdural telemetry system has been characterized ex-vivo with a 7-mm thick porcine tissue.}, }
@article {pmid38435744, year = {2024}, author = {Khan, H and Khadka, R and Sultan, MS and Yazidi, A and Ombao, H and Mirtaheri, P}, title = {Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1354143}, pmid = {38435744}, issn = {1662-5161}, abstract = {In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.}, }
@article {pmid38435343, year = {2023}, author = {Dong, K and Liu, WC and Su, Y and Lyu, Y and Huang, H and Zheng, N and Rogers, JA and Nan, K}, title = {Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices.}, journal = {BME frontiers}, volume = {4}, number = {}, pages = {0034}, pmid = {38435343}, issn = {2765-8031}, abstract = {Millimeter-scale animals such as Caenorhabditis elegans, Drosophila larvae, zebrafish, and bees serve as powerful model organisms in the fields of neurobiology and neuroethology. Various methods exist for recording large-scale electrophysiological signals from these animals. Existing approaches often lack, however, real-time, uninterrupted investigations due to their rigid constructs, geometric constraints, and mechanical mismatch in integration with soft organisms. The recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability, offering unique capabilities for chronic, stable interrogation and stimulation of millimeter-scale animals and miniature tissue constructs. This review summarizes the most advanced technologies for electrophysiological studies, based on methods of 3-dimensional flexible bioelectronics. A concluding section addresses the challenges of these devices in achieving freestanding, robust, and multifunctional biointerfaces.}, }
@article {pmid38435127, year = {2023}, author = {Herbert, C}, title = {Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1286895}, pmid = {38435127}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.}, }
@article {pmid38435056, year = {2024}, author = {Thota, AK and Jung, R}, title = {Accelerating neurotechnology development using an Agile methodology.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1328540}, pmid = {38435056}, issn = {1662-4548}, support = {R01 EB023261/EB/NIBIB NIH HHS/United States ; R01 EB027584/EB/NIBIB NIH HHS/United States ; }, abstract = {Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.}, }
@article {pmid38433651, year = {2024}, author = {Ma, X and Qi, Y and Xu, C and Weng, Y and Yu, J and Sun, X and Yu, Y and Wu, Y and Gao, J and Li, J and Shu, Y and Duan, S and Luo, B and Pan, G}, title = {How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study.}, journal = {Human brain mapping}, volume = {45}, number = {4}, pages = {e26586}, pmid = {38433651}, issn = {1097-0193}, support = {2021ZD0200400//STI 2030 Major Projects/ ; 61925603//Natural Science Foundation of China/ ; 62276228//Natural Science Foundation of China/ ; U1909202//Natural Science Foundation of China/ ; }, mesh = {Humans ; *Consciousness ; Reproducibility of Results ; *Wakefulness ; Benchmarking ; Electroencephalography ; Persistent Vegetative State ; }, abstract = {The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.}, }
@article {pmid38430561, year = {2024}, author = {Bekhelifi, O and Berrached, NE and Bendahmane, A}, title = {Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {3}, pages = {}, doi = {10.1088/2057-1976/ad2f58}, pmid = {38430561}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; }, abstract = {Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigue during prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% (± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 (± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.}, }
@article {pmid38429300, year = {2024}, author = {Du, X and Liang, K and Lv, Y and Qiu, S}, title = {Fast reconstruction of EEG signal compression sensing based on deep learning.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {5087}, pmid = {38429300}, issn = {2045-2322}, support = {JYTMS20230377//Liaoning Provincial Education Department/ ; }, mesh = {Signal Processing, Computer-Assisted ; *Deep Learning ; *Data Compression/methods ; Algorithms ; Electroencephalography/methods ; }, abstract = {When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.}, }
@article {pmid38428473, year = {2024}, author = {Wang, M and Deng, Y and Liu, Y and Suo, T and Guo, B and Eickhoff, SB and Xu, J and Rao, H}, title = {The common and distinct brain basis associated with adult and adolescent risk-taking behavior: Evidence from the neuroimaging meta-analysis.}, journal = {Neuroscience and biobehavioral reviews}, volume = {160}, number = {}, pages = {105607}, doi = {10.1016/j.neubiorev.2024.105607}, pmid = {38428473}, issn = {1873-7528}, mesh = {Adult ; Humans ; Adolescent ; *Magnetic Resonance Imaging ; *Brain/diagnostic imaging/physiology ; Frontal Lobe ; Brain Mapping ; Neuroimaging ; Risk-Taking ; }, abstract = {Risk-taking is a common, complex, and multidimensional behavior construct that has significant implications for human health and well-being. Previous research has identified the neural mechanisms underlying risk-taking behavior in both adolescents and adults, yet the differences between adolescents' and adults' risk-taking in the brain remain elusive. This study firstly employs a comprehensive meta-analysis approach that includes 73 adult and 20 adolescent whole-brain experiments, incorporating observations from 1986 adults and 789 adolescents obtained from online databases, including Web of Science, PubMed, ScienceDirect, Google Scholar and Neurosynth. It then combines functional decoding methods to identify common and distinct brain regions and corresponding psychological processes associated with risk-taking behavior in these two cohorts. The results indicated that the neural bases underlying risk-taking behavior in both age groups are situated within the cognitive control, reward, and sensory networks. Subsequent contrast analysis revealed that adolescents and adults risk-taking engaged frontal pole within the fronto-parietal control network (FPN), but the former recruited more ventrolateral area and the latter recruited more dorsolateral area. Moreover, adolescents' risk-taking evoked brain area activity within the ventral attention network (VAN) and the default mode network (DMN) compared with adults, consistent with the functional decoding analyses. These findings provide new insights into the similarities and disparities of risk-taking neural substrates underlying different age cohorts, supporting future neuroimaging research on the dynamic changes of risk-taking.}, }
@article {pmid38428423, year = {2024}, author = {Wang, WW and Ji, SY and Zhang, W and Zhang, J and Cai, C and Hu, R and Zang, SK and Miao, L and Xu, H and Chen, LN and Yang, Z and Guo, J and Qin, J and Shen, DD and Liang, P and Zhang, Y and Zhang, Y}, title = {Structure-based design of non-hypertrophic apelin receptor modulator.}, journal = {Cell}, volume = {187}, number = {6}, pages = {1460-1475.e20}, doi = {10.1016/j.cell.2024.02.004}, pmid = {38428423}, issn = {1097-4172}, mesh = {*Apelin Receptors/agonists/chemistry/ultrastructure ; Cryoelectron Microscopy ; GTP-Binding Proteins/metabolism ; Receptors, G-Protein-Coupled/metabolism ; Signal Transduction ; Humans ; *Cardiovascular Agents/chemistry ; *Drug Design ; }, abstract = {Apelin is a key hormone in cardiovascular homeostasis that activates the apelin receptor (APLNR), which is regarded as a promising therapeutic target for cardiovascular disease. However, adverse effects through the β-arrestin pathway limit its pharmacological use. Here, we report cryoelectron microscopy (cryo-EM) structures of APLNR-Gi1 complexes bound to three agonists with divergent signaling profiles. Combined with functional assays, we have identified "twin hotspots" in APLNR as key determinants for signaling bias, guiding the rational design of two exclusive G-protein-biased agonists WN353 and WN561. Cryo-EM structures of WN353- and WN561-stimulated APLNR-G protein complexes further confirm that the designed ligands adopt the desired poses. Pathophysiological experiments have provided evidence that WN561 demonstrates superior therapeutic effects against cardiac hypertrophy and reduced adverse effects compared with the established APLNR agonists. In summary, our designed APLNR modulator may facilitate the development of next-generation cardiovascular medications.}, }
@article {pmid38426121, year = {2024}, author = {Kumar, S and Alawieh, H and Racz, FS and Fakhreddine, R and Millán, JDR}, title = {Transfer learning promotes acquisition of individual BCI skills.}, journal = {PNAS nexus}, volume = {3}, number = {2}, pages = {pgae076}, pmid = {38426121}, issn = {2752-6542}, abstract = {Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.}, }
@article {pmid38425214, year = {2024}, author = {Shi, C and Zhang, C and Chen, JF and Yao, Z}, title = {Enhancement of low gamma oscillations by volitional conditioning of local field potential in the primary motor and visual cortex of mice.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {2}, pages = {}, doi = {10.1093/cercor/bhae051}, pmid = {38425214}, issn = {1460-2199}, support = {2023ZY1011//Department of Science and Technology of Zhejiang Province/ ; OJQDSP2022007//Scientific Research Starting Foundation of Oujiang Laboratory/ ; LQ18C090002//Natural Science Foundation of Zhejiang Province/ ; }, mesh = {Mice ; Animals ; *Gamma Rhythm ; Brain ; *Visual Cortex ; }, abstract = {Volitional control of local field potential oscillations in low gamma band via brain machine interface can not only uncover the relationship between low gamma oscillation and neural synchrony but also suggest a therapeutic potential to reverse abnormal local field potential oscillation in neurocognitive disorders. In nonhuman primates, the volitional control of low gamma oscillations has been demonstrated by brain machine interface techniques in the primary motor and visual cortex. However, it is not clear whether this holds in other brain regions and other species, for which gamma rhythms might involve in highly different neural processes. Here, we established a closed-loop brain-machine interface and succeeded in training mice to volitionally elevate low gamma power of local field potential in the primary motor and visual cortex. We found that the mice accomplished the task in a goal-directed manner and spiking activity exhibited phase-locking to the oscillation in local field potential in both areas. Moreover, long-term training made the power enhancement specific to direct and adjacent channel, and increased the transcriptional levels of NMDA receptors as well as that of hypoxia-inducible factor relevant to metabolism. Our results suggest that volitionally generated low gamma rhythms in different brain regions share similar mechanisms and pave the way for employing brain machine interface in therapy of neurocognitive disorders.}, }
@article {pmid38423166, year = {2024}, author = {Wang, H and Zhu, Z and Bi, H and Jiang, Z and Cao, Y and Wang, S and Zou, L}, title = {Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment.}, journal = {Neuroscience}, volume = {544}, number = {}, pages = {1-11}, doi = {10.1016/j.neuroscience.2024.02.026}, pmid = {38423166}, issn = {1873-7544}, mesh = {Humans ; *Brain Mapping/methods ; Magnetic Resonance Imaging/methods ; Neural Pathways/diagnostic imaging ; Brain/pathology ; *Cognitive Dysfunction/diagnostic imaging/pathology ; }, abstract = {Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.}, }
@article {pmid38417170, year = {2024}, author = {Yang, J and Zhao, S and Fu, Z and Liu, X}, title = {PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {3}, pages = {}, doi = {10.1088/2057-1976/ad2e36}, pmid = {38417170}, issn = {2057-1976}, mesh = {*Evoked Potentials, Visual ; *Neural Networks, Computer ; Algorithms ; Brain/physiology ; Electroencephalography/methods ; }, abstract = {Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.}, }
@article {pmid38417162, year = {2024}, author = {Blanco-Díaz, CF and Guerrero-Mendez, CD and Delisle-Rodriguez, D and Jaramillo-Isaza, S and Ruiz-Olaya, AF and Frizera-Neto, A and Ferreira de Souza, A and Bastos-Filho, T}, title = {Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {3}, pages = {}, doi = {10.1088/2057-1976/ad2e35}, pmid = {38417162}, issn = {2057-1976}, mesh = {Humans ; Activities of Daily Living ; *Stroke ; Movement ; Electroencephalography/methods ; *Brain-Computer Interfaces ; }, abstract = {Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.}, }
@article {pmid38415197, year = {2024}, author = {Subramaniam, S and Akay, M and Anastasio, MA and Bailey, V and Boas, D and Bonato, P and Chilkoti, A and Cochran, JR and Colvin, V and Desai, TA and Duncan, JS and Epstein, FH and Fraley, S and Giachelli, C and Grande-Allen, KJ and Green, J and Guo, XE and Hilton, IB and Humphrey, JD and Johnson, CR and Karniadakis, G and King, MR and Kirsch, RF and Kumar, S and Laurencin, CT and Li, S and Lieber, RL and Lovell, N and Mali, P and Margulies, SS and Meaney, DF and Ogle, B and Palsson, B and A Peppas, N and Perreault, EJ and Rabbitt, R and Setton, LA and Shea, LD and Shroff, SG and Shung, K and Tolias, AS and van der Meulen, MCH and Varghese, S and Vunjak-Novakovic, G and White, JA and Winslow, R and Zhang, J and Zhang, K and Zukoski, C and Miller, MI}, title = {Grand Challenges at the Interface of Engineering and Medicine.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {5}, number = {}, pages = {1-13}, doi = {10.1109/OJEMB.2024.3351717}, pmid = {38415197}, issn = {2644-1276}, support = {P41 EB027062/EB/NIBIB NIH HHS/United States ; R01 CA249799/CA/NCI NIH HHS/United States ; R01 HL076485/HL/NHLBI NIH HHS/United States ; R01 HL120046/HL/NHLBI NIH HHS/United States ; }, abstract = {Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.}, }
@article {pmid38414845, year = {2024}, author = {Chen, S and Yao, L and Cao, L and Caimmi, M and Jia, J}, title = {Editorial: Exploration of the non-invasive brain-computer interface and neurorehabilitation.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1377665}, doi = {10.3389/fnins.2024.1377665}, pmid = {38414845}, issn = {1662-4548}, }
@article {pmid38414505, year = {2024}, author = {Jin, Y and Wu, C and Chen, W and Li, J and Jiang, H}, title = {Gestational diabetes and risk of perinatal depression in low- and middle-income countries: a meta-analysis.}, journal = {Frontiers in psychiatry}, volume = {15}, number = {}, pages = {1331415}, pmid = {38414505}, issn = {1664-0640}, abstract = {BACKGROUND: The relationship between gestational diabetes (GDM) and the risk of depression has been thoroughly investigated in high-income countries on their financial basis, while it is largely unexplored in low- and middle- income countries. This meta-analysis aims to assess how GDM influences the risk of perinatal depression by searching multiple electronic databases for studies measuring the odds ratios between them in low- and middle-income countries.<br><br>METHODS: Two independent reviewers searched multiple electronic databases for studies that investigated GDM and perinatal mental disorders on August 31, 2023. Pooled odds ratios (ORs) and confidence intervals (CIs) were calculated using the random effect model. Subgroup analyses were further conducted based on the type of study design and country income level.<br><br>RESULTS: In total, 16 observational studies met the inclusion criteria. Only the number of studies on depression (n=10) satisfied the conditions to conduct a meta-analysis, showing the relationship between mental illness and GDM has been overlooked in low- and middle-income countries. Evidence shows an elevated risk of perinatal depression in women with GDM (pooled OR 1.92; 95% CI 1.24, 2.97; 10 studies). The increased risk of perinatal depression in patients with GDM was not significantly different between cross-sectional and prospective design. Country income level is a significant factor that adversely influences the risk of perinatal depression in GDM patients.<br><br>CONCLUSION: Our findings suggested that women with GDM are vulnerable to perinatal depressive symptoms, and a deeper understanding of potential risk factors and mechanisms may help inform strategies aimed at prevention of exposure to these complications during pregnancy.}, }
@article {pmid38413782, year = {2024}, author = {Mondini, V and Sburlea, AI and Müller-Putz, GR}, title = {Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {4714}, pmid = {38413782}, issn = {2045-2322}, mesh = {Humans ; Intention ; Electroencephalography ; Evoked Potentials ; Movement ; *Brain-Computer Interfaces ; Spinal Cord ; *Neocortex ; }, abstract = {Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.}, }
@article {pmid38411720, year = {2024}, author = {Wang, W and Wang, Y and Yin, F and Niu, H and Shin, YK and Li, Y and Kim, ES and Kim, NY}, title = {Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware.}, journal = {Nano-micro letters}, volume = {16}, number = {1}, pages = {133}, pmid = {38411720}, issn = {2150-5551}, abstract = {Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.}, }
@article {pmid38408002, year = {2024}, author = {Wu, J and Akinin, A and Somayajulu, J and Lee, MS and Paul, A and Lu, H and Park, Y and Kim, SJ and Mercier, PP and Cauwenberghs, G}, title = {A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {18}, number = {2}, pages = {263-273}, pmid = {38408002}, issn = {1940-9990}, support = {UF1 NS116377/NS/NINDS NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; }, mesh = {Humans ; Equipment Design ; Electrodes ; *Electroencephalography ; *Brain Waves ; Electric Impedance ; Amplifiers, Electronic ; }, abstract = {Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.}, }
@article {pmid38406999, year = {2024}, author = {Mao, W and Xiao, Q and Shen, X and Zhou, X and Wang, A and Jin, J}, title = {How effort-based self-interest motivation shapes altruistic donation behavior and brain responses.}, journal = {Psychophysiology}, volume = {61}, number = {7}, pages = {e14552}, doi = {10.1111/psyp.14552}, pmid = {38406999}, issn = {1469-8986}, support = {72271166//National Natural Science Foundation of China/ ; 2020011540019//Ningbo University of Technology/ ; }, mesh = {Humans ; *Motivation/physiology ; *Altruism ; Male ; Female ; Young Adult ; Adult ; *Reward ; *Evoked Potentials/physiology ; *Electroencephalography ; }, abstract = {Prosocial behaviors are central to individual and societal well-being. Although the relationship between effort and prosocial behavior is increasingly studied, the impact of effort-based self-interested motivation on prosocial behavior has received less attention. In the current study, we carried out two experiments to examine the effect of motivation to obtain a reward for oneself on donation behavior and brain response. We observed that individuals who accumulated more money in the effort-expenditure rewards task (EEfRT) donated a lower proportion of their earnings. The sigmoid model fitted participants' choices in the EEfRT task, and the effort-reward bias and sigma parameters negatively correlated with the amount of money donated in the donation task. Additionally, the effort-reward bias and sigma parameters negatively predicted N2 amplitude during processing of charitable donation-related information. We propose that individuals who exhibit a lower level of effort-based self-interest motivation may allocate more cognitive control or attentional resources when processing information related to charitable donations. Our work adds weight to understanding the relationship between effort-based self-interest motivation and prosocial behavior and provides electrophysiological evidence.}, }
@article {pmid38406207, year = {2024}, author = {Gao, Y and Zhang, C and Huang, J and Meng, M}, title = {EEG multi-domain feature transfer based on sparse regularized Tucker decomposition.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {1}, pages = {185-197}, pmid = {38406207}, issn = {1871-4080}, abstract = {Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.}, }
@article {pmid38406198, year = {2024}, author = {Niu, X and Peng, Y and Jiang, Z and Huang, S and Liu, R and Zhu, M and Shi, L}, title = {Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {1}, pages = {37-47}, pmid = {38406198}, issn = {1871-4080}, abstract = {Birds have developed visual cognitions, especially in discriminating colors due to their four types of cones in the retina. The entopallium of birds is thought to be involved in the processing of color information during visual cognition. However, there is a lack of understanding about how functional connectivity in the entopallium region of birds changes during color cognition, which is related to various input colors. We therefore trained pigeons to perform a delayed color matching task, in which two colors were randomly presented in sample stimuli phrases, and the neural activity at individual recording site and the gamma band functional connectivity among local population in entopallium during sample presentation were analyzed. Both gamma band energy and gamma band functional connectivity presented dynamics as the stimulus was presented and persisted. The response features in the early-stimulus phase were significantly different from those of baseline and the late-stimulus phase. Furthermore, gamma band energy showed significant differences between different colors during the early-stimulus phase, but the global feature of the gamma band functional network did not. Further decoding results showed that decoding accuracy was significantly enhanced by adding functional connectivity features, suggesting the global feature of the gamma band functional network did not directly contain color information, but was related to it. These results provided insight into information processing rules among local neuronal populations in the entopallium of birds during color cognition, which is important for their daily life.}, }
@article {pmid38406193, year = {2024}, author = {Yin, X and Lin, M}, title = {Multi-information improves the performance of CCA-based SSVEP classification.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {1}, pages = {165-172}, pmid = {38406193}, issn = {1871-4080}, abstract = {The target recognition algorithm based on canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. To reduce visual fatigue and improve the information transfer rate (ITR), how to improve the accuracy of algorithms within a short time window has become one of the main problems at present. There were filter bank CCA (FBCCA), individual template CCA (ITCCA), and temporally local CCA (TCCA), which improve the CCA algorithm from different aspects.This paper proposed to consider individual, frequency, and time information at the same time, so as to extract features more effectively. A comparison of the various methods was performed using benchmark dataset. Classification accuracy and ITR were used for performance evaluation. In the different extensions of CCA, the method incorporating the above three kinds of information simultaneously achieved the best performance within a short time window. This study explores the effect of using a variety of information to improve the CCA algorithm.}, }
@article {pmid38406192, year = {2024}, author = {Niu, X and Peng, Y and Jiang, Z and Huang, S and Liu, R and Zhu, M and Shi, L}, title = {Correction to: Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {1}, pages = {299}, doi = {10.1007/s11571-023-09971-x}, pmid = {38406192}, issn = {1871-4080}, abstract = {[This corrects the article DOI: 10.1007/s11571-022-09916-w.].}, }
@article {pmid38405712, year = {2024}, author = {Kothe, C and Shirazi, SY and Stenner, T and Medine, D and Boulay, C and Grivich, MI and Mullen, T and Delorme, A and Makeig, S}, title = {The Lab Streaming Layer for Synchronized Multimodal Recording.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.02.13.580071}, pmid = {38405712}, issn = {2692-8205}, abstract = {Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.}, }
@article {pmid38404713, year = {2024}, author = {Zhang, X and Wang, Y and Tang, Y and Wang, Z}, title = {Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.}, journal = {Health information science and systems}, volume = {12}, number = {1}, pages = {11}, pmid = {38404713}, issn = {2047-2501}, abstract = {PURPOSE: In the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling individuals to control external devices. However, the low signal-to-noise ratio, multiple channels and non-linearity are the essential challenges of accurate MI classification. To tackle these issues, we investigate the role of adaptive frequency bands selection and spatial-temporal feature learning in decoding motor imagery.<br><br>METHODS: We propose an Adaptive Filter of Frequency Bands based Coordinate Attention Network (AFFB-CAN) to improve the performance of MI classification. Specifically, we design the AFFB to adaptively obtain the upper and lower limits of frequency bands in order to alleviate information loss caused by manual selection. Next, we propose the CAN-based network to emphasize the key brain regions and temporal segments. And we design a multi-scale module to enhance temporal context learning.<br><br>RESULTS: The conducted experiments on the BCI Competition IV-2a and 2b datasets reveal that our approach achieves an outstanding average accuracy, kappa values, and Macro F1-Score with 0.7825, 0.7104, and 0.7486 respectively. Similarly, for the BCI Competition IV-2b dataset, the average accuracy, kappa values, and F1-Score obtained are 0.8879, 0.7427, and 0.8734, respectively.<br><br>CONCLUSION: The proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated with &#xb5; and &#x3b2; rhythms. Moreover, we also find that &#x3b3; rhythms also play an important role in MI classification.}, }
@article {pmid38404196, year = {2025}, author = {Ramkumar, E and Paulraj, M}, title = {Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {28}, number = {1}, pages = {61-78}, doi = {10.1080/10255842.2024.2319701}, pmid = {38404196}, issn = {1476-8259}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Algorithms ; }, abstract = {The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models.}, }
@article {pmid38403735, year = {2025}, author = {Cao, HL and Wei, W and Meng, YJ and Deng, RH and Li, XJ and Deng, W and Liu, YS and Tang, Z and Du, XD and Greenshaw, AJ and Li, ML and Li, T and Guo, WJ}, title = {Interactions between overweight/obesity and alcohol dependence impact human brain white matter microstructure: evidence from DTI.}, journal = {European archives of psychiatry and clinical neuroscience}, volume = {275}, number = {2}, pages = {439-449}, pmid = {38403735}, issn = {1433-8491}, support = {81571305//National Natural Science Foundation of China/ ; 82171487//National Natural Science Foundation of China/ ; SZYJTD201715//Introduction Project of Suzhou Clinical Expert Team/ ; }, mesh = {Humans ; Male ; *Alcoholism/pathology/complications/diagnostic imaging ; Diffusion Tensor Imaging ; Female ; Adult ; *White Matter/diagnostic imaging/pathology ; *Overweight/complications/pathology/diagnostic imaging ; *Obesity/complications/pathology/diagnostic imaging ; Anisotropy ; Middle Aged ; *Brain/pathology/diagnostic imaging ; }, abstract = {There is inconsistent evidence for an association of obesity with white matter microstructural alterations. Such inconsistent findings may be related to the cumulative effects of obesity and alcohol dependence. This study aimed to investigate the possible interactions between alcohol dependence and overweight/obesity on white matter microstructure in the human brain. A total of 60 inpatients with alcohol dependence during early abstinence (44 normal weight and 16 overweight/obese) and 65 controls (42 normal weight and 23 overweight/obese) were included. The diffusion tensor imaging (DTI) measures [fractional anisotropy (FA) and radial diffusivity (RD)] of the white matter microstructure were compared between groups. We observed significant interactive effects between alcohol dependence and overweight/obesity on DTI measures in several tracts. The DTI measures were not significantly different between the overweight/obese and normal-weight groups (although widespread trends of increased FA and decreased RD were observed) among controls. However, among the alcohol-dependent patients, the overweight/obese group had widespread reductions in FA and widespread increases in RD, most of which significantly differed from the normal-weight group; among those with overweight/obesity, the alcohol-dependent group had widespread reductions in FA and widespread increases in RD, most of which were significantly different from the control group. This study found significant interactive effects between overweight/obesity and alcohol dependence on white matter microstructure, indicating that these two controllable factors may synergistically impact white matter microstructure and disrupt structural connectivity in the human brain.}, }
@article {pmid38403619, year = {2024}, author = {Zhang, Z and Chen, Y and Zhao, X and Wang, F and Ding, P and Zhao, L and Fu, Y}, title = {[Ethical considerations for medical applications of implantable brain-computer interfaces].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {41}, number = {1}, pages = {177-183}, pmid = {38403619}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Prostheses and Implants ; Electrodes ; }, abstract = {Implantable brain-computer interfaces (BCIs) have potentially important clinical applications due to the high spatial resolution and signal-to-noise ratio of electrodes that are closer to or implanted in the cerebral cortex. However, the surgery and electrodes of implantable BCIs carry safety risks of brain tissue damage, and their medical applications face ethical challenges, with little literature to date systematically considering ethical norms for the medical applications of implantable BCIs. In order to promote the clinical translation of this type of BCI, we considered the ethics of practice for the medical application of implantable BCIs, including: reducing the risk of brain tissue damage from implantable BCI surgery and electrodes, providing patients with customized and personalized implantable BCI treatments, ensuring multidisciplinary collaboration in the clinical application of implantable BCIs, and the responsible use of implantable BCIs, among others. It is expected that this article will provide thoughts and references for the research and development of ethics of the medical application of implantable BCI.}, }
@article {pmid38400897, year = {2024}, author = {Pirasteh, A and Shamseini Ghiyasvand, M and Pouladian, M}, title = {EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {19}, number = {8}, pages = {3183-3193}, doi = {10.1080/17483107.2024.2316312}, pmid = {38400897}, issn = {1748-3115}, mesh = {Humans ; *Amyotrophic Lateral Sclerosis/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; Support Vector Machine ; Neural Networks, Computer ; }, abstract = {Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.}, }
@article {pmid38400384, year = {2024}, author = {Correia, G and Crosse, MJ and Lopez Valdes, A}, title = {Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {4}, pages = {}, pmid = {38400384}, issn = {1424-8220}, mesh = {Humans ; Ear ; Brain/physiology ; Electroencephalography/methods ; Electrodes ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; }, abstract = {EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.}, }
@article {pmid38400359, year = {2024}, author = {Naseer, N and Niazi, IK and Santosa, H}, title = {Editorial: Signal Processing for Brain-Computer Interfaces-Special Issue.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {4}, pages = {}, pmid = {38400359}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Quality of Life ; Brain ; Signal Processing, Computer-Assisted ; }, abstract = {With the astounding ability to capture a wealth of brain signals, Brain-Computer Interfaces (BCIs) have the potential to revolutionize humans' quality of life [...].}, }
@article {pmid38396098, year = {2024}, author = {Drew, L}, title = {Neuralink brain chip: advance sparks safety and secrecy concerns.}, journal = {Nature}, volume = {627}, number = {8002}, pages = {19}, doi = {10.1038/d41586-024-00550-6}, pmid = {38396098}, issn = {1476-4687}, mesh = {Humans ; *Brain/surgery ; *Brain-Computer Interfaces/adverse effects/trends ; *Confidentiality/ethics ; *Prostheses and Implants/adverse effects ; }, }
@article {pmid38396070, year = {2024}, author = {Şekerci, Y and Kahraman, MU and Özturan, &#xd6; and Çelik, E and Ayan, S&#x15e;}, title = {Neurocognitive responses to spatial design behaviors and tools among interior architecture students: a pilot study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {4454}, pmid = {38396070}, issn = {2045-2322}, mesh = {Humans ; Pilot Projects ; *Emotions/physiology ; *Electroencephalography/methods ; Students ; Recognition, Psychology ; }, abstract = {The impact of emotions on human behavior is substantial, and the ability to recognize people's feelings has a wide range of practical applications including education. Here, the methods and tools of education are being calibrated according to the data gained over electroencephalogram (EEG) signals. The issue of which design tools would be ideal in the future of interior architecture education, is an uncertain field. It is important to measure the students' emotional states while using manual and digital design tools to determine the different impacts. Brain-computer interfaces have made it possible to monitor emotional states in a way that is both convenient and economical. In the research of emotion recognition, EEG signals have been employed, and the resulting literature explains basic emotions as well as complicated scenarios that are created from the combination of numerous basic emotions. The objective of this study is to investigate the emotional states and degrees of attachment experienced by interior architecture students while engaging in their design processes. This includes examining the use of 2D or 3D tools, whether manual or digital, and identifying any changes in design tool usage and behaviors that may be influenced by different teaching techniques. Accordingly, the hierarchical clustering which is a technique used in data analysis to group objects into a hierarchical structure of clusters based on their similarities has been conducted.}, }
@article {pmid38394680, year = {2024}, author = {Noble, SC and Woods, E and Ward, T and Ringwood, JV}, title = {Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad2c9e}, pmid = {38394680}, issn = {1741-2552}, mesh = {Adult ; Humans ; *Neurofeedback/methods ; Electroencephalography/methods ; Single-Blind Method ; Learning ; Cognition ; }, abstract = {Objective. Neurofeedback (NFB) training through brain-computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling task. This study aims to assess the impact of task difficulty adaptation on ERP-based attention training in healthy adults. To achieve this, we introduce a novel adaptation employing iterative learning control (ILC) and compare it against an existing method and a control group with random task difficulty variation.Approach. The study involved 45 healthy participants in a single-blind, three-arm randomised controlled trial. Each group underwent one NFB training session, using different methods to adapt task difficulty in a P300 spelling task: two groups with personalised difficulty adjustments (our proposed ILC and an existing approach) and one group with random difficulty. Cognitive performance was evaluated before and after the training session using a visual spatial attention task and we gathered participant feedback through questionnaires.Main results. All groups demonstrated a significant performance improvement in the spatial attention task post-training, with an average increase of 12.63%. Notably, the group using the proposed iterative learning controller achieved a 22% increase in P300 amplitude during training and a 17% reduction in post-training alpha power, all while significantly accelerating the training process compared to other groups.Significance. Our results suggest that ERP-based NFB training using a P300 speller effectively enhances attention in healthy adults, with significant improvements observed after a single session. Personalised task difficulty adaptation using ILC not only accelerates the training but also enhances ERPs during the training. Accelerating NFB training, while maintaining its effectiveness, is vital for its acceptability by both end-users and clinicians.}, }
@article {pmid38391734, year = {2024}, author = {Rassam, R and Chen, Q and Gai, Y}, title = {Competing Visual Cues Revealed by Electroencephalography: Sensitivity to Motion Speed and Direction.}, journal = {Brain sciences}, volume = {14}, number = {2}, pages = {}, pmid = {38391734}, issn = {2076-3425}, abstract = {Motion speed and direction are two fundamental cues for the mammalian visual system. Neurons in various places of the neocortex show tuning properties in term of firing frequency to both speed and direction. The present study applied a 32-channel electroencephalograph (EEG) system to 13 human subjects while they were observing a single object moving with different speeds in various directions from the center of view to the periphery on a computer monitor. Depending on the experimental condition, the subjects were either required to fix their gaze at the center of the monitor while the object was moving or to track the movement with their gaze; eye-tracking glasses were used to ensure that they followed instructions. In each trial, motion speed and direction varied randomly and independently, forming two competing visual features. EEG signal classification was performed for each cue separately (e.g., 11 speed values or 11 directions), regardless of variations in the other cue. Under the eye-fixed condition, multiple subjects showed distinct preferences to motion direction over speed; however, two outliers showed superb sensitivity to speed. Under the eye-tracking condition, in which the EEG signals presumably contained ocular movement signals, all subjects showed predominantly better classification for motion direction. There was a trend that speed and direction were encoded by different electrode sites. Since EEG is a noninvasive and portable approach suitable for brain-computer interfaces (BCIs), this study provides insights on fundamental knowledge of the visual system as well as BCI applications based on visual stimulation.}, }
@article {pmid38391261, year = {2025}, author = {Kumari, R and Dybus, A and Purcell, M and Vučković, A}, title = {Motor priming to enhance the effect of physical therapy in people with spinal cord injury.}, journal = {The journal of spinal cord medicine}, volume = {48}, number = {2}, pages = {312-326}, pmid = {38391261}, issn = {2045-7723}, mesh = {Humans ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; Middle Aged ; Electroencephalography ; *Physical Therapy Modalities ; *Neurological Rehabilitation/methods ; *Electric Stimulation Therapy/methods ; }, abstract = {CONTEXT: Brain-Computer Interface (BCI) is an emerging neurorehabilitation therapy for people with spinal cord injury (SCI).<br><br>OBJECTIVE: The study aimed to test whether priming the sensorimotor system using BCI-controlled functional electrical stimulation (FES) before physical practice is more beneficial than physical practice alone.<br><br>METHODS: Ten people with subacute SCI participated in a randomized control trial where the experimental (N&#x2009;=&#x2009;5) group underwent BCI-FES priming (&#x223c;15&#x2005;min) before physical practice (30&#x2005;min), while the control (N&#x2009;=&#x2009;5) group performed physical practice (40&#x2005;min) of the dominant hand. The primary outcome measures were BCI accuracy, adherence, and perceived workload. The secondary outcome measures were manual muscle test, grip strength, the range of motion, and Electroencephalography (EEG) measured brain activity.<br><br>RESULTS: The average BCI accuracy was 85%. The experimental group found BCI-FES priming mentally demanding but not frustrating. Two participants in the experimental group did not complete all sessions due to early discharge. There were no significant differences in physical outcomes between the groups. The ratio between eyes closed to eyes opened EEG activity increased more in the experimental group (theta P&#x3b8;&#x2009;=&#x2009;0.008, low beta Pl&#x3b2;&#x2009;=&#x2009;0.009, and high beta Ph&#x3b2;&#x2009;=&#x2009;1.48e-04) indicating better neurological outcomes. There were no measurable immediate effects of BCI-FES priming.<br><br>CONCLUSION: Priming the brain before physical therapy is feasible but may require more than 15&#x2005;min. This warrants further investigation with an increased sample size.}, }
@article {pmid38390751, year = {2024}, author = {El Khoury, J and Hermieu, N and Chesnel, C and Xylinas, E and Teng, M and Ouzaid, I and Hermieu, JF and Amarenco, G and Hentzen, C}, title = {Primary bladder neck obstruction in men: The importance of urodynamic assessment and cystourethrography in measuring its severity.}, journal = {Neurourology and urodynamics}, volume = {43}, number = {4}, pages = {874-882}, doi = {10.1002/nau.25429}, pmid = {38390751}, issn = {1520-6777}, mesh = {Male ; Adult ; Humans ; *Urinary Bladder Neck Obstruction/diagnosis ; Retrospective Studies ; Urodynamics ; Urinary Bladder ; Urination ; }, abstract = {OBJECTIVE: Primary bladder neck obstruction (PBNO) is a condition primarily affecting young men, characterized by obstruction at the bladder neck, leading to lower urinary tract symptoms. The aim of this study was to identify a correlation between the severity of bladder neck opening impairment and urinary symptoms by means of urodynamic studies.<br><br>MATERIALS AND METHODS: A retrospective analysis was conducted in adult males diagnosed with PBNO at a university neurourology department between 2015 and 2022 who underwent voiding cystourethrography (VCUG) and pressure-flow studies. The cohort was divided into two groups: absence of bladder neck opening on VCUG (Group A) and incomplete bladder neck opening (Group B).<br><br>RESULTS: Out of the 82 patients with PBNO screened, 53 were included in the analysis. Nocturia was the only symptom more prevalent in Group A (65% in Group A vs. 30% in Group B, p&#x2009;=&#x2009;0.02) but scores and subscores of the Urinary Symptom Profile questionnaire were not different between groups. In addition, the detrusor pressure at a maximum flow rate (PdetQmax), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI) were higher in Group A than in Group B [PdetQmax (A&#x2009;=&#x2009;93.7&#x2009;&#xb1;&#x2009;53.7&#x2009;cmH2O vs. B&#x2009;=&#x2009;65.7&#x2009;&#xb1;&#x2009;26.4&#x2009;cmH2O; p&#x2009;=&#x2009;0.01)-BOOI (A&#x2009;=&#x2009;77&#x2009;&#xb1;&#x2009;58.3 vs. B&#x2009;=&#x2009;48&#x2009;&#xb1;&#x2009;25.7; p&#x2009;=&#x2009;0.03)-BCI (A&#x2009;=&#x2009;136&#x2009;&#xb1;&#x2009;51.3 vs. B&#x2009;=&#x2009;110&#x2009;&#xb1;&#x2009;41.7; p&#x2009;=&#x2009;0.04)].<br><br>CONCLUSION: This study demonstrates a significant association between the extent of bladder neck opening impairment observed on VCUG and obstruction and contraction urodynamic parameters, but no association with the severity of urinary symptoms. Future studies should evaluate the predictive value of treatment response and the occurrence of complications based on clinical and urodynamic parameters.}, }
@article {pmid38390553, year = {2024}, author = {Chapman, DP and Wu, JY}, title = {Concept for intrathecal delivery of brain recording and stimulation device.}, journal = {Frontiers in medical technology}, volume = {6}, number = {}, pages = {1211585}, pmid = {38390553}, issn = {2673-3129}, abstract = {Neurological disorders are common, yet many neurological diseases don't have efficacious treatments. The protected nature of the brain both anatomically and physiologically through the blood brain barrier (BBB) make it exceptionally hard to access. Recent advancements in interventional approaches, like the Stentrode™, have opened the possibility of using the cerebral vasculature as a highway for minimally invasive therapeutic delivery to the brain. Despite the immense success that the Stentrode™ has faced recently, it is limited to major cerebral vasculature and exists outside the BBB, making drug eluting configurations largely ineffective. The present study seeks to identify a separate anatomical pathway for therapeutic delivery to the deep brain using the ventricular system. The intrathecal route, in which drug pumps and spinal cord stimulators are delivered through a lumbar puncture, is a well-established route for delivering therapies to the spinal cord as high as C1. The present study identifies an extension of this anatomical pathway through the foramen of Magendie and into the brains ventricular system. To test this pathway, a narrow self-expanding electrical recording device was manufactured and its potential to navigate the ventricular system was assessed on human anatomical brain samples. While the results of this paper are largely preliminary and a substantial amount of safety and efficacy data is needed, this paper identifies an important anatomical pathway for delivery of therapeutic and diagnostics tools to the brain that is minimally invasive, can access limbic structures, and is within the BBB.}, }
@article {pmid38388478, year = {2024}, author = {He, Q and Yang, Y and Ge, P and Li, S and Chai, X and Luo, Z and Zhao, J}, title = {The brain nebula: minimally invasive brain-computer interface by endovascular neural recording and stimulation.}, journal = {Journal of neurointerventional surgery}, volume = {16}, number = {12}, pages = {1237-1243}, pmid = {38388478}, issn = {1759-8486}, mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Endovascular Procedures/methods ; *Brain/physiology ; *Deep Brain Stimulation/methods ; }, abstract = {A brain-computer interface (BCI) serves as a direct communication channel between brain activity and external devices, typically a computer or robotic limb. Advances in technology have led to the increasing use of intracranial electrical recording or stimulation in the treatment of conditions such as epilepsy, depression, and movement disorders. This indicates that BCIs can offer clinical neurological rehabilitation for patients with disabilities and functional impairments. They also provide a means to restore consciousness and functionality for patients with sequelae from major brain diseases. Whether invasive or non-invasive, the collected cortical or deep signals can be decoded and translated for communication. This review aims to provide an overview of the advantages of endovascular BCIs compared with conventional BCIs, along with insights into the specific anatomical regions under study. Given the rapid progress, we also provide updates on ongoing clinical trials and the prospects for current research involving endovascular electrodes.}, }
@article {pmid38386506, year = {2024}, author = {Iwane, F and Porssut, T and Blanke, O and Chavarriaga, R and Del R Millán, J and Herbelin, B and Boulic, R}, title = {Customizing the human-avatar mapping based on EEG error related potentials.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, doi = {10.1088/1741-2552/ad2c02}, pmid = {38386506}, issn = {1741-2552}, mesh = {Humans ; *Avatar ; User-Computer Interface ; *Virtual Reality ; Movement ; Electroencephalography ; }, abstract = {Objective.A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. a break-in-embodiment (BiE). However, the way to detect such an inadequate event is currently limited to questionnaires or spontaneous reports from users. The ability to implicitly detect BiE in real-time enables us to adjust human-avatar mapping without interruption.Approach.We propose and empirically demonstrate a novel brain computer interface (BCI) approach that monitors the occurrence of BiE based on the users' brain oscillatory activity in real-time to adjust the human-avatar mapping in VR. We collected EEG activity of 37 participants while they performed reaching movements with their avatar with different magnitude of distortion.Main results.Our BCI approach seamlessly predicts occurrence of BiE in varying magnitude of erroneous interaction. The mapping has been customized by BCI-reinforcement learning (RL) closed-loop system to prevent BiE from occurring. Furthermore, a non-personalized BCI decoder generalizes to new users, enabling 'Plug-and-Play' ErrP-based non-invasive BCI. The proposed VR system allows customization of human-avatar mapping without personalized BCI decoders or spontaneous reports.Significance.We anticipate that our newly developed VR-BCI can be useful to maintain an engaging avatar-based interaction and a compelling immersive experience while detecting when users notice a problem and seamlessly correcting it.}, }
@article {pmid38383721, year = {2024}, author = {Wei, Z and Chen, Y and Zhao, Q and Ren, J and Piao, Y and Zhang, P and Zha, R and Qiu, B and Zhang, D and Bi, Y and Han, S and Li, C and Zhang, X}, title = {Separable amygdala activation patterns in the evaluations of robots.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {34}, number = {2}, pages = {}, doi = {10.1093/cercor/bhae011}, pmid = {38383721}, issn = {1460-2199}, support = {32100886//National Natural Science Foundation of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; GLHZ202128//CAS-VPST Silk Road Science Fund 2021/ ; DJK-LX-2022008//Global Select Project/ ; //Institute of Health and Medicine/ ; //Hefei Comprehensive National Science Center/ ; 2023JYBKFK008//Key Laboratory of Brain-Machine Intelligence for Information Behavior-Ministry of Education/ ; }, mesh = {Humans ; *Robotics/methods ; Brain/physiology ; Neuroimaging ; Amygdala/diagnostic imaging ; Self Report ; }, abstract = {Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.}, }
@article {pmid38382863, year = {2024}, author = {Shi, N and Miao, Y and Huang, C and Li, X and Song, Y and Chen, X and Wang, Y and Gao, X}, title = {Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface.}, journal = {NeuroImage}, volume = {289}, number = {}, pages = {120548}, doi = {10.1016/j.neuroimage.2024.120548}, pmid = {38382863}, issn = {1095-9572}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Signal-To-Noise Ratio ; Communication ; Photic Stimulation ; Algorithms ; }, abstract = {An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.}, }
@article {pmid38382711, year = {2024}, author = {He, L and Zhang, L and Sun, Q and Lin, X}, title = {A generative adaptive convolutional neural network with attention mechanism for driver fatigue detection with class-imbalanced and insufficient data.}, journal = {Behavioural brain research}, volume = {464}, number = {}, pages = {114898}, doi = {10.1016/j.bbr.2024.114898}, pmid = {38382711}, issn = {1872-7549}, mesh = {*Neural Networks, Computer ; Recognition, Psychology ; Upper Extremity ; }, abstract = {Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue detection systems. However, many existing methods involves tedious manual parameter tunings, a process that is both time-consuming and results in task-specific models. On the other hand, most of the researches on fatigue recognition are based on class-balanced and sufficient data, and effectively "mine" meaningful information from class-imbalanced and insufficient data for fatigue recognition is still a challenge. In this paper, we proposed two novel models, the attention-based residual adaptive multiscale fully convolutional network-long short term memory network (ARMFCN-LSTM), and the Generative ARMFCN-LSTM (GARMFCN-LSTM) aiming to address this issue. ARMFCN-LSTM excels at automatically extracting multiscale representations through adaptive multiscale temporal convolutions, while capturing temporal dependency features through LSTM. GARMFCN-LSTM integrates Wasserstein GAN with gradient penalty (WGAN-GP) into ARMFCN-LSTM to improve driver fatigue detection performance by alleviating data scarcity and addressing class imbalances. Experimental results show that ARMFCN-LSTM achieves the highest classification accuracy of 95.84% in driver fatigue detection on the class-balanced EEG dataset (binary classification), and GARMFCN-LSTM attained an improved classification accuracy of 84.70% on the class-imbalanced EOG dataset (triple classification), surpassing the competing methods. Therefore, the proposed models are promising for further implementations in online driver fatigue detection systems.}, }
@article {pmid38382104, year = {2024}, author = {Slack, JC and Zeiser, SL and Yadav, AP}, title = {The role of stimulus periodicity on spinal cord stimulation-induced artificial sensations in rodents.}, journal = {Journal of neural engineering}, volume = {21}, number = {2}, pages = {}, pmid = {38382104}, issn = {1741-2552}, support = {DP2 NS136872/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; Rats ; Animals ; Rodentia ; *Spinal Cord Stimulation/methods ; Sensation ; Learning ; *Brain-Computer Interfaces ; Spinal Cord/physiology ; }, abstract = {Objective.Sensory feedback is critical for effectively controlling brain-machine interfaces and neuroprosthetic devices. Spinal cord stimulation (SCS) is proposed as a technique to induce artificial sensory perceptions in rodents, monkeys, and humans. However, to realize the full potential of SCS as a sensory neuroprosthetic technology, a better understanding of the effect of SCS pulse train parameter changes on sensory detection and discrimination thresholds is necessary.Approach.Here we investigated whether stimulation periodicity impacts rats' ability to detect and discriminate SCS-induced perceptions at different frequencies.Main results.By varying the coefficient of variation (CV) of interstimulus pulse interval, we showed that at lower frequencies, rats could detect highly aperiodic SCS pulse trains at lower amplitudes (i.e. decreased detection thresholds). Furthermore, rats learned to discriminate stimuli with subtle differences in periodicity, and the just-noticeable differences from a highly aperiodic stimulus were smaller than those from a periodic stimulus.Significance.These results demonstrate that the temporal structure of an SCS pulse train is an integral parameter for modulating sensory feedback in neuroprosthetic applications.}, }
@article {pmid38380980, year = {2024}, author = {Van Gerrewey, T and Navarrete, O and Vandecruys, M and Perneel, M and Boon, N and Geelen, D}, title = {Bacterially enhanced plant-growing media for controlled environment agriculture.}, journal = {Microbial biotechnology}, volume = {17}, number = {2}, pages = {e14422}, pmid = {38380980}, issn = {1751-7915}, support = {HBC.2017.0209//Agentschap Innoveren en Ondernemen/ ; }, mesh = {RNA, Ribosomal, 16S/genetics ; *Agriculture ; *Bacteria/genetics ; Plants/genetics ; Soil/chemistry ; Plant Roots/microbiology ; Soil Microbiology ; }, abstract = {Microbe-plant interactions in the root zone not only shape crop performance in soil but also in hydroponic cultivation systems. The biological and physicochemical properties of the plant-growing medium determine the root-associated microbial community and influence bacterial inoculation effectiveness, which affects plant growth. This study investigated the combined impact of plant-growing media composition and bacterial community inoculation on the root-associated bacterial community of hydroponically grown lettuce (Lactuca sativa L.). Ten plant-growing media were composed of varying raw materials, including black peat, white peat, coir pith, wood fibre, composted bark, green waste com