@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 = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c12429},
pmid = {39628388},
issn = {1936-086X},
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 Università 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 = {2024},
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 = {},
number = {},
pages = {115363},
doi = {10.1016/j.bbr.2024.115363},
pmid = {39622415},
issn = {1872-7549},
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 = {2024},
author = {Wei, X and Narayan, J and Faisal, A},
title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9957},
pmid = {39622169},
issn = {1741-2552},
abstract = {Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has aided patients via brain-computer interfaces in neural activity analysis. 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. 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, and individual classifiers (final layers) for specific tasks of each data set. It enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy. 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, 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%. The `Sandwich' framework demonstrates significant 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 as a model-agnostic meta-framework.},
}
@article {pmid39622162,
year = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9956},
pmid = {39622162},
issn = {1741-2552},
abstract = {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 (EEG)-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 (BCIs).},
}
@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},
doi = {10.2196/57727},
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},
doi = {10.1371/journal.pbio.3002918},
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 = {2024},
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},
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 = {2024},
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 = {},
number = {},
pages = {110332},
doi = {10.1016/j.jneumeth.2024.110332},
pmid = {39615554},
issn = {1872-678X},
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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39612132},
issn = {1741-0444},
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 = {Humans ; *Depression/epidemiology/psychology ; Male ; Adult ; Cross-Sectional Studies ; China/epidemiology ; Female ; Middle Aged ; Mediation Analysis ; Stress, Psychological/epidemiology/psychology ; Prisons ; Family/psychology ; Occupational Stress/psychology/epidemiology ; Police/psychology/statistics &amp; numerical data ; Social Network Analysis ; Correctional Facilities Personnel ; East Asian People ; },
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 = {},
doi = {10.1101/2024.11.12.623096},
pmid = {39605556},
issn = {2692-8205},
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 = {},
doi = {10.1101/2024.11.20.624562},
pmid = {39605372},
issn = {2692-8205},
abstract = {UNLABELLED: 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 (Bb LP) 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. S ingle- c ell 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, du al s pecificity p hosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to Bb LP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to Bb LP, 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.<br><br>IMPORTANCE: Borrelia burgdorferi , the agent of Lyme disease, encodes numerous lipoproteins that play a crucial role as a pathogen associated molecular pattern affecting interactions with tick- and vertebrate-host cells. Single cell transcriptomics validated using unbiased proteomics and conventional molecular biology approaches have demonstrated significant differences in gene expression patterns in a dose- and time-dependent manner following treatment of murine bone marrow derived macrophages with borrelial lipoproteins. Distinct populations of macrophages, alterations in immune signaling pathways, cellular energy production and mitochondrial responses were identified and validated using primary murine macrophages and human reporter cell lines. Notably, the role of Dual Specificity Phosphatase 1 (DUSP1) in influencing several inflammatory, metabolic and mitochondrial responses of macrophages were observed in these studies using known pharmacological inhibitors. Significant outcomes include novel strategies to interfere with immunomodulatory and survival capabilities of B. burgdorferi in reservoir hosts affecting its natural infectious life cycle between ticks and vertebrate hosts.},
}
@article {pmid39603445,
year = {2024},
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 = {},
number = {},
pages = {138062},
doi = {10.1016/j.neulet.2024.138062},
pmid = {39603445},
issn = {1872-7972},
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 ; *Electroencephalography ; *Brain-Computer Interfaces ; Wearable Electronic Devices ; Artificial Intelligence ; Brain/physiology ; },
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 = {},
doi = {10.3390/s24227125},
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 = {},
doi = {10.3390/mi15111355},
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 = {},
doi = {10.3390/mi15111283},
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 = {},
doi = {10.3390/brainsci14111092},
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 = {R01EB035484/EB/NIBIB NIH HHS/United States ; R01MH130490/MH/NIMH NIH HHS/United States ; 1R01NS126337/NS/NINDS NIH HHS/United States ; 01GQ2201//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 01GQ2304A//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 2018 IZN 004//Free State of Thuringia/ ; 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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39592732},
issn = {1745-7254},
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 = {},
number = {},
pages = {},
doi = {10.1111/ejn.16621},
pmid = {39592434},
issn = {1460-9568},
support = {214535, UMR7077//Janssen Horizon/ ; },
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9778},
pmid = {39591752},
issn = {1741-2552},
abstract = {Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility in comparison to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification in ear-EEG faces a major challenge due to the significant attenuation and distorted amplitude of the signal. Our aim is to enhance the classification performance of SSVEP using ear-EEG and augment its practical application value. To address this challenge, we focuse on enhancing ear-EEG feature representations by training the model to learn feature representations similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed for optimizing SSVEP target classification recognition in ear-EEG data. This framework combines signals from the scalp area to obtains multi-layer distilled knowledge through the cooperation of distillation of features in the mid-layer feature distillation and output layer response distillation. We improved the classification of the shorter first 1s data and achieved a maximum classification result of 75.7%. We evaluate the proposed MESD framework through single-session, cross-session and cross-subject transfer decoding, comparing it with baseline method. The results demonstrate that the proposed framework achieves the best classification results in all experiments. 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 including auxiliary control and rehabilitation training in forthcoming 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 Erdogmus, D},
title = {Improving subject transfer in EEG classification with divergence estimation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9777},
pmid = {39591745},
issn = {1741-2552},
abstract = {\textit{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. \textit{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. \textit{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. \textit{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 = {},
doi = {10.3390/bios14110553},
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},
doi = {10.1073/pnas.2412423121},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1039/d4tb02090a},
pmid = {39588722},
issn = {2050-7518},
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 = {2024},
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 = {},
number = {},
pages = {e202418949},
doi = {10.1002/anie.202418949},
pmid = {39588687},
issn = {1521-3773},
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 = {2024},
author = {Thenmozhi, T and Helen, R and Mythili, S},
title = {Classification of Motor Imagery EEG with Ensemble RNCA model.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115345},
doi = {10.1016/j.bbr.2024.115345},
pmid = {39586499},
issn = {1872-7549},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.044},
pmid = {39586422},
issn = {1873-7544},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.054},
pmid = {39586421},
issn = {1873-7544},
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 = {2024},
author = {Higashi, H},
title = {Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110323},
doi = {10.1016/j.jneumeth.2024.110323},
pmid = {39586380},
issn = {1872-678X},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1002/alz.14369},
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/ ; },
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.xphs.2024.10.044},
pmid = {39581346},
issn = {1520-6017},
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. However, 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},
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 = {2024},
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},
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 = {2024},
author = {Oxley, T},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9633},
pmid = {39577098},
issn = {1741-2552},
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, an innovation that stands alongside well-established methods such as electroencephalography (EEG), traditional electrocorticography (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. This 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 {pmid39576281,
year = {2024},
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 = {},
number = {},
pages = {},
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)/ ; },
abstract = {This review follows two previous papers (Farina et al., 2004, 2014) in which we reflected on the use of surface 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 modelling; 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 non-measurable parameters by inverse modelling. 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 non-stationarities 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 years since our first review, we conclude that the recording and analysis of surface EMG signals has 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 = {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 {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},
doi = {10.1002/adhm.202402215},
pmid = {39011811},
issn = {2192-2659},
support = {FA9550-19-1-0278//Air Force Office of Scientific Research/ ; //National Institutes of Health's Brain Research/ ; UG3NS123723-01//BRAIN Initiative/ ; R01NS123655-01//BRAIN Initiative/ ; DP2-EB029757/EB/NIBIB NIH HHS/United States ; //UC President's Dissertation Year Fellowship/ ; //Natural Sciences and Engineering Research Council of Canada/ ; //Kuwait Foundation for the Advancement of Sciences/ ; CHE-1338173//National Science Foundation Major Research Instrumentation Program/ ; //UC Irvine Materials Research Institute/ ; //Alfred P. Sloan Foundation/ ; 898571//H2020 Marie Sk&#x142;odowska-Curie Actions/ ; CBET-2223566//National Science Foundation Disability and Rehabilitation Engineering/ ; 1845683//National Science Foundation/ ; ECCS-1542148//National Science Foundation/ ; DMR-2011967//National Science Foundation/ ; 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 {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 = {},
number = {},
pages = {120949},
doi = {10.1016/j.neuroimage.2024.120949},
pmid = {39571645},
issn = {1095-9572},
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 = {2024},
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},
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},
doi = {10.1371/journal.pone.0309706},
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},
doi = {10.1371/journal.pone.0313261},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a6},
pmid = {39569894},
issn = {1741-2552},
abstract = {Background 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 various implanted devices, few studies have identified patient preferences underlying device design, and moreover, each study has typically captured a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large patient cohort across multiple aetiologies. Methods We performed a systematic review of all published studies reporting patient preferences for BCI devices. We searched MEDLINE, Embase, and CINAHL from inception to April 18th, 2023. We included any study reporting either qualitative or quantitative preferences concerning BCI devices. Article screening and data extraction were performed by two reviewers in duplicate. Extracted information included demographic information, current digital device use, device invasiveness preference, device design preferences, and device functional preferences. Findings Our search identified 1316 articles, of which 28 studies were eligible for inclusion. Preference information was captured from 1701 patients (mean age = 42.1-64.3 years). Amyotrophic lateral sclerosis was the most represented clinical condition (n = 15 studies, 53.6%), followed by spinal cord injury (n = 13 studies, 46.4%). We found that individuals with motor impairment prioritise device accuracy over other device design characteristics. We also 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. When comparing populations across studies, we found that patient preferences vary according to both disease aetiology and the severity of motor impairment. Interpretation Our findings support a greater research emphasis on minimising BCI setup and training burden, and they suggest future BCI devices may require bespoke configuration and training for specific patient groups. .},
}
@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: An in silico proof of concept.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a5},
pmid = {39569892},
issn = {1741-2552},
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 (BCI) 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 40-100ms 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 controler's Linear, Time-Invariant (LTI) 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 silico and in vivo data 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, E 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a7},
pmid = {39569866},
issn = {1741-2552},
abstract = {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 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 = {AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; NS127849, NS096761, NS131069, NS124564//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; 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 {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 {pmid39567330,
year = {2024},
author = {Pu, Y and Francks, C and Kong, XZ},
title = {Global brain asymmetry.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2024.10.008},
pmid = {39567330},
issn = {1879-307X},
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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39565521},
issn = {1995-8218},
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 = {},
number = {},
pages = {},
pmid = {39565505},
issn = {2523-899X},
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 = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.4c02604},
pmid = {39561980},
issn = {1530-6992},
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 = {2024},
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 = {},
number = {},
pages = {2450069},
doi = {10.1142/S0129065724500692},
pmid = {39560446},
issn = {1793-6462},
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 = {2024},
author = {Shah, AM},
title = {Hopeful progress in artificial vision.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.14912},
pmid = {39560167},
issn = {1525-1594},
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 {pmid39557568,
year = {2024},
author = {Harris Caceres, A and Barany, DA and Dundon, NM and Smith, J and Marneweck, M},
title = {Neural encoding of direction and distance across reference frames in visually guided reaching.},
journal = {eNeuro},
volume = {},
number = {},
pages = {},
doi = {10.1523/ENEURO.0405-24.2024},
pmid = {39557568},
issn = {2373-2822},
abstract = {Goal-directed actions require transforming sensory information into motor plans defined across multiple parameters and reference frames. Substantial evidence supports the encoding of target direction in gaze- and body-centered coordinates within parietal and premotor regions. However, how the brain encodes the equally critical parameter of target distance remains less understood. Here, using Bayesian pattern component modeling of fMRI data during a delayed reach-to-target task, we dissociated the neural encoding of both target direction and the relative distances between target, gaze, and hand at early and late stages of motor planning. This approach revealed independent representations of direction and distance along the human dorsomedial reach pathway. During early planning, most premotor and superior parietal areas encoded a target's distance in single or multiple reference frames and encoded its direction. In contrast, distance encoding was magnified in gaze- and body-centric reference frames during late planning. These results emphasize a flexible and efficient human central nervous system that achieves goals by remapping sensory information related to multiple parameters, such as distance and direction, in the same brain areas.Significance statement Motor plans specify various parameters, e.g., target direction and distance, each of which can be defined in multiple reference frames relative to gaze, limb, or head. Combining fMRI, a delayed reach-to-target task, and Bayesian pattern component modeling, we present evidence for independent goal-relevant representations of direction and distance in multiple reference frames across early and late planning along the dorsomedial reach pathway. Initially, areas encoding distance also encode direction, but later in planning, distance encoding in multiple reference frames was magnified. These results emphasize central nervous system flexibility in transforming movement parameters in multiple reference frames crucial for successful goal-directed actions and have important implications for brain-computer interface technology advances with sensory integration.},
}
@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 {pmid39556950,
year = {2024},
author = {Kılınç Bülbül, D and Walston, ST and Duvan, FT and Garrido, JA and Guclu, B},
title = {Decoding sensorimotor information from somatosensory cortex by flexible epicortical μECoG arrays in unrestrained behaving rats.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9405},
pmid = {39556950},
issn = {1741-2552},
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: 25 μm).<br><br>APPROACH: Motor and somatosensory information was decoded offline from microelectrocorticography (&#x3bc;ECoG) 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. &#x3bc;ECoG 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.<br><br>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.<br><br>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 using &#x3bc;ECoG 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 {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 = {},
doi = {10.1083/jcb.202408061},
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 {pmid39551270,
year = {2024},
author = {Xiang, L and Zhao, Y and Li, X and Shi, R and Wen, Z and Xu, X and Hu, Y and Xu, Q and Chen, Y and Ma, J and Shen, W},
title = {Astrocytic calcium signals modulate exercise-induced fatigue in mice.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.033},
pmid = {39551270},
issn = {1873-7544},
abstract = {Exercise-induced fatigue (EF) is characterized by a decline in maximal voluntary muscle force following prolonged physical activity, influenced by both peripheral and central factors. Central fatigue involves complex interactions within the central nervous system (CNS), where astrocytes play a crucial role. This study explores the impact of astrocytic calcium signals on EF. We used adeno-associated viruses to express GCaMP7b in astrocytes of the dorsal striatum in mice, allowing us to monitor calcium dynamics. Our findings reveal that EF significantly increases the frequency of spontaneous astrocytic calcium signals. Utilizing genetic tools to either enhance or reduce astrocytic calcium signaling, we observed corresponding decreases and increases in exercise-induced fatigue time, respectively. Furthermore, modulation of astrocytic calcium signals influenced corticostriatal synaptic plasticity, with increased signals impairing and decreased signals ameliorating long-term depression (LTD). These results highlight the pivotal role of astrocytic calcium signaling in the regulation of exercise-induced fatigue and synaptic plasticity in the striatum.},
}
@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 = {},
number = {},
pages = {120937},
doi = {10.1016/j.neuroimage.2024.120937},
pmid = {39550056},
issn = {1095-9572},
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 = {2024},
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},
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 = {2024},
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},
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 = {2024},
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},
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},
doi = {10.1002/edm2.70015},
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 = {2024},
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},
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 = {2024},
author = {Noneman, KK and Patrick Mayo, J},
title = {Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2450070},
doi = {10.1142/S0129065724500709},
pmid = {39545725},
issn = {1793-6462},
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},
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},
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 {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 = {e162-83},
pmid = {38888742},
issn = {1872-6623},
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 {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},
doi = {10.3389/fnhum.2024.1486167},
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},
doi = {10.3389/fnhum.2024.1423382},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2024.10.035},
pmid = {39537459},
issn = {2095-9281},
}
@article {pmid39536406,
year = {2024},
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},
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},
doi = {10.1371/journal.pone.0313007},
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 {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 = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3496727},
pmid = {39531567},
issn = {1558-0210},
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 Silva, M and Lima, RHH and Dantas, AFOA and Cardoso Rodrigues, A 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 = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad9155},
pmid = {39530641},
issn = {2057-1976},
abstract = {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, the Z-score-based PLV normalization using both modified k-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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1111/acps.13770},
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 {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 {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 {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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3496757},
pmid = {39527418},
issn = {2168-2208},
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},
doi = {10.1111/cns.70115},
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 = {2024},
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 = {},
number = {},
pages = {e2403119},
doi = {10.1002/adhm.202403119},
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/ ; },
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 = {},
doi = {10.3390/s24217084},
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 = {},
doi = {10.3390/s24217080},
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 = {},
doi = {10.3390/s24217016},
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 = {},
doi = {10.3390/s24216965},
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 = {},
doi = {10.3390/s24216847},
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 = {},
number = {},
pages = {e2401140},
doi = {10.1002/advs.202401140},
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/ ; },
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad905d},
pmid = {39514976},
issn = {1741-2552},
abstract = {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-LSTM, designed to address these issues. Specifically, it integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Gated Recurrent Units (GRU) and Long Short-Term Memory (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 = {2024},
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 = {},
number = {},
pages = {},
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)/ ; },
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 = {2024},
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},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3491096},
pmid = {39509308},
issn = {2168-2208},
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 = {},
doi = {10.7554/eLife.83424},
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 {pmid37471544,
year = {2023},
author = {Timko, BP},
title = {Neural implants without brain surgery.},
journal = {Science (New York, N.Y.)},
volume = {381},
number = {6655},
pages = {268-269},
pmid = {37471544},
issn = {1095-9203},
support = {23TPA1057212/AHA/American Heart Association-American Stroke Association/United States ; },
mesh = {Animals ; Humans ; *Brain/blood supply/physiology ; *Neurons/physiology ; *Brain-Computer Interfaces ; *Biosensing Techniques ; Single-Cell Analysis ; *Blood Vessels/physiology ; },
abstract = {Injectable bioprobes record single-neuron activity from within blood vessels.},
}
@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 = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3492191},
pmid = {39504276},
issn = {1558-0210},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3492977},
pmid = {39504275},
issn = {1558-2531},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3492506},
pmid = {39504274},
issn = {1558-2531},
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},
doi = {10.20463/pan.2024.0019},
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, G 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8efc},
pmid = {39500053},
issn = {1741-2552},
abstract = {OBJECTIVE: We aim to assess the severity of spatial neglect 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 (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients.<br><br>APPROACH: Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. 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 spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.<br><br>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.<br><br>SIGNIFICANCE: These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.},
}
@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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8ef7},
pmid = {39500051},
issn = {1741-2552},
abstract = {Reactive Brain-Computer Interfaces (rBCIs) 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-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction. Methods: 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: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes. Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in 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 seconds. Conclusion: 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, MC},
title = {Estimating cognitive workload using a commercial in-ear EEG headset.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8ef8},
pmid = {39500044},
issn = {1741-2552},
abstract = {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 of gamma band 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 frequency gamma band features can improve workload estimation. Significance: The application of EEG-based Brain-Computer Interfaces (BCIs) 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8e86},
pmid = {39496200},
issn = {1741-2552},
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 artefacts 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 resultsRigorous 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 = {},
number = {},
pages = {e2407706},
doi = {10.1002/advs.202407706},
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/ ; },
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 = {},
number = {},
pages = {120906},
doi = {10.1016/j.neuroimage.2024.120906},
pmid = {39490945},
issn = {1095-9572},
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 an 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 {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 = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.10.046},
pmid = {39490518},
issn = {1873-7544},
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 {pmid39488002,
year = {2024},
author = {Xu, Z and Khazaee, M and Truong, ND 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8dfe},
pmid = {39488002},
issn = {1741-2552},
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.<br><br>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.<br><br>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(6mm), skull(10mm), and subdural space(5mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.<br><br>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 {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 {pmid39486261,
year = {2024},
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},
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},
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 {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 {pmid39481863,
year = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1136/medhum-2024-013022},
pmid = {39481863},
issn = {1473-4265},
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 {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 {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 = {},
doi = {10.1101/2024.10.11.24315027},
pmid = {39484239},
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 {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 {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 {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 = {},
number = {},
pages = {},
doi = {10.1039/d4tb01628a},
pmid = {39479901},
issn = {2050-7518},
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 = {2024},
author = {Craik, A and Dial, HR and Contreras-Vidal, JL},
title = {Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8d0a},
pmid = {39476487},
issn = {1741-2552},
abstract = {Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking 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. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences displayed on a screen 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, achieving statistically significant participant-independent decoding performance 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, and 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.},
}
@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 = {},
number = {},
pages = {e2407525},
doi = {10.1002/advs.202407525},
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/ ; },
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 {pmid39466862,
year = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3487133},
pmid = {39466862},
issn = {1558-2531},
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},
doi = {10.1371/journal.pbio.3002899},
pmid = {39466848},
issn = {1545-7885},
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 ; *Disabled Persons/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 {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 = {},
doi = {10.3390/s24206645},
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 = {},
doi = {10.3390/s24206585},
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 = {},
doi = {10.3390/biomedicines12102415},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8b6e},
pmid = {39454612},
issn = {1741-2552},
abstract = {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 = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3486551},
pmid = {39453797},
issn = {1558-0210},
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 = {},
doi = {10.3390/biomimetics9100594},
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 = {},
doi = {10.3390/bioengineering11100990},
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 = {},
doi = {10.3390/bioengineering11100967},
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 {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 {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 = {},
number = {},
pages = {},
pmid = {39446156},
issn = {1432-1459},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; },
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 = {NS106014/NS/NINDS NIH HHS/United States ; LM011826//U.S. National Library of Medicine/ ; UL1TR001873 from NCATS/NIH//Clinical and Translational Science Awards/ ; },
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},
doi = {10.62438/tunismed.v102i10.5068},
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 = {},
number = {},
pages = {e0070224},
doi = {10.1128/msphere.00702-24},
pmid = {39440972},
issn = {2379-5042},
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 vs. deep learning.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad89c5},
pmid = {39437806},
issn = {2057-1976},
abstract = {Backgrounds Virtual reality (VR) simulates real-life events and scenarios, widely used in education, entertainment, and medicine. VR can be presented in two or three dimensions (2D or 3D), and 3D VR produces a more realistic and immersive experience. Previous research has revealed that the electroencephalogram (EEG) induced by 3D VR has a different profile from that of 2D VR, manifesting in many aspects, such as the power of brain rhythm, brain activation, and brain functional connectivity. However, studies on how to classify EEG in 2D and 3D VR were limited. Methods 64-channel EEG was recorded, while visual stimuli were given in 2D and 3D VR. The classification of these recorded EEG signals was done using two machine learning methods: the traditional method and the deep learning method. In the traditional machine learning classification, EEG features of power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classification algorithms, support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF), were used. A specialized convolutional neural network, EEGNet, was used in the deep learning classification. These classification approaches were compared with respect to their classification performance. Results In aspects of four performance evaluations for classification, accuracy, precision, recall, and F1-score, respectively, classification using the deep learning method is better than the traditional machine learning approaches. Classification accuracy with deep learning with EEGNet could reach up to 97.86%. Conclusions The classification performance of 2D and 3D VR-induced EEG can be achieved with EEGNet-based deep learning, outperforming conventional machine learning approaches. Given the role of EEGNet, which is designed for EEG-based brain-computer interfaces (BCI), better performance classification of EEG in 2D and 3D VR environments might be predicted to be helpful for the application of 3D VR in BCI. .},
}
@article {pmid39435615,
year = {2024},
author = {Song, X and Li, R and Chu, X and Li, Q and Li, R and Li, Q and Tong, K 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 = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00641},
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 = {N2864C, A2295R, A2827R, A3803R//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; UH2NS095548, U01NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8963},
pmid = {39433073},
issn = {1741-2552},
abstract = {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 (SNR) 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 (CCA) 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8962},
pmid = {39433072},
issn = {1741-2552},
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. 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. 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. 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. 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8964},
pmid = {39433071},
issn = {1741-2552},
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.<br><br>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.<br><br>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 (AUC) score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.<br><br>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 = {2024},
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},
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},
doi = {10.1073/pnas.2407904121},
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},
}
@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},
doi = {10.1098/rstb.2023.0098},
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},
doi = {10.1098/rstb.2023.0081},
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},
doi = {10.1098/rstb.2023.0093},
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 = {2024},
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 = {},
number = {},
pages = {115295},
doi = {10.1016/j.bbr.2024.115295},
pmid = {39428037},
issn = {1872-7549},
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% for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92% for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02% for pairwise classification and 55.58% 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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.xphs.2024.10.016},
pmid = {39426564},
issn = {1520-6017},
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 {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 = {},
number = {},
pages = {1-16},
doi = {10.1080/15368378.2024.2415089},
pmid = {39425602},
issn = {1536-8386},
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 = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1215-24.2024},
pmid = {39424369},
issn = {1529-2401},
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 bares 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 cat 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 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.Significance Statement Sensory features, such as the position or orientation of a visual stimulus, are mapped onto the surface of cortex as functional domains. Their selective activation, that may enable eliciting complex percepts, is intensively pursued for basic science and clinical applications. However, delivery of light into one functional domain in optogenetically transfected cortex results in complex, widespread neuronal activity, spreading beyond the targeted domain. Our computational study reveals that neuron morphology contributes to this diffuse response in a cortical-layer and intensity-dependent manner. We show that enhancing the stimulator optics is more effective than soma-targeting of the opsin in increasing spatial precision of stimulation. Our simulations provide insights for designing optogenetic stimulation protocols and hardware to achieve selective activation of functional domains.},
}
@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 Au Yong, N and Slutzky, MW and Pandarinath, C},
title = {Reducing power requirements for high-accuracy decoding in iBCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a4},
pmid = {39423832},
issn = {1741-2552},
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.<br><br>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.<br><br>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.<br><br>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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a2},
pmid = {39423831},
issn = {1741-2552},
abstract = {\textbf{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 BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) 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. \textbf{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 \method{}
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 (MOABB) framework. \textbf{Main results:}
The results of our \method{}
demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. \textbf{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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a3},
pmid = {39423829},
issn = {1741-2552},
abstract = {Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks 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 Deep Neural Networks (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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a5},
pmid = {39423826},
issn = {1741-2552},
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.<br><br>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.<br><br>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.<br><br>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 = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3483294},
pmid = {39423083},
issn = {1558-0210},
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},
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},
}
@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},
doi = {10.1631/jzus.B2400103},
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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39419932},
issn = {2509-2723},
support = {NIH NIA R01 AG062655/AG/NIA NIH HHS/United States ; SAGA23-1142437/ALZ/Alzheimer's Association/United States ; },
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8838},
pmid = {39419108},
issn = {1741-2552},
abstract = {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. 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 affected hand. 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, FJ and González Zamorano, Y and Romero, JP 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8836},
pmid = {39419104},
issn = {1741-2552},
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.<br><br>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.<br><br>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.<br><br>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 Drahos, LM and Nagrale, SS and Yadav, S and Widge, AS and Shoaran, M},
title = {Neural decoding and feature selection methods for closed-loop control of avoidance behavior.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8839},
pmid = {39419091},
issn = {1741-2552},
abstract = {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 = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.09.018},
pmid = {39419024},
issn = {1097-4199},
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 = {},
doi = {10.1101/2024.10.09.615886},
pmid = {39416032},
issn = {2692-8205},
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},
doi = {10.1016/j.xcrm.2024.101662},
pmid = {39413730},
issn = {2666-3791},
mesh = {Humans ; Animals ; *Chronic Pain/therapy/physiopathology ; Pain Management/methods ; Brain-Computer Interfaces ; },
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},
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 = {2024},
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 = {},
number = {},
pages = {1-14},
doi = {10.1080/21646821.2024.2408501},
pmid = {39413360},
issn = {2164-6821},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3481886},
pmid = {39412979},
issn = {1558-0210},
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 {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 = {},
doi = {10.3390/s24196466},
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 = {},
doi = {10.3390/s24196378},
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 = {},
doi = {10.3390/s24196366},
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 = {},
doi = {10.3390/s24196304},
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 = {},
doi = {10.3390/s24196161},
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 = {2024},
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},
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 = {2024},
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 7T MRI study.},
journal = {International journal of stroke : official journal of the International Stroke Society},
volume = {},
number = {},
pages = {17474930241293966},
doi = {10.1177/17474930241293966},
pmid = {39402900},
issn = {1747-4949},
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 &#xb1; 9.9 years) were prospectively enrolled and underwent 7T 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; = -0.573, p-corrected = 0.004]. Parietal DMV score was associated with WMH volume [&#x3b2; = 0.463, p-corrected = 0.013] and PVS volume in basal ganglia (&#x3b2; = 0.415, p-corrected = 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 = 0.115, Pm=43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect = 0.161, Pm=42.8%).<br><br>CONCLUSIONS: 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 {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},
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 {pmid39401512,
year = {2024},
author = {Eisma, YB and Van Vliet, ST and Nederveen, A and de Winter, J},
title = {Assessing the Influence of Visual Stimulus Properties on Steady-State Visually Evoked Potentials and Pupil Diameter.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad865d},
pmid = {39401512},
issn = {2057-1976},
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 vs. 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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1002/nau.25599},
pmid = {39400422},
issn = {1520-6777},
support = {//The authors received no specific funding for this work./ ; },
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 = {2024},
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 = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2024.2410221},
pmid = {39397592},
issn = {1476-8259},
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 = {},
number = {},
pages = {},
doi = {10.1097/RLU.0000000000005496},
pmid = {39397321},
issn = {1536-0229},
abstract = {Here is a case of chyle leak post McKeown esophagectomy. Lymphoscintigraphy with 99mTc-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 = {},
number = {},
pages = {107458},
doi = {10.1016/j.phrs.2024.107458},
pmid = {39396768},
issn = {1096-1186},
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 = {2024},
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 = {},
number = {},
pages = {149261},
doi = {10.1016/j.brainres.2024.149261},
pmid = {39396567},
issn = {1872-6240},
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 {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 = {},
number = {},
pages = {e2406390},
doi = {10.1002/advs.202406390},
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/ ; },
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 {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 = {2024},
author = {Fan, J and Wang, X and Xu, H},
title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39395910},
issn = {1995-8218},
}
@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},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.09.012},
pmid = {39389052},
issn = {1097-4199},
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},
doi = {10.1111/cns.70077},
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 {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},
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 {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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad851c},
pmid = {39383883},
issn = {1741-2552},
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.<br><br>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.<br><br>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.<br><br>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},
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/ ; },
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 = {},
number = {},
pages = {},
doi = {10.1093/toxsci/kfae129},
pmid = {39378126},
issn = {1096-0929},
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 United States Food and Drug Administration (FDA) 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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3464550},
pmid = {39374272},
issn = {2168-2208},
abstract = {Motor imagery, as a paradigm of brainmachine interfaces, 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-machine 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 = {},
number = {},
pages = {e4443},
doi = {10.1002/ecy.4443},
pmid = {39373084},
issn = {1939-9170},
support = {//China Scholarship Council/ ; },
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 = {},
doi = {10.1101/2024.09.18.24313755},
pmid = {39371161},
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. 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. 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. 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. Clinical Trial Information . ClinicalTrials.gov, registration number NCT03567213 .},
}
@article {pmid39369514,
year = {2024},
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},
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 = {},
number = {},
pages = {111091},
doi = {10.1016/j.brainresbull.2024.111091},
pmid = {39368632},
issn = {1873-2747},
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 {pmid39366386,
year = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2024.09.002},
pmid = {39366386},
issn = {1932-7420},
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},
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 = {2024},
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 &amp; Cross-Dataset Motor Imagery BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3474049},
pmid = {39365711},
issn = {1558-2531},
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 = {},
number = {},
pages = {},
doi = {10.1021/acssensors.4c01568},
pmid = {39353205},
issn = {2379-3694},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3472097},
pmid = {39352826},
issn = {2168-2208},
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},
doi = {10.1073/pnas.2319709121},
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 = {Humans ; *Amyotrophic Lateral Sclerosis/genetics ; *Superoxide Dismutase-1/genetics ; Male ; Female ; Middle Aged ; *Founder Effect ; *Asian People/genetics ; Adult ; *Haplotypes ; Aged ; China/epidemiology ; Exome Sequencing ; Genetic Association Studies ; Mutation ; Age of Onset ; Phenotype ; East Asian People ; },
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 = {Humans ; *Mucolipidoses/genetics ; Male ; Female ; *Founder Effect ; China/epidemiology ; Haplotypes/genetics ; Child, Preschool ; Polymorphism, Single Nucleotide/genetics ; Neuraminidase/genetics ; Child ; Mutation/genetics ; Genotype ; Infant ; Genetic Association Studies ; Asian People/genetics ; Adolescent ; East Asian People ; },
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 = {},
number = {},
pages = {},
pmid = {39347924},
issn = {2523-899X},
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},
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. https://snel-repo.github.io/falcon/.},
}
@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 = {},
doi = {10.1101/2024.09.13.612676},
pmid = {39345497},
issn = {2692-8205},
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},
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 = {2024},
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 = {},
number = {},
pages = {15459683241287731},
doi = {10.1177/15459683241287731},
pmid = {39345118},
issn = {1552-6844},
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},
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 = {},
number = {},
pages = {},
doi = {10.22365/jpsych.2024.017},
pmid = {39342624},
issn = {1105-2333},
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},
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},
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},
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},
doi = {10.1073/pnas.2403380121},
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 = {},
number = {},
pages = {15459683241282783},
doi = {10.1177/15459683241282783},
pmid = {39328074},
issn = {1552-6844},
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},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.arthro.2024.09.028},
pmid = {39326569},
issn = {1526-3231},
abstract = {PURPOSE: The purpose of this study was 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 (PRISMA) guidelines. Clinical studies reporting following BCI for rotator cuff tears were included. Quantitive and qualitative data was 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 ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 75%-100% of patients meeting the MCID. 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%-100% of patients meeting the MCID. For studies that quantified percent increases in tendon thickness, the reported increases ranged from 13% in 44% full thickness tears, and 14% to 60% in partial thickness tears. There were 6 studies that evaluated rotator cuff re-tears after BCI treatment in the full thickness cohort, with rates reported ranging from 0-9%. There were 5 studies that evaluated rotator cuff re-tears after BCI treatment in the partial thickness cohort, with rates reported ranging from 0-18%. Two of the included studies found that BCI was cost-effective due to the increased tendon healing with cost savings of $5,338-$13,061 per healed rotator cuff tendon.<br><br>CONCLUSION: 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 two 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8031},
pmid = {39326451},
issn = {1741-2552},
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 onchip 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. is 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 {pmid39326449,
year = {2024},
author = {Harel, A and Shriki, O},
title = {Task-guided attention increases non-linearity of steady-state visually evoked potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8032},
pmid = {39326449},
issn = {1741-2552},
abstract = {Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. In this study, we investigated the involvement of nonlinear processes in top-down visual attention by employing a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background - a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. We examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing. In line with prior research, our findings indicate that the power spectrum density of EEG responses is influenced by both task presence and stimulus contrast. By utilizing the Rhythmic Entrainment Source Separation (RESS) technique, we discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses. Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention while also providing insights into optimizing brain-computer interfaces. .},
}
@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 Rehman, FU 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7f8e},
pmid = {39321840},
issn = {1741-2552},
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 (BCI). 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},
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},
doi = {10.1016/j.clinph.2024.08.009},
pmid = {39321571},
issn = {1872-8952},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3468351},
pmid = {39320995},
issn = {1558-2531},
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 singlechannel 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 realtime neurofeedback. The neurofeedback is represented by output value of attention, which calculated from singlechannel 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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3467090},
pmid = {39316474},
issn = {2168-2208},
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 13K, 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 crossdomain 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 = {},
number = {},
pages = {},
pmid = {39316274},
issn = {1559-0089},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00539},
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 = {2024},
author = {Lv, Y and Li, H},
title = {Blood diagnostic and prognostic biomarkers in amyotrophic lateral sclerosis.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00286},
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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39313602},
issn = {1741-0444},
support = {2024A1515012810//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
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},
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},
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 = {},
number = {},
pages = {},
doi = {10.1002/cac2.12603},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.bbi.2024.09.014},
pmid = {39303815},
issn = {1090-2139},
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 = {},
number = {},
pages = {},
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},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3463737},
pmid = {39292591},
issn = {2168-2208},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3462991},
pmid = {39292590},
issn = {2168-2208},
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 = {},
number = {},
pages = {},
pmid = {39289533},
issn = {2397-3374},
support = {RF1MH121373//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; },
}
@article {pmid39288794,
year = {2024},
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, E and Oxley, T and Fry, A and Weber, DJ 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7bec},
pmid = {39288794},
issn = {1741-2552},
abstract = {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. 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 clinical outcome assessments (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. 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. 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 = {},
number = {},
pages = {1-9},
doi = {10.1080/14737159.2024.2405919},
pmid = {39285529},
issn = {1744-8352},
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 = {},
number = {},
pages = {},
doi = {10.1242/bio.060595},
pmid = {39284710},
issn = {2046-6390},
support = {PICT2020-03395//Fondo para la Investigacion Cientifica y Tecnologica/ ; },
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) 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 seven 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},
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},
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},
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 = {},
number = {},
pages = {},
doi = {10.1002/nau.25553},
pmid = {39268765},
issn = {1520-6777},
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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7a24},
pmid = {39265614},
issn = {1741-2552},
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.<br><br>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.<br><br>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.<br><br>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 = {2024},
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},
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},
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},
doi = {10.1109/TNSRE.2024.3459801},
pmid = {39264785},
issn = {1558-0210},
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 = {2024},
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 = {},
number = {},
pages = {},
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 = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0159-24.2024},
pmid = {39261006},
issn = {1529-2401},
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.Significance Statement Over the decades, studies have proposed three sound source decoders: the space map decoder (topographically tuned to sound location), the opponent channel decoder (compares the averaged tuning between two groups of neurons), and the population pattern decoder (decodes locations by utilizing the diverse tunings across the population). This is the first study that 1) visualizes the local organization of spatial tuning and identifies clusters in a brain area that features an auditory spatial map, 2) tests the three decoders in a single brain area of the same species and discovers that distinct neuron types favor different decoders, and 3) reveals the differential spatial coding between excitatory and inhibitory neurons and elucidates this disparity through a computational model.},
}
@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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3458060},
pmid = {39259621},
issn = {1558-2531},
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. The source code for our approach is available at https://github.com/didi226/scut_ssvep_aperiod.},
}
@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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3458389},
pmid = {39255081},
issn = {1558-2531},
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 = {},
number = {},
pages = {},
pmid = {39253749},
issn = {1473-6551},
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 for microtia patients with aural atresia: acoustic and subjective benefits.},
journal = {Journal of the Chinese Medical Association : JCMA},
volume = {},
number = {},
pages = {},
doi = {10.1097/JCMA.0000000000001162},
pmid = {39252162},
issn = {1728-7731},
abstract = {BACKGROUND: This study evaluated the long-term acoustic and subjective outcomes of Bonebridge bone conduction implant (BCI) 601 implantation in Taiwanese microtia patients with aural atresia (AA).<br><br>METHODS: A total of 41 microtia patients (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, including the 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 follow-up duration was 6.3 years (range, 2.8-9.1). The mean unaided air conduction pure tone average (PTA4) was 65.3 &#xb1; 8.8 decibels (dB) hearing level (HL) and the mean aided sound field PTA4 was 31.1 &#xb1; 9.1 dB HL, resulting in a FHG of 34.2 &#xb1; 11.7 dB HL (p < 0.05). After Bonebridge implantation, the mean SRT in quiet, SRT in noise, WRS in quiet, and WRS in noise improved from 58.3 &#xb1; 7.4 dB HL to 29.4 &#xb1; 7.0 dB HL, from -1.4 &#xb1; 7.3 dB signal-to-noise ratio (SNR) to -9.6 &#xb1; 5.4 dB SNR, from 46.4 &#xb1; 26.9% to 93.8 &#xb1; 3.1%, and from 46.7 &#xb1; 21.8% to 72.7 &#xb1; 19.3%, respectively (p < 0.05). 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 to microtia patients with 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 = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad788e},
pmid = {39250958},
issn = {1741-2552},
abstract = {\textit{Objective.}
In this paper, we conduct a detailed investigation on the effect of IC-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets. \textit{Approach.}
We apply a pipeline matrix of two popular different Independent Component (IC) decomposition methods (Infomax, AMICA) with three different component rejection strategies (none, ICLabel, and 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 (CNN) and one long short term memory (LSTM) based model. We compare decoding performances on within-participant and within-dataset levels. \textit{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 ICA computations. \textit{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 Independent Component (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 tradeoff 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 = {PP},
number = {},
pages = {},
doi = {10.1109/TVCG.2024.3456147},
pmid = {39250394},
issn = {1941-0506},
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 = {},
number = {},
pages = {},
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 = {},
number = {},
pages = {e2401379},
doi = {10.1002/advs.202401379},
pmid = {39248654},
issn = {2198-3844},
support = {//National Institute of Health's National Institute on Deafness and Other Communication Disorders/ ; //The Marie-Josee and Henry R. Kravis Foundation/ ; },
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 = {},
number = {},
pages = {},
doi = {10.1016/j.wneu.2024.08.163},
pmid = {39245135},
issn = {1878-8769},
abstract = {BACKGROUND: Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life (QOL). 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 application for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection (WoSCC) 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 WoSCC 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>CONCLUSION: 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 QOL 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},
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},
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 = {2024},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3449083},
pmid = {39236139},
issn = {2168-2208},
abstract = {Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electro encephalogram (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 spatiotemporal 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 spatiotemporal 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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1002/ksa.12429},
pmid = {39234682},
issn = {1433-7347},
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},
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 = {},
number = {},
pages = {e2400340},
doi = {10.1002/advs.202400340},
pmid = {39229920},
issn = {2198-3844},
support = {82330114//National Natural Science Foundation of China/ ; 82273949//National Natural Science Foundation of China/ ; },
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},
support = {T32 MH115895/MH/NIMH NIH HHS/United States ; },
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},
doi = {10.1098/rsif.2024.0148},
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},
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},
doi = {10.1016/j.nicl.2024.103663},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3454158},
pmid = {39226201},
issn = {2168-2208},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1093/procel/pwae048},
pmid = {39225378},
issn = {1674-8018},
}
@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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3452701},
pmid = {39222461},
issn = {2168-2208},
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 = {2024},
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 = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2024.08.022},
pmid = {39218135},
issn = {1873-2402},
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 underlying regional SC-FC coupling patterns are not well understood.<br><br>METHODS: We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) 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 default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases.<br><br>CONCLUSIONS: This work enhances our understanding of MDD and pave 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 = {},
number = {},
pages = {},
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/ ; },
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},
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 = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3452410},
pmid = {39213268},
issn = {2168-2208},
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 ; *Social Media/statistics &amp; numerical data ; Pandemics/prevention &amp; control ; Internet ; },
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},
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},
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 = {2024},
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 = {},
number = {},
pages = {},
pmid = {39207622},
issn = {1995-8218},
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 ; Memory, Short-Term/physiology ; },
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 = {},
doi = {10.3390/brainsci14080836},
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 = {},
number = {},
pages = {},
pmid = {39196465},
issn = {1572-8633},
support = {2023EZX004//Shanghai Office of Philosophy and Social Science/ ; 23FZXA012//National Social Science Fund of China/ ; },
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 com